diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py index 9563b412e..8fb377a91 100644 --- a/funasr/models/llm_asr/model.py +++ b/funasr/models/llm_asr/model.py @@ -361,1133 +361,634 @@ class LLMASR(nn.Module): return results, meta_data -@tables.register("model_classes", "LLMASR2") -class LLMASR2(nn.Module): - """ """ - - def __init__( - self, - specaug: str = None, - specaug_conf: dict = None, - normalize: str = None, - normalize_conf: dict = None, - audio_encoder: str = None, - audio_encoder_conf: dict = None, - audio_adaptor: str = None, - audio_adaptor_conf: dict = None, - decoder: str = None, - decoder_conf: dict = None, - ctc: str = None, - ctc_conf: dict = None, - ctc_weight: float = 0.5, - llm: str = None, - llm_conf: dict = None, - input_size: int = 80, - vocab_size: int = -1, - ignore_id: int = -1, - blank_id: int = 0, - sos: int = 1, - eos: int = 2, - lsm_weight: float = 0.0, - length_normalized_loss: bool = False, - report_cer: bool = True, - report_wer: bool = True, - sym_space: str = "", - sym_blank: str = "", - # extract_feats_in_collect_stats: bool = True, - share_embedding: bool = False, - # preencoder: Optional[AbsPreEncoder] = None, - # postencoder: Optional[AbsPostEncoder] = None, - **kwargs, - ): - - super().__init__() - - # audio encoder - hub = audio_encoder_conf.get("hub", None) - if hub == "ms": - from funasr import AutoModel - - model = AutoModel(model=audio_encoder, model_revision="master") - # frontend = model.kwargs.get("frontend") - audio_encoder_output_size = model.model.encoder_output_size - - audio_encoder = ( - model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder - ) - - # self.frontend = frontend - - elif hub == "hf": - pass - else: - encoder_class = tables.encoder_classes.get(audio_encoder) - audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf) - audio_encoder_output_size = audio_encoder.output_size() - freeze = audio_encoder_conf.get("freeze", True) - freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1)) - # if freeze_layer_num > 0: - # freeze_layer_num = range(freeze_layer_num) - - if freeze: - for name, param in audio_encoder.named_parameters(): - if freeze_layer_num > 0: - idx = re.search(r"\.\d+\.", name) - if idx is not None: - beg, end = idx.regs[0] - layer_id = int(name[beg + 1 : end - 1]) - if layer_id < freeze_layer_num: - param.requires_grad = False - elif "ln_post." not in name: - param.requires_grad = False - else: - param.requires_grad = False - - audio_encoder.eval() - - self.audio_encoder = audio_encoder - - # llm - self.llm = None - - from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig - - init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5") - - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map=None, - use_cache=None, - ) - freeze = llm_conf.get("freeze", True) - if freeze: - for name, param in model.named_parameters(): - param.requires_grad = False - model.eval() - self.llm_dtype = llm_conf.get("llm_dtype", "fp32") - self.llm = model.to(dtype_map[self.llm_dtype]) - llm_dim = model.get_input_embeddings().weight.shape[-1] - - # adaptor - adaptor_class = tables.adaptor_classes.get(audio_adaptor) - audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size - audio_adaptor_conf["llm_dim"] = llm_dim - audio_adaptor = adaptor_class(**audio_adaptor_conf) - init_param_path = audio_adaptor_conf.get("init_param_path", None) - if init_param_path is not None: - src_state = torch.load(init_param_path, map_location="cpu") - flag = audio_adaptor.load_state_dict(src_state, strict=False) - logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}") - - self.audio_adaptor = audio_adaptor - - self.error_calculator = None - - self.length_normalized_loss = length_normalized_loss - self.beam_search = None - - def forward( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - input_ids: torch.Tensor, - attention_mask: torch.Tensor, - labels_ids: torch.Tensor, - fbank_beg: torch.Tensor, - fbank_mask: torch.Tensor, - **kwargs, - ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: - """Encoder + Decoder + Calc loss - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - # import pdb; - # pdb.set_trace() - if len(speech_lengths.size()) > 1: - speech_lengths = speech_lengths[:, 0] - - batch_size, frames, _ = speech.shape - - with torch.cuda.amp.autocast(enabled=False): - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - batch_size, token_num, dims = inputs_embeds.shape - fbank_mask[fbank_mask < 0] = 0 - fbank_fake_lens = fbank_mask.sum(-1).to(torch.int32) - # _, l, _ = encoder_out.shape - for batch_idx in range(batch_size): - - fbank_fake_len = fbank_fake_lens[batch_idx].item() - fbank_beg_idx = fbank_beg[batch_idx, 0].item() - min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx) - - try: - inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[ - batch_idx, :min_len, : - ] - except Exception as e: - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens[batch_idx].item()}" - ) - fbank_fake_len = encoder_out_lens[batch_idx].item() - min_len = min(fbank_fake_len, min_len) - inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[ - batch_idx, :min_len, : - ] - - with torch.cuda.amp.autocast( - enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype] - ): - labels_ids[labels_ids == -1] = -100 - attention_mask[attention_mask < 0] = 0 - model_outputs = self.llm( - inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]), - attention_mask=attention_mask, - labels=labels_ids, - ) - loss = model_outputs.loss - - stats = {} - with torch.no_grad(): - preds = torch.argmax(model_outputs.logits, -1) - acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100) - stats["acc"] = acc_att - - stats["loss"] = torch.clone(loss.detach()) - stats["batch_size"] = batch_size - stats["batch_size_x_frames"] = frames * batch_size - stats["batch_size_real_frames"] = speech_lengths.sum().item() - stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] - stats["batch_size_x_tokens"] = token_num * batch_size - stats["batch_size_real_tokens"] = attention_mask.sum().item() - stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] - - # force_gatherable: to-device and to-tensor if scalar for DataParallel - if self.length_normalized_loss: - batch_size = int((labels_ids > 0 + 1).sum()) - loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) - return loss, stats, weight - - def encode(self, speech, speech_lengths): - # audio encoder - encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) - - return encoder_out, encoder_out_lens - - def data_template(self, data): - system, user, assistant = [], [], [] - for i, item in enumerate(data): - role = item["role"] - content = item["content"] - if role == "system": - system.append(content) - elif role == "user": - user.append(content) - elif role == "assistant": - assistant.append(content) - - system = system * len(user) - - contents = { - "system": system, - "user": user, - "assistant": assistant, - } - - return contents - - def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs): - - system = contents["system"] - user = contents["user"] - assistant = contents["assistant"] - pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") - input_ids, labels, source_ids, target_ids, fbank, fbank_lens, fbank_mask, fbank_beg = ( - [], - [], - [], - [], - [], - [], - [], - [], - ) - - for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)): - - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - - splits = pattern.split(source_input) - source_ids_i = [] - fbank_mask_i = [] - fbank_beg_i = [] - fbank_lens_i = [] - # target_ids_i = [] - for k, sub_str in enumerate(splits): - if not sub_str.startswith("<|startofspeech|>"): - sub_token = tokenizer.encode(sub_str) - source_ids_i += sub_token - fbank_mask_i += [0] * len(sub_token) - else: - sub_str = sub_str.replace("<|startofspeech|>", "").replace( - "<|endofspeech|>", "" - ) - if sub_str.startswith("!"): - try: - time1 = time.perf_counter() - data_src = load_audio_text_image_video(sub_str[1:], fs=frontend.fs) - time2 = time.perf_counter() - meta_data["load_data"] = f"{time2 - time1:0.3f}" - except Exception as e: - logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}") - - speech, speech_lengths = extract_fbank( - data_src, - data_type=kwargs.get("data_type", "sound"), - frontend=frontend, - is_final=True, - ) # speech: [b, T, d] - - time3 = time.perf_counter() - meta_data["extract_feat"] = f"{time3 - time2:0.3f}" - meta_data["batch_data_time"] = ( - speech_lengths.sum().item() - * frontend.frame_shift - * frontend.lfr_n - / 1000 - ) - - if hasattr(frontend, "permute") and not frontend.permute: - # if kwargs.get("permute", True): - speech = speech.permute(0, 2, 1) - - if ( - kwargs.get("dataset_conf", {}).get("audio_encoder_downsample_rate", 1) - == 4 - ): - olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2 - olens = 1 + (olens - 3 + 2 * 1) // 2 - elif ( - kwargs.get("dataset_conf", {}).get("audio_encoder_downsample_rate", 1) - == 1 - ): - olens = speech_lengths[0].item() - - sub_token_len = (olens - 1) // kwargs.get("dataset_conf", {}).get( - "audio_adaptor_downsample_rate", 1 - ) + 1 - sub_token = [0] * sub_token_len - fbank_beg_i = [len(source_ids_i)] - source_ids_i += sub_token - fbank_mask_i += [1] * len(sub_token) - - source_mask = [-100] * len(source_ids_i) - target_out = f"{target_out}<|im_end|>" - target_ids = tokenizer.encode(target_out) - input_ids += source_ids_i + target_ids - labels += source_mask + target_ids - fbank_mask += fbank_mask_i - fbank_beg.append(fbank_beg_i) - - input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] - attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32) - labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] - source_ids = torch.tensor(source_ids_i, dtype=torch.int64) - target_ids = torch.tensor(target_ids, dtype=torch.int64) - - fbank = speech[0, :, :] - fbank_lens = speech_lengths - fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32) - fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32) - - output = { - "speech": fbank[None, :, :], - "speech_lengths": fbank_lens[:, None], - "fbank_mask": fbank_mask[None, :], - "fbank_beg": fbank_beg[None,], - "input_ids": input_ids[None, :], - "attention_mask": attention_mask[None, :], - "labels_ids": labels[None, :], - "source_ids": source_ids[None, :], - "target_ids": target_ids[None, :], - } - - return output - - def inference( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - meta_data = {} - prompt = kwargs.get("prompt", None) - - if kwargs.get("batch_size", 1) > 1: - raise NotImplementedError("batch decoding is not implemented") - - contents = self.data_template(data_in[0]) - output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs) - batch = to_device(output, kwargs["device"]) - - # audio encoder - speech = batch["speech"] - speech_lengths = batch["speech_lengths"][:, 0] - # fp16 - if kwargs.get("fp16", False): - speech = speech.to(torch.float16) - elif kwargs.get("bf16", False): - speech = speech.to(torch.bfloat16) - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - input_ids = batch["input_ids"] - source_ids = batch["source_ids"] - if not kwargs.get("tearchforing", False): - input_ids = source_ids - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - batch_size, token_num, dims = inputs_embeds.shape - fbank_beg = batch["fbank_beg"] - for batch_idx in range(batch_size): - - min_len = encoder_out_lens[batch_idx].item() - fbank_beg_idx = fbank_beg[batch_idx] - inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[ - batch_idx, :min_len, : - ] - - llm_dtype = kwargs.get("llm_dtype", "fp32") - if llm_dtype == "fp32": - llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype - llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype - - with torch.cuda.amp.autocast( - enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] - ): - label = contents["assistant"][0] - self.llm = self.llm.to(dtype_map[llm_dtype]) - inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) - - if not kwargs.get("tearchforing", False): - - generated_ids = self.llm.generate( - inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512) - ) - # generated_ids = [ - # output_ids[len(input_id) :] - # for input_id, output_ids in zip(input_ids, generated_ids) - # ] - response = tokenizer.batch_decode( - generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) - )[0] - - loss = None - else: - - labels_ids = batch["labels_ids"] - labels_ids[labels_ids == -1] = -100 - attention_mask = batch.get("attention_mask", None) - # attention_mask = attention_mask.to(dtype_map[llm_dtype]) - model_outputs = self.llm( - inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids - ) - - preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :] - response = tokenizer.batch_decode( - preds, - add_special_tokens=False, - skip_special_tokens=kwargs.get("skip_special_tokens", True), - )[0] - loss = model_outputs.loss.item() - - ibest_writer = None - if kwargs.get("output_dir") is not None: - if not hasattr(self, "writer"): - self.writer = DatadirWriter(kwargs.get("output_dir")) - ibest_writer = self.writer[f"{0 + 1}best_recog"] - - results = [] - response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response) - result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label} - if loss is not None: - result_i["loss"] = loss - results.append(result_i) - - if ibest_writer is not None: - ibest_writer["text"][key[0]] = response - ibest_writer["label"][key[0]] = label - ibest_writer["text_tn"][key[0]] = response_clean - - return results, meta_data - - -@tables.register("model_classes", "LLMASR3") -class LLMASR3(LLMASR2): - """ """ - - def __init__( - self, - *args, - **kwargs, - ): - - super().__init__(*args, **kwargs) - - def encode(self, speech, speech_lengths): - # audio encoder - encoder_out, encoder_out_lens = self.audio_encoder(speech, speech_lengths) - return encoder_out, encoder_out_lens - - # @tables.register("model_classes", "LLMASR4") -class LLMASR4(nn.Module): - """ """ - - def __init__( - self, - audio_encoder: str = None, - audio_encoder_conf: dict = None, - audio_adaptor: str = None, - audio_adaptor_conf: dict = None, - llm: str = None, - llm_conf: dict = None, - input_size: int = 80, - length_normalized_loss: bool = False, - **kwargs, - ): - - super().__init__() - - # audio encoder - hub = audio_encoder_conf.get("hub", None) - self.audio_encoder_activation_checkpoint = audio_encoder_conf.get( - "activation_checkpoint", False - ) - if hub == "ms": - from funasr import AutoModel - - model = AutoModel(model=audio_encoder, model_revision="master") - # frontend = model.kwargs.get("frontend") - audio_encoder_output_size = model.model.encoder_output_size - - audio_encoder = ( - model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder - ) - - # self.frontend = frontend - - elif hub == "hf": - pass - else: - encoder_class = tables.encoder_classes.get(audio_encoder) - audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf) - audio_encoder_output_size = audio_encoder.output_size() - freeze = audio_encoder_conf.get("freeze", True) - freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1)) - # if freeze_layer_num > 0: - # freeze_layer_num = range(freeze_layer_num) - - if freeze: - for name, param in audio_encoder.named_parameters(): - if freeze_layer_num > 0: - idx = re.search(r"\.\d+\.", name) - if idx is not None: - beg, end = idx.regs[0] - layer_id = int(name[beg + 1 : end - 1]) - if layer_id < freeze_layer_num: - param.requires_grad = False - elif "ln_post." not in name: - param.requires_grad = False - else: - param.requires_grad = False - - audio_encoder.eval() - - self.audio_encoder = audio_encoder - - # llm - self.llm = None - - from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig - - init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5") - llm_load_kwargs = llm_conf.get("load_kwargs", {}) - - if not llm_conf.get("low_cpu", False): - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map=None, - use_cache=None, - **llm_load_kwargs, - ) - else: - import os - - if int(os.environ.get("RANK", 0)) == 0: - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map="cpu", - use_cache=None, - **llm_load_kwargs, - ) - else: - llm_config = AutoConfig.from_pretrained(init_param_path) - model = AutoModelForCausalLM.from_config(llm_config) - - freeze = llm_conf.get("freeze", True) - if freeze: - for name, param in model.named_parameters(): - param.requires_grad = False - model.eval() - - logging.info(f"use_lora: {llm_conf.get('use_lora', False)}") - if llm_conf.get("use_lora", False): - from omegaconf import OmegaConf, DictConfig - - lora_conf = llm_conf.get("lora_conf", {}) - if isinstance(lora_conf, (OmegaConf, DictConfig)): - lora_conf = OmegaConf.to_container(lora_conf, resolve=True) - from peft import get_peft_model, LoraConfig, TaskType, PeftConfig, PeftModel - - lora_init_param_path = lora_conf.get("init_param_path", None) - if lora_init_param_path is not None: - logging.info(f"lora_init_param_path: {lora_init_param_path}") - model = PeftModel.from_pretrained(model, lora_init_param_path) - for name, param in model.named_parameters(): - if not lora_conf.get("freeze_lora", False): - if "lora_" in name: - param.requires_grad = True - else: - peft_config = LoraConfig(**lora_conf) - model = get_peft_model(model, peft_config) - - model.print_trainable_parameters() - - if llm_conf.get("activation_checkpoint", False): - model.gradient_checkpointing_enable() - - self.llm_dtype = llm_conf.get("llm_dtype", "fp32") - self.llm = model.to(dtype_map[self.llm_dtype]) - llm_dim = model.get_input_embeddings().weight.shape[-1] - - # adaptor - adaptor_class = tables.adaptor_classes.get(audio_adaptor) - audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size - audio_adaptor_conf["llm_dim"] = llm_dim - audio_adaptor = adaptor_class(**audio_adaptor_conf) - init_param_path = audio_adaptor_conf.get("init_param_path", None) - if init_param_path is not None: - src_state = torch.load(init_param_path, map_location="cpu") - flag = audio_adaptor.load_state_dict(src_state, strict=False) - logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}") - freeze = audio_adaptor_conf.get("freeze", False) - if freeze: - for name, param in audio_adaptor.named_parameters(): - param.requires_grad = False - audio_adaptor.eval() - - self.audio_adaptor = audio_adaptor - - self.error_calculator = None - - self.length_normalized_loss = length_normalized_loss - self.beam_search = None - import os - - rank = int(os.environ.get("RANK", 0)) - logging.info(f"rank: {rank}, model is builded.") - - def forward( - self, - speech: torch.Tensor = None, - speech_lengths: torch.Tensor = None, - input_ids: torch.Tensor = None, - attention_mask: torch.Tensor = None, - labels_ids: torch.Tensor = None, - fbank_beg: torch.Tensor = None, - fbank_mask: torch.Tensor = None, - **kwargs, - ): - """Encoder + Decoder + Calc loss - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - # import pdb - # - # pdb.set_trace() - batch_size, token_num = input_ids.shape - stats = {} - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - if speech is not None: - if len(speech_lengths.size()) > 1: - speech_lengths = speech_lengths[:, 0] - - batch_size_speech, frames, _ = speech.shape - - # audio encoder - if self.audio_encoder_activation_checkpoint: - from torch.utils.checkpoint import checkpoint - - encoder_out, encoder_out_lens = checkpoint( - self.encode, speech, speech_lengths, use_reentrant=False - ) - else: - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - batch_size, token_num, dims = inputs_embeds.shape - fake_token_len = kwargs.get("fake_token_len") - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - - stats["batch_size_speech"] = batch_size_speech - stats["batch_size_x_frames"] = frames * batch_size_speech - stats["batch_size_real_frames"] = speech_lengths.sum().item() - stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] - - with torch.cuda.amp.autocast( - enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype] - ): - labels_ids[labels_ids == -1] = -100 - attention_mask[attention_mask < 0] = 0 - model_outputs = self.llm( - inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]), - attention_mask=attention_mask, - labels=labels_ids, - ) - loss = model_outputs.loss - - with torch.no_grad(): - preds = torch.argmax(model_outputs.logits, -1) - acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100) - stats["acc"] = acc_att - - stats["loss"] = torch.clone(loss.detach()) - stats["batch_size"] = batch_size - - stats["batch_size_x_tokens"] = token_num * batch_size - stats["batch_size_real_tokens"] = attention_mask.sum().item() - stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] - - dialog_turns = (fbank_beg > 0).sum(-1) - dialog_turns_max = torch.max(dialog_turns).int().item() - dialog_turns_avg = dialog_turns.sum().item() / batch_size - stats["dialog_turns_max"] = dialog_turns_max - stats["dialog_turns_avg"] = dialog_turns_avg - - # force_gatherable: to-device and to-tensor if scalar for DataParallel - if self.length_normalized_loss: - batch_size = int((labels_ids > 0 + 1).sum()) - loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) - return loss, stats, weight - - def encode(self, speech, speech_lengths): - # audio encoder - encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) - - return encoder_out, encoder_out_lens - - def data_template(self, data): - system, user, assistant = [], [], [] - for i, item in enumerate(data): - role = item["role"] - content = item["content"] - if role == "system": - system.append(content) - elif role == "user": - if "audio" in item: - audio = item["audio"] - content = [content, audio] - user.append(content) - elif role == "assistant": - assistant.append(content) - - system = system * len(user) - - contents = { - "system": system, - "user": user, - "assistant": assistant, - } - - return contents - - def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs): - - system = contents["system"] - user = contents["user"] - assistant = contents["assistant"] - pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") - - input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = ( - [], - [], - [], - [], - [], - [], - [], - ) - input_source_ids = [] - for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)): - if i >= kwargs.get("multiturn_num_max", 5): - break - if len(input_ids) > kwargs.get("max_token_length", 1500): - - break - if isinstance(user_prompt, (list, tuple)): - user_prompt, audio = user_prompt - if i == 0: - if kwargs.get("infer_with_assistant_input", False): - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}" - else: - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - else: - if kwargs.get("infer_with_assistant_input", False): - source_input = f"<|im_start|>user\n{user_prompt}" - else: - source_input = ( - f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - ) - - splits = pattern.split(source_input) - source_ids = [] - fbank_i = [] - fbank_mask_i = [] - fake_token_len_i = 0 - fbank_beg_i = -1 - fbank_lens_i = [] - speech, speech_lengths = [], [] - for k, sub_str in enumerate(splits): - if not sub_str.startswith("<|startofspeech|>"): - sub_token = tokenizer.encode(sub_str) - source_ids += sub_token - fbank_mask_i += [0] * len(sub_token) - else: - sub_str = sub_str.replace("<|startofspeech|>", "").replace( - "<|endofspeech|>", "" - ) - if sub_str.startswith("!"): - sub_str = sub_str[1:] - if sub_str.startswith("!"): # !!: audio sample point - sub_str = audio - try: - time1 = time.perf_counter() - data_src = load_audio_text_image_video(sub_str, fs=frontend.fs) - time2 = time.perf_counter() - meta_data["load_data"] = f"{time2 - time1:0.3f}" - except Exception as e: - logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}") - - speech, speech_lengths = extract_fbank( - data_src, - data_type=kwargs.get("data_type", "sound"), - frontend=frontend, - is_final=True, - ) # speech: [b, T, d] - - time3 = time.perf_counter() - meta_data["extract_feat"] = f"{time3 - time2:0.3f}" - meta_data["batch_data_time"] = ( - speech_lengths.sum().item() - * frontend.frame_shift - * frontend.lfr_n - / 1000 - ) - - if kwargs.get("permute", True): - speech = speech.permute(0, 2, 1) - if speech_lengths > kwargs.get("max_source_length", 5500): - # logging.info( - # f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}" - # ) - badcase_flag = True - - olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2 - olens = 1 + (olens - 3 + 2 * 1) // 2 - fake_token_len_i = (olens - 1) // 2 + 1 - fake_token = [0] * fake_token_len_i - fbank_beg_i = len(source_ids) - source_ids += fake_token - fbank_mask_i += [1] * len(fake_token) - - fbank_beg += [fbank_beg_i + len(input_ids)] - fake_token_len += [fake_token_len_i] - source_mask = [-100] * len(source_ids) - target_out = f"{target_out}<|im_end|>" - target_ids = tokenizer.encode(target_out) - input_source_ids = input_ids + source_ids - input_ids += source_ids + target_ids - labels += source_mask + target_ids - fbank_mask += fbank_mask_i - if len(speech) > 0: - fbank.append(speech[0, :, :]) - fbank_lens.append(speech_lengths) - - input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] - attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32) - labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] - - # fbank = speech[0, :, :] - # fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32) - fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32) - fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32) - fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32) - source_ids = torch.tensor(input_source_ids, dtype=torch.int64) - target_ids = torch.tensor(target_ids, dtype=torch.int64) - - if len(fbank) > 0: - speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0) - speech_lengths = torch.nn.utils.rnn.pad_sequence( - fbank_lens, batch_first=True, padding_value=-1 - ) - else: - speech = [] - speech_lengths = [] - output = { - "speech": speech, - "speech_lengths": speech_lengths, - "fbank_mask": fbank_mask[None, :], - "fbank_beg": fbank_beg[None,], - "fake_token_len": fake_token_len[None, :], - "input_ids": input_ids[None,], - "attention_mask": attention_mask[None,], - "labels_ids": labels, - "source_ids": source_ids[None, :], - "target_ids": target_ids[None, :], - } - - return output - - def inference_prepare( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - meta_data = {} - prompt = kwargs.get("prompt", None) - - if kwargs.get("batch_size", 1) > 1: - raise NotImplementedError("batch decoding is not implemented") - - contents = self.data_template(data_in[0]) - output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs) - batch = to_device(output, kwargs["device"]) - - # audio encoder - speech = batch["speech"] - - if len(speech) > 0: - if "audio_embedding" in kwargs and "audio_embedding_lens" in kwargs: - encoder_out = kwargs["audio_embedding"] - encoder_out_lens = kwargs["audio_embedding_lens"] - else: - speech_lengths = batch["speech_lengths"][:, 0] - # fp16 - if kwargs.get("fp16", False): - speech = speech.to(torch.float16) - elif kwargs.get("bf16", False): - speech = speech.to(torch.bfloat16) - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - meta_data["audio_adaptor_out"] = encoder_out - meta_data["audio_adaptor_out_lens"] = encoder_out_lens - - input_ids = batch["input_ids"] - source_ids = batch["source_ids"] - fbank_beg = batch["fbank_beg"] - fake_token_len = batch["fake_token_len"] - - if not kwargs.get("tearchforing", False): - input_ids = source_ids - - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - batch_size, token_num, dims = inputs_embeds.shape - - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - return inputs_embeds, contents, batch, source_ids, meta_data - - def inference( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare( - data_in, data_lengths, key, tokenizer, frontend, **kwargs - ) - - llm_dtype = kwargs.get("llm_dtype", "fp32") - if llm_dtype == "fp32": - llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype - llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype - - with torch.cuda.amp.autocast( - enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] - ): - label = contents["assistant"][-1] - self.llm = self.llm.to(dtype_map[llm_dtype]) - inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) - llm_kwargs = kwargs.get("llm_kwargs", {}) - if not kwargs.get("tearchforing", False): - - generated_ids = self.llm.generate( - inputs_embeds=inputs_embeds, - max_new_tokens=kwargs.get("max_length", 512), - **llm_kwargs, - ) - # generated_ids = [ - # output_ids[len(input_id) :] - # for input_id, output_ids in zip(input_ids, generated_ids) - # ] - response = tokenizer.batch_decode( - generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) - )[0] - - loss = None - else: - - labels_ids = batch["labels_ids"] - labels_ids[labels_ids == -1] = -100 - attention_mask = batch.get("attention_mask", None) - # attention_mask = attention_mask.to(dtype_map[llm_dtype]) - model_outputs = self.llm( - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - labels=labels_ids, - **llm_kwargs, - ) - - preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :] - response = tokenizer.batch_decode( - preds, - add_special_tokens=False, - skip_special_tokens=kwargs.get("skip_special_tokens", True), - )[0] - loss = model_outputs.loss.item() - - ibest_writer = None - if kwargs.get("output_dir") is not None: - if not hasattr(self, "writer"): - self.writer = DatadirWriter(kwargs.get("output_dir")) - ibest_writer = self.writer[f"{0 + 1}best_recog"] - - results = [] - response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response) - result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label} - if loss is not None: - result_i["loss"] = loss - results.append(result_i) - - if ibest_writer is not None: - ibest_writer["text"][key[0]] = response.replace("\n", " ") - ibest_writer["label"][key[0]] = label.replace("\n", " ") - ibest_writer["text_tn"][key[0]] = response_clean - - return results, meta_data +# class LLMASR4(nn.Module): +# """ """ +# +# def __init__( +# self, +# audio_encoder: str = None, +# audio_encoder_conf: dict = None, +# audio_adaptor: str = None, +# audio_adaptor_conf: dict = None, +# llm: str = None, +# llm_conf: dict = None, +# input_size: int = 80, +# length_normalized_loss: bool = False, +# **kwargs, +# ): +# +# super().__init__() +# +# # audio encoder +# hub = audio_encoder_conf.get("hub", None) +# self.audio_encoder_activation_checkpoint = audio_encoder_conf.get( +# "activation_checkpoint", False +# ) +# if hub == "ms": +# from funasr import AutoModel +# +# model = AutoModel(model=audio_encoder, model_revision="master") +# # frontend = model.kwargs.get("frontend") +# audio_encoder_output_size = model.model.encoder_output_size +# +# audio_encoder = ( +# model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder +# ) +# +# # self.frontend = frontend +# +# elif hub == "hf": +# pass +# else: +# encoder_class = tables.encoder_classes.get(audio_encoder) +# audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf) +# audio_encoder_output_size = audio_encoder.output_size() +# freeze = audio_encoder_conf.get("freeze", True) +# freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1)) +# # if freeze_layer_num > 0: +# # freeze_layer_num = range(freeze_layer_num) +# +# if freeze: +# for name, param in audio_encoder.named_parameters(): +# if freeze_layer_num > 0: +# idx = re.search(r"\.\d+\.", name) +# if idx is not None: +# beg, end = idx.regs[0] +# layer_id = int(name[beg + 1 : end - 1]) +# if layer_id < freeze_layer_num: +# param.requires_grad = False +# elif "ln_post." not in name: +# param.requires_grad = False +# else: +# param.requires_grad = False +# +# audio_encoder.eval() +# +# self.audio_encoder = audio_encoder +# +# # llm +# self.llm = None +# +# from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig +# +# init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5") +# llm_load_kwargs = llm_conf.get("load_kwargs", {}) +# +# if not llm_conf.get("low_cpu", False): +# model = AutoModelForCausalLM.from_pretrained( +# init_param_path, +# load_in_8bit=None, +# device_map=None, +# use_cache=None, +# **llm_load_kwargs, +# ) +# else: +# import os +# +# if int(os.environ.get("RANK", 0)) == 0: +# model = AutoModelForCausalLM.from_pretrained( +# init_param_path, +# load_in_8bit=None, +# device_map="cpu", +# use_cache=None, +# **llm_load_kwargs, +# ) +# else: +# llm_config = AutoConfig.from_pretrained(init_param_path) +# model = AutoModelForCausalLM.from_config(llm_config) +# +# freeze = llm_conf.get("freeze", True) +# if freeze: +# for name, param in model.named_parameters(): +# param.requires_grad = False +# model.eval() +# +# logging.info(f"use_lora: {llm_conf.get('use_lora', False)}") +# if llm_conf.get("use_lora", False): +# from omegaconf import OmegaConf, DictConfig +# +# lora_conf = llm_conf.get("lora_conf", {}) +# if isinstance(lora_conf, (OmegaConf, DictConfig)): +# lora_conf = OmegaConf.to_container(lora_conf, resolve=True) +# from peft import get_peft_model, LoraConfig, TaskType, PeftConfig, PeftModel +# +# lora_init_param_path = lora_conf.get("init_param_path", None) +# if lora_init_param_path is not None: +# logging.info(f"lora_init_param_path: {lora_init_param_path}") +# model = PeftModel.from_pretrained(model, lora_init_param_path) +# for name, param in model.named_parameters(): +# if not lora_conf.get("freeze_lora", False): +# if "lora_" in name: +# param.requires_grad = True +# else: +# peft_config = LoraConfig(**lora_conf) +# model = get_peft_model(model, peft_config) +# +# model.print_trainable_parameters() +# +# if llm_conf.get("activation_checkpoint", False): +# model.gradient_checkpointing_enable() +# +# self.llm_dtype = llm_conf.get("llm_dtype", "fp32") +# self.llm = model.to(dtype_map[self.llm_dtype]) +# llm_dim = model.get_input_embeddings().weight.shape[-1] +# +# # adaptor +# adaptor_class = tables.adaptor_classes.get(audio_adaptor) +# audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size +# audio_adaptor_conf["llm_dim"] = llm_dim +# audio_adaptor = adaptor_class(**audio_adaptor_conf) +# init_param_path = audio_adaptor_conf.get("init_param_path", None) +# if init_param_path is not None: +# src_state = torch.load(init_param_path, map_location="cpu") +# flag = audio_adaptor.load_state_dict(src_state, strict=False) +# logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}") +# freeze = audio_adaptor_conf.get("freeze", False) +# if freeze: +# for name, param in audio_adaptor.named_parameters(): +# param.requires_grad = False +# audio_adaptor.eval() +# +# self.audio_adaptor = audio_adaptor +# +# self.error_calculator = None +# +# self.length_normalized_loss = length_normalized_loss +# self.beam_search = None +# import os +# +# rank = int(os.environ.get("RANK", 0)) +# logging.info(f"rank: {rank}, model is builded.") +# +# def forward( +# self, +# speech: torch.Tensor = None, +# speech_lengths: torch.Tensor = None, +# input_ids: torch.Tensor = None, +# attention_mask: torch.Tensor = None, +# labels_ids: torch.Tensor = None, +# fbank_beg: torch.Tensor = None, +# fbank_mask: torch.Tensor = None, +# **kwargs, +# ): +# """Encoder + Decoder + Calc loss +# Args: +# speech: (Batch, Length, ...) +# speech_lengths: (Batch, ) +# text: (Batch, Length) +# text_lengths: (Batch,) +# """ +# # import pdb +# # +# # pdb.set_trace() +# batch_size, token_num = input_ids.shape +# stats = {} +# input_ids[input_ids < 0] = 0 +# inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) +# if speech is not None: +# if len(speech_lengths.size()) > 1: +# speech_lengths = speech_lengths[:, 0] +# +# batch_size_speech, frames, _ = speech.shape +# +# # audio encoder +# if self.audio_encoder_activation_checkpoint: +# from torch.utils.checkpoint import checkpoint +# +# encoder_out, encoder_out_lens = checkpoint( +# self.encode, speech, speech_lengths, use_reentrant=False +# ) +# else: +# encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) +# +# # audio_adaptor +# encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) +# +# batch_size, token_num, dims = inputs_embeds.shape +# fake_token_len = kwargs.get("fake_token_len") +# fake_token_len[fake_token_len < 0] = 0 +# fbank_beg[fbank_beg < 0] = 0 +# +# speech_idx = 0 +# for batch_idx in range(batch_size): +# +# for turn_id in range(fbank_beg.shape[1]): +# fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() +# if fbank_beg_idx > 0: +# speech_token_len = fake_token_len[batch_idx, turn_id] +# speech_token = encoder_out[speech_idx, :speech_token_len, :] +# +# try: +# inputs_embeds[ +# batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : +# ] = speech_token +# except Exception as e: +# # +# logging.error(f"{str(e)}, {traceback.format_exc()}") +# logging.info( +# f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" +# ) +# # import pdb; +# # pdb.set_trace() +# speech_token_len = encoder_out_lens[speech_idx].item() +# speech_token = encoder_out[speech_idx, :speech_token_len, :] +# inputs_embeds[ +# batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : +# ] = speech_token +# +# speech_idx += 1 +# +# stats["batch_size_speech"] = batch_size_speech +# stats["batch_size_x_frames"] = frames * batch_size_speech +# stats["batch_size_real_frames"] = speech_lengths.sum().item() +# stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] +# +# with torch.cuda.amp.autocast( +# enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype] +# ): +# labels_ids[labels_ids == -1] = -100 +# attention_mask[attention_mask < 0] = 0 +# model_outputs = self.llm( +# inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]), +# attention_mask=attention_mask, +# labels=labels_ids, +# ) +# loss = model_outputs.loss +# +# with torch.no_grad(): +# preds = torch.argmax(model_outputs.logits, -1) +# acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100) +# stats["acc"] = acc_att +# +# stats["loss"] = torch.clone(loss.detach()) +# stats["batch_size"] = batch_size +# +# stats["batch_size_x_tokens"] = token_num * batch_size +# stats["batch_size_real_tokens"] = attention_mask.sum().item() +# stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] +# +# dialog_turns = (fbank_beg > 0).sum(-1) +# dialog_turns_max = torch.max(dialog_turns).int().item() +# dialog_turns_avg = dialog_turns.sum().item() / batch_size +# stats["dialog_turns_max"] = dialog_turns_max +# stats["dialog_turns_avg"] = dialog_turns_avg +# +# # force_gatherable: to-device and to-tensor if scalar for DataParallel +# if self.length_normalized_loss: +# batch_size = int((labels_ids > 0 + 1).sum()) +# loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) +# return loss, stats, weight +# +# def encode(self, speech, speech_lengths): +# # audio encoder +# encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) +# +# return encoder_out, encoder_out_lens +# +# def data_template(self, data): +# system, user, assistant = [], [], [] +# for i, item in enumerate(data): +# role = item["role"] +# content = item["content"] +# if role == "system": +# system.append(content) +# elif role == "user": +# if "audio" in item: +# audio = item["audio"] +# content = [content, audio] +# user.append(content) +# elif role == "assistant": +# assistant.append(content) +# +# system = system * len(user) +# +# contents = { +# "system": system, +# "user": user, +# "assistant": assistant, +# } +# +# return contents +# +# def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs): +# +# system = contents["system"] +# user = contents["user"] +# assistant = contents["assistant"] +# pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") +# +# input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = ( +# [], +# [], +# [], +# [], +# [], +# [], +# [], +# ) +# input_source_ids = [] +# for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)): +# if i >= kwargs.get("multiturn_num_max", 5): +# break +# if len(input_ids) > kwargs.get("max_token_length", 1500): +# +# break +# if isinstance(user_prompt, (list, tuple)): +# user_prompt, audio = user_prompt +# if i == 0: +# if kwargs.get("infer_with_assistant_input", False): +# source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}" +# else: +# source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" +# else: +# if kwargs.get("infer_with_assistant_input", False): +# source_input = f"<|im_start|>user\n{user_prompt}" +# else: +# source_input = ( +# f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" +# ) +# +# splits = pattern.split(source_input) +# source_ids = [] +# fbank_i = [] +# fbank_mask_i = [] +# fake_token_len_i = 0 +# fbank_beg_i = -1 +# fbank_lens_i = [] +# speech, speech_lengths = [], [] +# for k, sub_str in enumerate(splits): +# if not sub_str.startswith("<|startofspeech|>"): +# sub_token = tokenizer.encode(sub_str) +# source_ids += sub_token +# fbank_mask_i += [0] * len(sub_token) +# else: +# sub_str = sub_str.replace("<|startofspeech|>", "").replace( +# "<|endofspeech|>", "" +# ) +# if sub_str.startswith("!"): +# sub_str = sub_str[1:] +# if sub_str.startswith("!"): # !!: audio sample point +# sub_str = audio +# try: +# time1 = time.perf_counter() +# data_src = load_audio_text_image_video(sub_str, fs=frontend.fs) +# time2 = time.perf_counter() +# meta_data["load_data"] = f"{time2 - time1:0.3f}" +# except Exception as e: +# logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}") +# +# speech, speech_lengths = extract_fbank( +# data_src, +# data_type=kwargs.get("data_type", "sound"), +# frontend=frontend, +# is_final=True, +# ) # speech: [b, T, d] +# +# time3 = time.perf_counter() +# meta_data["extract_feat"] = f"{time3 - time2:0.3f}" +# meta_data["batch_data_time"] = ( +# speech_lengths.sum().item() +# * frontend.frame_shift +# * frontend.lfr_n +# / 1000 +# ) +# +# if kwargs.get("permute", True): +# speech = speech.permute(0, 2, 1) +# if speech_lengths > kwargs.get("max_source_length", 5500): +# # logging.info( +# # f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}" +# # ) +# badcase_flag = True +# +# olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2 +# olens = 1 + (olens - 3 + 2 * 1) // 2 +# fake_token_len_i = (olens - 1) // 2 + 1 +# fake_token = [0] * fake_token_len_i +# fbank_beg_i = len(source_ids) +# source_ids += fake_token +# fbank_mask_i += [1] * len(fake_token) +# +# fbank_beg += [fbank_beg_i + len(input_ids)] +# fake_token_len += [fake_token_len_i] +# source_mask = [-100] * len(source_ids) +# target_out = f"{target_out}<|im_end|>" +# target_ids = tokenizer.encode(target_out) +# input_source_ids = input_ids + source_ids +# input_ids += source_ids + target_ids +# labels += source_mask + target_ids +# fbank_mask += fbank_mask_i +# if len(speech) > 0: +# fbank.append(speech[0, :, :]) +# fbank_lens.append(speech_lengths) +# +# input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] +# attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32) +# labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] +# +# # fbank = speech[0, :, :] +# # fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32) +# fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32) +# fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32) +# fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32) +# source_ids = torch.tensor(input_source_ids, dtype=torch.int64) +# target_ids = torch.tensor(target_ids, dtype=torch.int64) +# +# if len(fbank) > 0: +# speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0) +# speech_lengths = torch.nn.utils.rnn.pad_sequence( +# fbank_lens, batch_first=True, padding_value=-1 +# ) +# else: +# speech = [] +# speech_lengths = [] +# output = { +# "speech": speech, +# "speech_lengths": speech_lengths, +# "fbank_mask": fbank_mask[None, :], +# "fbank_beg": fbank_beg[None,], +# "fake_token_len": fake_token_len[None, :], +# "input_ids": input_ids[None,], +# "attention_mask": attention_mask[None,], +# "labels_ids": labels, +# "source_ids": source_ids[None, :], +# "target_ids": target_ids[None, :], +# } +# +# return output +# +# def inference_prepare( +# self, +# data_in, +# data_lengths=None, +# key: list = None, +# tokenizer=None, +# frontend=None, +# **kwargs, +# ): +# +# meta_data = {} +# prompt = kwargs.get("prompt", None) +# +# if kwargs.get("batch_size", 1) > 1: +# raise NotImplementedError("batch decoding is not implemented") +# +# contents = self.data_template(data_in[0]) +# output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs) +# batch = to_device(output, kwargs["device"]) +# +# # audio encoder +# speech = batch["speech"] +# +# if len(speech) > 0: +# if "audio_embedding" in kwargs and "audio_embedding_lens" in kwargs: +# encoder_out = kwargs["audio_embedding"] +# encoder_out_lens = kwargs["audio_embedding_lens"] +# else: +# speech_lengths = batch["speech_lengths"][:, 0] +# # fp16 +# if kwargs.get("fp16", False): +# speech = speech.to(torch.float16) +# elif kwargs.get("bf16", False): +# speech = speech.to(torch.bfloat16) +# # audio encoder +# encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) +# +# # audio_adaptor +# encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) +# +# meta_data["audio_adaptor_out"] = encoder_out +# meta_data["audio_adaptor_out_lens"] = encoder_out_lens +# +# input_ids = batch["input_ids"] +# source_ids = batch["source_ids"] +# fbank_beg = batch["fbank_beg"] +# fake_token_len = batch["fake_token_len"] +# +# if not kwargs.get("tearchforing", False): +# input_ids = source_ids +# +# input_ids[input_ids < 0] = 0 +# inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) +# +# batch_size, token_num, dims = inputs_embeds.shape +# +# fake_token_len[fake_token_len < 0] = 0 +# fbank_beg[fbank_beg < 0] = 0 +# +# speech_idx = 0 +# for batch_idx in range(batch_size): +# +# for turn_id in range(fbank_beg.shape[1]): +# fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() +# if fbank_beg_idx > 0: +# speech_token_len = fake_token_len[batch_idx, turn_id] +# speech_token = encoder_out[speech_idx, :speech_token_len, :] +# +# try: +# inputs_embeds[ +# batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : +# ] = speech_token +# except Exception as e: +# # +# logging.error(f"{str(e)}, {traceback.format_exc()}") +# logging.info( +# f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" +# ) +# # import pdb; +# # pdb.set_trace() +# speech_token_len = encoder_out_lens[speech_idx].item() +# speech_token = encoder_out[speech_idx, :speech_token_len, :] +# inputs_embeds[ +# batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : +# ] = speech_token +# +# speech_idx += 1 +# return inputs_embeds, contents, batch, source_ids, meta_data +# +# def inference( +# self, +# data_in, +# data_lengths=None, +# key: list = None, +# tokenizer=None, +# frontend=None, +# **kwargs, +# ): +# +# inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare( +# data_in, data_lengths, key, tokenizer, frontend, **kwargs +# ) +# +# llm_dtype = kwargs.get("llm_dtype", "fp32") +# if llm_dtype == "fp32": +# llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype +# llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype +# +# with torch.cuda.amp.autocast( +# enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] +# ): +# label = contents["assistant"][-1] +# self.llm = self.llm.to(dtype_map[llm_dtype]) +# inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) +# llm_kwargs = kwargs.get("llm_kwargs", {}) +# if not kwargs.get("tearchforing", False): +# +# generated_ids = self.llm.generate( +# inputs_embeds=inputs_embeds, +# max_new_tokens=kwargs.get("max_length", 512), +# **llm_kwargs, +# ) +# # generated_ids = [ +# # output_ids[len(input_id) :] +# # for input_id, output_ids in zip(input_ids, generated_ids) +# # ] +# response = tokenizer.batch_decode( +# generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) +# )[0] +# +# loss = None +# else: +# +# labels_ids = batch["labels_ids"] +# labels_ids[labels_ids == -1] = -100 +# attention_mask = batch.get("attention_mask", None) +# # attention_mask = attention_mask.to(dtype_map[llm_dtype]) +# model_outputs = self.llm( +# inputs_embeds=inputs_embeds, +# attention_mask=attention_mask, +# labels=labels_ids, +# **llm_kwargs, +# ) +# +# preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :] +# response = tokenizer.batch_decode( +# preds, +# add_special_tokens=False, +# skip_special_tokens=kwargs.get("skip_special_tokens", True), +# )[0] +# loss = model_outputs.loss.item() +# +# ibest_writer = None +# if kwargs.get("output_dir") is not None: +# if not hasattr(self, "writer"): +# self.writer = DatadirWriter(kwargs.get("output_dir")) +# ibest_writer = self.writer[f"{0 + 1}best_recog"] +# +# results = [] +# response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response) +# result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label} +# if loss is not None: +# result_i["loss"] = loss +# results.append(result_i) +# +# if ibest_writer is not None: +# ibest_writer["text"][key[0]] = response.replace("\n", " ") +# ibest_writer["label"][key[0]] = label.replace("\n", " ") +# ibest_writer["text_tn"][key[0]] = response_clean +# +# return results, meta_data @tables.register("model_classes", "LLMASR4_extract_kv") @@ -2135,1256 +1636,6 @@ class LLMASR4_extract_kv(nn.Module): return results, meta_data -@tables.register("model_classes", "LLMASRXvecSlotTTS") -class LLMASRXvecSlotTTS(nn.Module): - """ """ - - def __init__( - self, - audio_encoder: str = None, - audio_encoder_conf: dict = None, - audio_adaptor: str = None, - audio_adaptor_conf: dict = None, - llm: str = None, - llm_conf: dict = None, - input_size: int = 80, - length_normalized_loss: bool = False, - **kwargs, - ): - - super().__init__() - - # audio encoder - hub = audio_encoder_conf.get("hub", None) - self.audio_encoder_activation_checkpoint = audio_encoder_conf.get( - "activation_checkpoint", False - ) - if hub == "ms": - from funasr import AutoModel - - model = AutoModel(model=audio_encoder, model_revision="master") - # frontend = model.kwargs.get("frontend") - audio_encoder_output_size = model.model.encoder_output_size - - audio_encoder = ( - model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder - ) - - # self.frontend = frontend - - elif hub == "hf": - pass - else: - encoder_class = tables.encoder_classes.get(audio_encoder) - audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf) - audio_encoder_output_size = audio_encoder.output_size() - freeze = audio_encoder_conf.get("freeze", True) - freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1)) - # if freeze_layer_num > 0: - # freeze_layer_num = range(freeze_layer_num) - - if freeze: - for name, param in audio_encoder.named_parameters(): - if freeze_layer_num > 0: - idx = re.search(r"\.\d+\.", name) - if idx is not None: - beg, end = idx.regs[0] - layer_id = int(name[beg + 1 : end - 1]) - if layer_id < freeze_layer_num: - param.requires_grad = False - elif "ln_post." not in name: - param.requires_grad = False - else: - param.requires_grad = False - - audio_encoder.eval() - - self.audio_encoder = audio_encoder - - # llm - self.llm = None - - from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig - - init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5") - - if not llm_conf.get("low_cpu", False): - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map=None, - use_cache=None, - output_hidden_states=True, - ) - else: - import os - - if int(os.environ.get("RANK", 0)) == 0: - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map="cpu", - use_cache=None, - ) - else: - llm_config = AutoConfig.from_pretrained(init_param_path) - model = AutoModelForCausalLM.from_config(llm_config) - - freeze = llm_conf.get("freeze", True) - if freeze: - for name, param in model.named_parameters(): - param.requires_grad = False - model.eval() - - logging.info(f"use_lora: {llm_conf.get('use_lora', False)}") - if llm_conf.get("use_lora", False): - from omegaconf import OmegaConf, DictConfig - - lora_conf = llm_conf.get("lora_conf", {}) - if isinstance(lora_conf, (OmegaConf, DictConfig)): - lora_conf = OmegaConf.to_container(lora_conf, resolve=True) - from peft import get_peft_model, LoraConfig, TaskType, PeftConfig, PeftModel - - lora_init_param_path = lora_conf.get("init_param_path", None) - if lora_init_param_path is not None: - model = PeftModel.from_pretrained(model, lora_init_param_path) - else: - peft_config = LoraConfig(**lora_conf) - model = get_peft_model(model, peft_config) - model.print_trainable_parameters() - - if llm_conf.get("activation_checkpoint", False): - model.gradient_checkpointing_enable() - - self.llm_dtype = llm_conf.get("llm_dtype", "fp32") - self.llm = model.to(dtype_map[self.llm_dtype]) - llm_dim = model.get_input_embeddings().weight.shape[-1] - - # adaptor - adaptor_class = tables.adaptor_classes.get(audio_adaptor) - audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size - audio_adaptor_conf["llm_dim"] = llm_dim - audio_adaptor = adaptor_class(**audio_adaptor_conf) - init_param_path = audio_adaptor_conf.get("init_param_path", None) - if init_param_path is not None: - src_state = torch.load(init_param_path, map_location="cpu") - flag = audio_adaptor.load_state_dict(src_state, strict=False) - logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}") - freeze = audio_adaptor_conf.get("freeze", False) - if freeze: - for name, param in audio_adaptor.named_parameters(): - param.requires_grad = False - audio_adaptor.eval() - - self.audio_adaptor = audio_adaptor - - self.error_calculator = None - - self.length_normalized_loss = length_normalized_loss - self.beam_search = None - - # tts text tokenizer related - audio_decoder_conf = kwargs.get("audio_decoder_conf", {}) - tts_token_type = audio_decoder_conf.get("tts_token_type", "whisper_rich_ttsfrd") - ttsfrd_res_dir = audio_decoder_conf.get("ttsfrd_res_dir", "./ttsfrd/9.5.5") - from funasr.models.llm_asr.tts_text_tokenizer.build_tokenizer import build_tokenizer - - self.tts_text_tokenizer = build_tokenizer( - tts_token_type, - bpemodel=ttsfrd_res_dir, - p_word2phn=1.0, - ) - # e2e tts model related - from funasr.models.llm_asr.tts_models.e2e_model import UCTDXvecSlotModel - - self.tts_model = UCTDXvecSlotModel(**kwargs.get("tts_model_conf", {})).to(torch.float32) - # vocoder related - vocoder_name = kwargs.get("vocoder", None) - vocoder_conf = kwargs.get("vocoder_conf", None) - self.vocoder = self.build_vocoder(name=vocoder_name, conf=vocoder_conf).to(torch.float32) - import lameenc - - self.mp3_encoder = lameenc.Encoder() - self.mp3_encoder.set_bit_rate(128) - self.mp3_encoder.set_in_sample_rate(22050) - self.mp3_encoder.set_channels(1) - self.mp3_encoder.set_quality(2) - - import os - - rank = int(os.environ.get("RANK", 0)) - logging.info(f"rank: {rank}, model is builded.") - - def build_vocoder(self, name: str, conf: dict): - if name is None or conf is None: - return None - if name == "HifiGAN": - from funasr.models.llm_asr.hifigan import HifiGan - - return HifiGan(**conf) - return None - - def forward( - self, - speech: torch.Tensor = None, - speech_lengths: torch.Tensor = None, - input_ids: torch.Tensor = None, - attention_mask: torch.Tensor = None, - labels_ids: torch.Tensor = None, - fbank_beg: torch.Tensor = None, - fbank_mask: torch.Tensor = None, - **kwargs, - ): - """Encoder + Decoder + Calc loss - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - # import pdb - # - # pdb.set_trace() - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - if speech is not None: - if len(speech_lengths.size()) > 1: - speech_lengths = speech_lengths[:, 0] - - batch_size_speech, frames, _ = speech.shape - batch_size, token_num = input_ids.shape - - # with torch.cuda.amp.autocast(enabled=False): - # audio encoder - if self.audio_encoder_activation_checkpoint: - from torch.utils.checkpoint import checkpoint - - encoder_out, encoder_out_lens = checkpoint( - self.encode, speech, speech_lengths, use_reentrant=False - ) - else: - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - batch_size, token_num, dims = inputs_embeds.shape - fake_token_len = kwargs.get("fake_token_len") - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - - with torch.cuda.amp.autocast( - enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype] - ): - labels_ids[labels_ids == -1] = -100 - attention_mask[attention_mask < 0] = 0 - model_outputs = self.llm( - inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]), - attention_mask=attention_mask, - labels=labels_ids, - ) - loss = model_outputs.loss - - stats = {} - with torch.no_grad(): - preds = torch.argmax(model_outputs.logits, -1) - acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100) - stats["acc"] = acc_att - - # tts model training related - # audio sampling point - audio = kwargs.get("audio") - audio_len = kwargs.get("audio_len") - - # codec - codec = kwargs.get("codec") - codec_len = (codec > 0).sum(-1) - - input_mask = kwargs.get("input_mask") - input_mask[input_mask < 0] = 0 - - hidden_states = model_outputs.hidden_states[-1].float() - hidden_states_his_select = [] - - # target, str - target_ids = [] - target_ids_len = [] - turn_id_cum = 0 - for batch_idx in range(labels_ids.shape[0]): - - for turn_id in range(fbank_beg.shape[1]): - beg = 0 - end = input_mask[turn_id_cum].sum(-1) - print(f"beg: {beg}, end: {end}") - hidden_states_his_i = hidden_states[batch_idx, beg:end, :] - hidden_states_his_select.append(hidden_states_his_i) - - turn_id_cum += 1 - - beg_i = 0 - end_i = 0 - for token_idx in range(labels_ids.shape[1]): - token_int = labels_ids[batch_idx, token_idx].item() - if token_int == self.eos: - target_ids_i = labels_ids[batch_idx, beg_i:end_i] - target_ids_len_i = end_i - beg_i - target_ids_len.append(target_ids_len_i) - target = self.tokenizer.decode(target_ids_i) - target_ids.append(target) - - end_i += 1 - beg_i = end_i - continue - - end_i += 1 - if token_int <= 0: - beg_i += 1 - - # hidden_states_his_select - hidden_states_his_select = torch.nn.utils.rnn.pad_sequence( - hidden_states_his_select, batch_first=True, padding_value=0.0 - ) - hidden_states_his_select = hidden_states_his_select.to(device=input_ids.device) - hidden_states_his_select_len = input_mask.sum(-1) - - # nar tts model related - device = hidden_states_his_select.device - text = [self.tts_text_tokenizer.text2tokens(x) for x in target_ids] - text_lengths = [len(x) for x in text] - text = pad_list(text, pad_value=-1).long().to(device) - text_lengths = torch.tensor(text_lengths).to(audio_len) - # mute the "da" noise. - # TODO: make sure the sample rate is 22050. - audio[:, : int(0.02 * 22050)] = 0 - hidden_states_his_select = self.tts_dim_proj(hidden_states_his_select) - tts_loss, tts_states, tts_weight = self.tts_model.forward( - text=text, - text_lengths=text_lengths, - speech_token=codec, - speech_token_lengths=codec_len, - audio=audio, - audio_lengths=audio_len, - prompt=hidden_states_his_select, - prompt_len=hidden_states_his_select_len, - ) - loss = loss + tts_loss - for key, value in tts_states.items(): - stats[f"tts_{key}"] = value - - stats["loss"] = torch.clone(loss.detach()) - stats["batch_size"] = batch_size - stats["batch_size_speech"] = batch_size_speech - stats["batch_size_x_frames"] = frames * batch_size_speech - stats["batch_size_real_frames"] = speech_lengths.sum().item() - stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] - stats["batch_size_x_tokens"] = token_num * batch_size - stats["batch_size_real_tokens"] = attention_mask.sum().item() - stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] - - dialog_turns = (fbank_beg > 0).sum(-1) - dialog_turns_max = torch.max(dialog_turns).int().item() - dialog_turns_avg = dialog_turns.sum().item() / batch_size - stats["dialog_turns_max"] = dialog_turns_max - stats["dialog_turns_avg"] = dialog_turns_avg - - # force_gatherable: to-device and to-tensor if scalar for DataParallel - if self.length_normalized_loss: - batch_size = int((labels_ids > 0 + 1).sum()) - loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) - return loss, stats, weight - - def encode(self, speech, speech_lengths): - # audio encoder - encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) - - return encoder_out, encoder_out_lens - - def data_template(self, data): - system, user, assistant = [], [], [] - for i, item in enumerate(data): - role = item["role"] - content = item["content"] - if role == "system": - system.append(content) - elif role == "user": - if "audio" in item: - audio = item["audio"] - content = [content, audio] - user.append(content) - elif role == "assistant": - assistant.append(content) - - system = system * len(user) - - contents = { - "system": system, - "user": user, - "assistant": assistant, - } - - return contents - - def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs): - - system = contents["system"] - user = contents["user"] - assistant = contents["assistant"] - pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") - - ( - input_ids, - labels, - fbank, - fbank_lens, - fbank_mask, - fbank_beg, - fake_token_len, - input_mask, - input_mask_beg, - ) = ([], [], [], [], [], [], [], [], []) - input_source_ids = [] - for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)): - if i >= kwargs.get("multiturn_num_max", 5): - break - if len(input_ids) > kwargs.get("max_token_length", 1500): - - break - if isinstance(user_prompt, (list, tuple)): - user_prompt, audio = user_prompt - if i == 0: - if kwargs.get("infer_with_assistant_input", False): - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}" - else: - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - else: - if kwargs.get("infer_with_assistant_input", False): - source_input = f"<|im_start|>user\n{user_prompt}" - else: - source_input = ( - f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - ) - - splits = pattern.split(source_input) - source_ids = [] - fbank_i = [] - fbank_mask_i = [] - fake_token_len_i = 0 - fbank_beg_i = -1 - fbank_lens_i = [] - speech, speech_lengths = [], [] - for k, sub_str in enumerate(splits): - if not sub_str.startswith("<|startofspeech|>"): - sub_token = tokenizer.encode(sub_str) - source_ids += sub_token - fbank_mask_i += [0] * len(sub_token) - else: - sub_str = sub_str.replace("<|startofspeech|>", "").replace( - "<|endofspeech|>", "" - ) - if sub_str.startswith("!"): - sub_str = sub_str[1:] - if sub_str.startswith("!"): # !!: audio sample point - sub_str = audio - try: - time1 = time.perf_counter() - data_src = load_audio_text_image_video(sub_str, fs=frontend.fs) - time2 = time.perf_counter() - meta_data["load_data"] = f"{time2 - time1:0.3f}" - except Exception as e: - logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}") - - speech, speech_lengths = extract_fbank( - data_src, - data_type=kwargs.get("data_type", "sound"), - frontend=frontend, - is_final=True, - ) # speech: [b, T, d] - - time3 = time.perf_counter() - meta_data["extract_feat"] = f"{time3 - time2:0.3f}" - meta_data["batch_data_time"] = ( - speech_lengths.sum().item() - * frontend.frame_shift - * frontend.lfr_n - / 1000 - ) - - if kwargs.get("permute", True): - speech = speech.permute(0, 2, 1) - if speech_lengths > kwargs.get("max_source_length", 5500): - # logging.info( - # f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}" - # ) - badcase_flag = True - - olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2 - olens = 1 + (olens - 3 + 2 * 1) // 2 - fake_token_len_i = (olens - 1) // 2 + 1 - fake_token = [0] * fake_token_len_i - fbank_beg_i = len(source_ids) - source_ids += fake_token - fbank_mask_i += [1] * len(fake_token) - - fbank_beg += [fbank_beg_i + len(input_ids)] - fake_token_len += [fake_token_len_i] - source_mask = [-100] * len(source_ids) - target_out = f"{target_out}<|im_end|>" - target_ids = tokenizer.encode(target_out) - - if i == 0: - sys_prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n" - sys_prompt_len = tokenizer.encode(sys_prompt) - input_mask_i = ( - [1] * len(sys_prompt_len) + [0] * len(source_ids) + [0] * len(target_ids) - ) - else: - input_mask_i = [1] * len(input_ids) + [0] * len(source_ids) - input_mask_i = torch.tensor(input_mask_i, dtype=torch.int64) - input_mask_beg.append(input_mask_i) - - input_mask_i = [1] * len(input_ids) + [1] * len(source_ids) - input_mask_i = torch.tensor(input_mask_i, dtype=torch.int64) - input_mask.append(input_mask_i) - - input_source_ids = input_ids + source_ids - input_ids += source_ids + target_ids - labels += source_mask + target_ids - fbank_mask += fbank_mask_i - if len(speech) > 0: - fbank.append(speech[0, :, :]) - fbank_lens.append(speech_lengths) - - input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] - attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32) - labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] - - # fbank = speech[0, :, :] - # fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32) - fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32) - fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32) - fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32) - source_ids = torch.tensor(input_source_ids, dtype=torch.int64) - target_ids = torch.tensor(target_ids, dtype=torch.int64) - - if len(fbank) > 0: - speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0) - speech_lengths = torch.nn.utils.rnn.pad_sequence( - fbank_lens, batch_first=True, padding_value=-1 - ) - else: - speech = [] - speech_lengths = [] - output = { - "speech": speech, - "speech_lengths": speech_lengths, - "fbank_mask": fbank_mask[None, :], - "fbank_beg": fbank_beg[None,], - "fake_token_len": fake_token_len[None, :], - "input_ids": input_ids[None,], - "attention_mask": attention_mask[None,], - "labels_ids": labels, - "source_ids": source_ids[None, :], - "target_ids": target_ids[None, :], - } - if len(input_mask) > 0: - output["input_mask"] = input_mask - output["input_mask_beg"] = input_mask_beg - return output - - def inference_prepare( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - meta_data = {} - prompt = kwargs.get("prompt", None) - - if kwargs.get("batch_size", 1) > 1: - raise NotImplementedError("batch decoding is not implemented") - - contents = self.data_template(data_in[0]) - output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs) - batch = to_device(output, kwargs["device"]) - - # audio encoder - speech = batch["speech"] - if len(speech) > 0: - speech_lengths = batch["speech_lengths"][:, 0] - # fp16 - if kwargs.get("fp16", False): - speech = speech.to(torch.float16) - elif kwargs.get("bf16", False): - speech = speech.to(torch.bfloat16) - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - input_ids = batch["input_ids"] - source_ids = batch["source_ids"] - fbank_beg = batch["fbank_beg"] - fake_token_len = batch["fake_token_len"] - - if not kwargs.get("tearchforing", False): - input_ids = source_ids - - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - batch_size, token_num, dims = inputs_embeds.shape - - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - return inputs_embeds, contents, batch, source_ids, meta_data - - def inference( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare( - data_in, data_lengths, key, tokenizer, frontend, **kwargs - ) - rand_seed = kwargs.get("rand_seed", 0) - - llm_dtype = kwargs.get("llm_dtype", "fp32") - if llm_dtype == "fp32": - llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype - llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype - - with torch.cuda.amp.autocast( - enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] - ): - label = contents["assistant"][-1] - self.llm.to(dtype_map[llm_dtype]) - inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) - llm_kwargs = kwargs.get("llm_kwargs", {}) - if not kwargs.get("tearchforing", False): - - generated_ids = self.llm.generate( - inputs_embeds=inputs_embeds, - max_new_tokens=kwargs.get("max_length", 512), - output_hidden_states=True, - return_dict_in_generate=True, - output_scores=True, - **llm_kwargs, - ) - - # TODO: get llm_cur_kv_cache - - target_ids = generated_ids["sequences"] - response = tokenizer.batch_decode( - target_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) - )[0] - - loss = None - else: - - labels_ids = batch["labels_ids"] - labels_ids[labels_ids == -1] = -100 - attention_mask = batch.get("attention_mask", None) - # attention_mask = attention_mask.to(dtype_map[llm_dtype]) - model_outputs = self.llm( - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - labels=labels_ids, - **llm_kwargs, - ) - - preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :] - response = tokenizer.batch_decode( - preds, - add_special_tokens=False, - skip_special_tokens=kwargs.get("skip_special_tokens", True), - )[0] - loss = model_outputs.loss.item() - - ibest_writer = None - if kwargs.get("output_dir") is not None: - if not hasattr(self, "writer"): - self.writer = DatadirWriter(kwargs.get("output_dir")) - ibest_writer = self.writer[f"{0 + 1}best_recog"] - - results = [] - response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response) - result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label} - if loss is not None: - result_i["loss"] = loss - results.append(result_i) - - if ibest_writer is not None: - ibest_writer["text"][key[0]] = response.replace("\n", " ") - ibest_writer["label"][key[0]] = label.replace("\n", " ") - ibest_writer["text_tn"][key[0]] = response_clean - - # tts related inference, require the kv cache of llm last layer for only the current inputs - # TODO: select kv cache of the current turn inputs - attention_mask = batch.get("attention_mask", None) - model_outputs = self.llm( - inputs_embeds=inputs_embeds, - attention_mask=None, - labels=None, - **llm_kwargs, - ) - hidden_states = model_outputs.hidden_states[-1].float() - # hidden_states = generated_ids[ - # "hidden_states" - # ] # hidden_states: (t1, t2, ..., tn, ..., tN), tn=(l1, l2, ..., ln, ..., lN), ln: shape: 1x1x3584 - - # token_num = len(hidden_states) - # hidden_states_select = torch.zeros((1, token_num, 3584), dtype=torch.float32).to( - # inputs_embeds.device - # ) - # - # for i in range(token_num): - # hidden_states_select[0, i, :] = hidden_states[i][-1][0, 0, :].to(torch.float32) - - llm_cur_kv_cache, llm_cur_kv_cache_len = None, None - - input_mask_beg = batch.get("input_mask_beg")[-1][None, :] - input_mask_beg[input_mask_beg < 0] = 0 - input_mask = batch.get("input_mask")[-1][None, :] - input_mask[input_mask < 0] = 0 - - for turn_id_cum in range(input_mask.shape[0]): - beg = input_mask_beg[turn_id_cum].sum(-1) - end = input_mask[turn_id_cum].sum(-1) - llm_cur_kv_cache = hidden_states[:, beg:end, :] - llm_cur_kv_cache_len = torch.tensor( - [ - end - beg, - ], - dtype=torch.int32, - ).to(inputs_embeds.device) - # Generative quality is sensitive to dtype, FM requires fp32 - tts_dtype = "fp32" - with torch.cuda.amp.autocast( - enabled=True if tts_dtype != "fp32" else False, dtype=dtype_map[tts_dtype] - ): - assert llm_cur_kv_cache is not None - # set_all_random_seed(rand_seed) - # speech_tokens, mel, wav = self.generate_speech( - # response, llm_cur_kv_cache, llm_cur_kv_cache_len, dtype_map[tts_dtype] - # ) - speech_tokens, mel, wav, mp3 = self.simulate_streaming_generate_speech( - target_ids, llm_cur_kv_cache, llm_cur_kv_cache_len, dtype_map[tts_dtype], tokenizer - ) - self.write_mel_wav(kwargs.get("output_dir"), mel, wav, mp3, key[0]) - - return results, meta_data - - def prepare_k_v_cache(self, inputs_embeds, contents, batch, source_ids, meta_data, **kwargs): - - # tts related inference, require the kv cache of llm last layer for only the current inputs - # TODO: select kv cache of the current turn inputs - attention_mask = batch.get("attention_mask", None) - model_outputs = self.llm( - inputs_embeds=inputs_embeds, - attention_mask=None, - labels=None, - ) - hidden_states = model_outputs.hidden_states[-1].float() - - llm_cur_kv_cache, llm_cur_kv_cache_len = None, None - - input_mask_beg = batch.get("input_mask_beg")[-1][None, :] - input_mask_beg[input_mask_beg < 0] = 0 - input_mask = batch.get("input_mask")[-1][None, :] - input_mask[input_mask < 0] = 0 - - for turn_id_cum in range(input_mask.shape[0]): - beg = input_mask_beg[turn_id_cum].sum(-1) - end = input_mask[turn_id_cum].sum(-1) - llm_cur_kv_cache = hidden_states[:, beg:end, :] - llm_cur_kv_cache_len = torch.tensor( - [ - end - beg, - ], - dtype=torch.int32, - ).to(inputs_embeds.device) - - return llm_cur_kv_cache, llm_cur_kv_cache_len - - def generate_speech(self, text, llm_cur_kv_cache, llm_cur_kv_cache_len, llm_dtype): - # self.tts_text_tokenizer = self.tts_text_tokenizer - self.vocoder.to(llm_dtype) - device = llm_cur_kv_cache.device - # tokenize text - text_token = self.tts_text_tokenizer.text2tokens(f"<|endofprompt|><|sil|>{text}<|sil|>") - text_token = torch.tensor([text_token], dtype=torch.long, device=device) - text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.long, device=device) - # e2e tts model forward - self.tts_model.to(llm_dtype) - speech_tokens, mel_feats = self.tts_model.inference( - text_token, - text_token_len, - None, - None, - outside_prompt=llm_cur_kv_cache, - outside_prompt_lengths=llm_cur_kv_cache_len, - sampling="threshold_1e-6", - ) - # vocoder forward - wav = self.vocoder.inference(mel_feats.transpose(1, 2)) - - return speech_tokens, mel_feats, wav - - def split_characters_and_words(self, input_string): - # 定义正则表达式模式 - pattern = r"[\u4e00-\u9fff]|[\w]+|[^\w\s]" - # 使用 re.findall 找到所有匹配的字符和单词 - results = re.findall(pattern, input_string) - return results - - def tts_tokenizer_warpper(self, text): - text_token = self.tts_text_tokenizer.text2tokens(text) - # remove the added pouc by ttsfrd. - if text[-1] != "。" and text_token[-1] == 1542: - text_token = text_token[:-1] - return text_token - - def find_pounc_idx(self, pouncs: list, text: str): - idx = -1 - for p in pouncs: - idx = text.find(p) - if idx >= 0: - break - - return idx - - @torch.no_grad() - def generate_speech_one_step( - self, - text: str, - preds: str, - last_t_size, - llm_cur_kv_cache, - llm_cur_kv_cache_len, - prompt_token, - prompt_audio, - tts_text_chunk_size, - chunk_idx, - is_last, - para_phone_len=200, - ): - device = llm_cur_kv_cache.device - pounc = ["。", "？", "！", "；", "：", ".", "?", "!", ";", "\n"] - - # remove duplicated pounctuations - normed_preds = [] - for i, c in enumerate(preds): - if i > 0 and preds[i - 1] in pounc and preds[i] in pounc: - continue - normed_preds.append(c) - normed_preds = "".join(normed_preds) - idx = -1 - for p in pounc: - str_idx = normed_preds.find(p) - if str_idx > -1: - preds = self.split_characters_and_words(normed_preds[:str_idx]) - idx = len(preds) - preds.append(normed_preds[str_idx]) - preds.extend(self.split_characters_and_words(normed_preds[str_idx + 1 :])) - break - - _text = f"<|endofprompt|><|sil|>{text+normed_preds}" + ("<|sil|>" if is_last else "") - para_end = False - if idx > -1 and not is_last: - pre_part = "".join(preds[: idx + 1]) - if len(self.tts_tokenizer_warpper(text + pre_part)) >= para_phone_len: - _text = f"<|endofprompt|><|sil|>{text+pre_part}<|sil|>" - para_end = True - - cur_token, feat, wav = None, None, None - text_token = self.tts_tokenizer_warpper(_text) - t_size = len(text_token) - if (t_size - last_t_size) >= tts_text_chunk_size or is_last or para_end: - text_token = torch.tensor([text_token], dtype=torch.long, device=device) - text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.long, device=device) - cur_token, feat = self.tts_model.streaming_one_step( - text_token, - text_token_len, - xvec=None, - xvec_lengths=None, - prompt_dict={ - "prompt_token": prompt_token, - "prompt_audio": prompt_audio, - }, - outside_prompt=llm_cur_kv_cache, - outside_prompt_lengths=llm_cur_kv_cache_len, - sampling="threshold_6e-1", - chunk_idx=chunk_idx, - diff_steps=5, - ) - if cur_token is not None and cur_token.shape[1] > 0 and feat.shape[2] > 0: - # process first package, token in B,T,D, feat in B,F,T - if prompt_token[0] is None: - prompt_token = [ - cur_token, - torch.tensor([cur_token.shape[1]], dtype=torch.long, device=device), - ] - prompt_audio = [ - feat.transpose(1, 2), - torch.tensor([feat.shape[2]], dtype=torch.long, device=device), - ] - else: - prompt_token[1] = prompt_token[1] + cur_token.shape[1] - prompt_token[0] = torch.concat([prompt_token[0], cur_token], dim=1) - prompt_audio[1] = prompt_audio[1] + feat.shape[2] - prompt_audio[0] = torch.concat([prompt_audio[0], feat.transpose(1, 2)], dim=1) - wav = self.vocoder.inference(feat.transpose(1, 2)) - chunk_idx += 1 - else: - cur_token, feat, wav = None, None, None - - # post process - if not para_end: - last_t_size = t_size - - if para_end: - text = "".join(preds[idx + 1 :]) - last_t_size = 0 - prompt_token, prompt_audio = [None, None], [None, None] - wav = torch.cat([wav, torch.zeros([1, 2205]).to(wav)], dim=1) - chunk_idx = 0 - else: - text = text + normed_preds - - return ((cur_token, feat, wav), (text, last_t_size, prompt_token, prompt_audio, chunk_idx)) - - @torch.no_grad() - def simple_generate_speech_one_step( - self, - text: str, - preds: str, - last_t_size, - llm_cur_kv_cache, - llm_cur_kv_cache_len, - prompt_token, - prompt_audio, - tts_text_chunk_size, - chunk_idx, - is_last, - para_phone_len=200, - ): - device = llm_cur_kv_cache.device - _text = f"<|endofprompt|><|sil|>{text}" + ("<|sil|>" if is_last else "") - text_token = self.tts_tokenizer_warpper(_text) - - cur_token, feat, wav = None, None, None - if len(text_token) > tts_text_chunk_size: - text_token = torch.tensor([text_token], dtype=torch.long, device=device) - text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.long, device=device) - cur_token, feat = self.tts_model.streaming_one_step( - text_token, - text_token_len, - xvec=None, - xvec_lengths=None, - prompt_dict={ - "prompt_token": prompt_token, - "prompt_audio": prompt_audio, - }, - outside_prompt=llm_cur_kv_cache, - outside_prompt_lengths=llm_cur_kv_cache_len, - sampling="threshold_6e-1", - chunk_idx=chunk_idx, - diff_steps=5, - ) - if cur_token is not None and cur_token.shape[1] > 0 and feat.shape[2] > 0: - # process first package, token in B,T,D, feat in B,F,T - if prompt_token[0] is None: - prompt_token = [ - cur_token, - torch.tensor([cur_token.shape[1]], dtype=torch.long, device=device), - ] - prompt_audio = [ - feat.transpose(1, 2), - torch.tensor([feat.shape[2]], dtype=torch.long, device=device), - ] - else: - prompt_token[1] = prompt_token[1] + cur_token.shape[1] - prompt_token[0] = torch.concat([prompt_token[0], cur_token], dim=1) - prompt_audio[1] = prompt_audio[1] + feat.shape[2] - prompt_audio[0] = torch.concat([prompt_audio[0], feat.transpose(1, 2)], dim=1) - wav = self.vocoder.inference(feat.transpose(1, 2)) - chunk_idx += 1 - - return ((cur_token, feat, wav), (text, last_t_size, prompt_token, prompt_audio, chunk_idx)) - - def convert_wav_to_mp3(self, wav: torch.Tensor, is_last): - wav = wav.detach().cpu().numpy() - wav = (wav * (2**15 - 1) * 0.8).astype(np.int16) - # mp3 = AudioSegment( - # wav.tobytes(), - # sample_width=16 // 8, # Sample width in bytes - # frame_rate=22050, - # channels=1, - # ) - # mp3_buffer = BytesIO() - # mp3.export(mp3_buffer, format="mp3", bitrate="192k") - # # we should return this to web page. - # mp3_bytes_data = mp3_buffer.getvalue() - mp3_data = self.mp3_encoder.encode(wav.tobytes()) - if is_last: - mp3_data += self.mp3_encoder.flush() - import lameenc - - self.mp3_encoder = lameenc.Encoder() - self.mp3_encoder.set_bit_rate(128) - self.mp3_encoder.set_in_sample_rate(22050) - self.mp3_encoder.set_channels(1) - self.mp3_encoder.set_quality(2) - - return mp3_data - - def simulate_streaming_generate_speech( - self, preds, llm_cur_kv_cache, llm_cur_kv_cache_len, llm_dtype, llm_tokenizer - ): - # self.tts_text_tokenizer = self.tts_text_tokenizer - self.vocoder.to(llm_dtype) - self.tts_model.to(llm_dtype) - llm_token_num_per_call = 3 - text_chunk_size = 8 - given_rtf = 0.5 - - token_list, feat_list, wav_list, mp3_list = [], [], [], [] - prompt_token, prompt_audio = [None, None], [None, None] - new_text, last_t_size, chunk_idx = "", 0, 0 - st, count = 0, 0 - while st < preds.shape[1]: - chunk_size = int(llm_token_num_per_call / (given_rtf ** min(count, 2))) - _resp = llm_tokenizer.batch_decode( - preds[:, st : st + chunk_size], - add_special_tokens=False, - skip_special_tokens=True, - )[0] - is_last = st + chunk_size >= preds.shape[1] - - # new_text = new_text + _resp - rt_value, states = self.generate_speech_one_step( - new_text, - _resp, - last_t_size, - llm_cur_kv_cache, - llm_cur_kv_cache_len, - prompt_token, - prompt_audio, - text_chunk_size, - chunk_idx, - is_last, - ) - cur_token, feat, wav = rt_value - new_text, last_t_size, prompt_token, prompt_audio, chunk_idx = states - # save results - if cur_token is not None and feat is not None and wav is not None: - token_list.append(cur_token) - feat_list.append(feat) - # we should return this data to web page for playing. - mp3_data = self.convert_wav_to_mp3(wav, is_last) - wav_list.append(wav) - mp3_list.append(mp3_data) - - st += chunk_size - count += 1 - - speech_tokens = torch.cat(token_list, dim=1) - mel_feats = torch.cat(feat_list, dim=2) - wav = torch.cat(wav_list, dim=1) - mp3 = b"".join(mp3_list) - return speech_tokens, mel_feats, wav, mp3 - - def reset_generate_states(self, states={}): - - if states is None: - states = {} - states["new_text"] = "" - states["last_t_size"] = 0 - states["prompt_token"] = [None, None] - states["prompt_audio"] = [None, None] - states["chunk_idx"] = 0 - - def streaming_generate_speech( - self, - preds, - states, - llm_cur_kv_cache, - llm_cur_kv_cache_len, - is_last=False, - text_chunk_size=8, - format="mp3", - ): - - new_text, last_t_size, prompt_token, prompt_audio, chunk_idx = ( - states["new_text"], - states["last_t_size"], - states["prompt_token"], - states["prompt_audio"], - states["chunk_idx"], - ) - # new_text = new_text + preds - with torch.cuda.amp.autocast(enabled=False, dtype=torch.float32): - rt_value, states_ret = self.generate_speech_one_step( - new_text, - preds, - last_t_size, - llm_cur_kv_cache, - llm_cur_kv_cache_len, - prompt_token, - prompt_audio, - text_chunk_size, - chunk_idx, - is_last, - ) - cur_token, feat, wav = rt_value - new_text, last_t_size, prompt_token, prompt_audio, chunk_idx = states_ret - states["new_text"] = new_text - states["last_t_size"] = last_t_size - states["prompt_token"] = prompt_token - states["prompt_audio"] = prompt_audio - states["chunk_idx"] = chunk_idx - if format == "mp3": - if cur_token is not None: - wav = self.convert_wav_to_mp3(wav, is_last) - return cur_token, feat, wav - - def simple_streaming_generate_speech( - self, - preds, - states, - llm_cur_kv_cache, - llm_cur_kv_cache_len, - is_last=False, - text_chunk_size=8, - format="mp3", - ): - - new_text, last_t_size, prompt_token, prompt_audio, chunk_idx = ( - states["new_text"], - states["last_t_size"], - states["prompt_token"], - states["prompt_audio"], - states["chunk_idx"], - ) - # new_text = new_text + preds - with torch.cuda.amp.autocast(enabled=False, dtype=torch.float32): - rt_value, states_ret = self.simple_generate_speech_one_step( - preds, - "", - last_t_size, - llm_cur_kv_cache, - llm_cur_kv_cache_len, - prompt_token, - prompt_audio, - text_chunk_size, - chunk_idx, - is_last, - ) - cur_token, feat, wav = rt_value - new_text, last_t_size, prompt_token, prompt_audio, chunk_idx = states_ret - states["new_text"] = new_text - states["last_t_size"] = last_t_size - states["prompt_token"] = prompt_token - states["prompt_audio"] = prompt_audio - states["chunk_idx"] = chunk_idx - if format == "mp3": - if cur_token is not None: - wav = self.convert_wav_to_mp3(wav, is_last) - return cur_token, feat, wav - - def write_mel_wav(self, output_dir, feat, wav, mp3, key): - out_dir = os.path.join(output_dir, "1best_recog", "mels") - os.makedirs(out_dir, exist_ok=True) - if feat is not None: - feat = feat.cpu().numpy()[0] - np.save(os.path.join(out_dir, f"{key}.npy"), feat) - - out_dir = os.path.join(output_dir, "1best_recog", "wavs") - os.makedirs(out_dir, exist_ok=True) - if wav is not None: - path = os.path.join(out_dir, f"{key}.wav") - torchaudio.save( - path, - wav.cpu(), - sample_rate=self.vocoder.sample_rate, - encoding="PCM_S", - bits_per_sample=16, - ) - if mp3 is not None: - path = os.path.join(out_dir, f"{key}.mp3") - fd = open(path, "wb") - fd.write(mp3) - fd.close() - - class Swish(torch.nn.Module): """Construct an Swish object.""" @@ -3406,4084 +1657,3 @@ class LayerNorm(nn.LayerNorm): self.eps, ) return output.type_as(input) - - -@tables.register("model_classes", "LLMASR5") -class LLMASR5(nn.Module): - """ """ - - def __init__( - self, - audio_encoder: str = None, - audio_encoder_conf: dict = None, - audio_adaptor: str = None, - audio_adaptor_conf: dict = None, - llm: str = None, - llm_conf: dict = None, - input_size: int = 80, - lsm_weight: float = 0.0, - length_normalized_loss: bool = False, - audio_decoder: str = None, - audio_decoder_conf: dict = None, - **kwargs, - ): - - super().__init__() - - # audio encoder - hub = audio_encoder_conf.get("hub", None) - if hub == "ms": - from funasr import AutoModel - - model = AutoModel(model=audio_encoder, model_revision="master") - # frontend = model.kwargs.get("frontend") - audio_encoder_output_size = model.model.encoder_output_size - - audio_encoder = ( - model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder - ) - - # self.frontend = frontend - - elif hub == "hf": - pass - else: - encoder_class = tables.encoder_classes.get(audio_encoder) - audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf) - audio_encoder_output_size = audio_encoder.output_size() - freeze = audio_encoder_conf.get("freeze", True) - freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1)) - # if freeze_layer_num > 0: - # freeze_layer_num = range(freeze_layer_num) - - if freeze: - for name, param in audio_encoder.named_parameters(): - if freeze_layer_num > 0: - idx = re.search(r"\.\d+\.", name) - if idx is not None: - beg, end = idx.regs[0] - layer_id = int(name[beg + 1 : end - 1]) - if layer_id < freeze_layer_num: - param.requires_grad = False - elif "ln_post." not in name: - param.requires_grad = False - else: - param.requires_grad = False - - audio_encoder.eval() - - self.audio_encoder = audio_encoder - - # llm - self.llm = None - - from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig - - init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5") - - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map=None, - use_cache=None, - output_hidden_states=True, - ) - freeze = llm_conf.get("freeze", True) - if freeze: - for name, param in model.named_parameters(): - param.requires_grad = False - model.eval() - self.llm_dtype = llm_conf.get("llm_dtype", "fp32") - self.llm = model.to(dtype_map[self.llm_dtype]) - llm_dim = model.get_input_embeddings().weight.shape[-1] - - # adaptor - adaptor_class = tables.adaptor_classes.get(audio_adaptor) - audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size - audio_adaptor_conf["llm_dim"] = llm_dim - audio_adaptor = adaptor_class(**audio_adaptor_conf) - init_param_path = audio_adaptor_conf.get("init_param_path", None) - if init_param_path is not None: - src_state = torch.load(init_param_path, map_location="cpu") - flag = audio_adaptor.load_state_dict(src_state, strict=False) - logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}") - - self.audio_adaptor = audio_adaptor - - self.error_calculator = None - - self.length_normalized_loss = length_normalized_loss - self.beam_search = None - - self.eos = kwargs.get("eos", 151645) - - # audio decoder related - - self.codebook_dim = audio_decoder_conf.get("codebook_dim", 1024) - self.codebook_size = audio_decoder_conf.get("codebook_size", 4096) - self.lm_out_voc_size = self.codebook_size + 1 - self.audio_decoder = self.build_audio_decoder(name=audio_decoder, conf=audio_decoder_conf) - self.concat_emb_hidden = audio_decoder_conf.get("concat_emb_hidden", False) - self.concat_emb_hidden_norm = audio_decoder_conf.get("concat_emb_hidden_norm", False) - if self.concat_emb_hidden_norm: - self.hidden_norm = LayerNorm(llm_dim) - self.fusion_dropout = nn.Dropout(audio_decoder_conf.get("fusion_drop_rate", 0.0)) - self.emb_norm = LayerNorm(llm_dim) - self.fusion_norm = LayerNorm(self.audio_decoder.embed_unit) - self.fusion_act = Swish() - audio_decoder_in_proj_dim = llm_dim * 2 if self.concat_emb_hidden else llm_dim - self.audio_decoder_in_proj = torch.nn.Linear( - audio_decoder_in_proj_dim, self.audio_decoder.embed_unit - ) - self.codec_embedder = torch.nn.Embedding(self.codebook_size, self.codebook_dim) - self.audio_decoder_embedding = torch.nn.Embedding(2, self.audio_decoder.embed_unit) - self.ad_sos_eos = 0 - self.ad_task_id = 1 - self.ad_ignore_id = -1 - self.predict_nq = 1 - - from .label_smoothing_loss import LabelSmoothingLoss - - self.criterion_ce = LabelSmoothingLoss( - size=self.lm_out_voc_size // self.predict_nq, - padding_idx=self.ad_ignore_id, - smoothing=lsm_weight, - normalize_length=length_normalized_loss, - reduction=False, - ) - - mel_decoder_name = kwargs.get("mel_decoder", None) - mel_decoder_conf = kwargs.get("mel_decoder_conf", None) - self.mel_decoder = self.build_mel_decoder(name=mel_decoder_name, conf=mel_decoder_conf) - vocoder_name = kwargs.get("vocoder", None) - vocoder_conf = kwargs.get("vocoder_conf", None) - self.vocoder = self.build_vocoder(name=vocoder_name, conf=vocoder_conf) - - def build_mel_decoder(self, name: str, conf: dict): - if name is None or conf is None: - return None - if name == "MaskedDiffWithXvec": - from funasr.models.llm_asr.flow_matching import MaskedDiffWithXvec - - return MaskedDiffWithXvec(**conf) - return None - - def build_vocoder(self, name: str, conf: dict): - if name is None or conf is None: - return None - if name == "HifiGAN": - from funasr.models.llm_asr.hifigan import HifiGan - - return HifiGan(**conf) - return None - - def build_audio_decoder(self, name, conf): - if name == "transformer": - from funasr.models.llm_asr.transformer_lm import TransformerEmbedLM - - if "text_vocab_size" in conf: - lm_model = TransformerEmbedLM(vocab_size=self.lm_out_voc_size, **conf) - else: - lm_model = TransformerEmbedLM( - vocab_size=self.lm_out_voc_size, text_vocab_size=self.lm_out_voc_size, **conf - ) - else: - raise TypeError(f"Unknown codec decoder type {name}") - - return lm_model - - def calc_dense_vector(self, codec, codec_lengths): - """ - Args: - codec: (B, T, Nq) - codec_lengths: (B, ) - """ - mask = codec != self.ad_ignore_id - return self.codec_embedder(codec * mask).sum(dim=-2) * mask - - def prepare_audio_decoder_io( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - codec: Optional[torch.Tensor] = None, - codec_lengths: Optional[torch.Tensor] = None, - need_targets: bool = True, - ): - """build inputs and targets for language model - - Normally, this function is called in batchify_nll. - Args: - text: (Batch, Length, Dim) - text_lengths: (Batch,) - codec: (Batch, Length) - codec_lengths: (Batch,) - need_targets: bool, whether provide targets - """ - - if need_targets: - assert ( - codec is not None and codec_lengths is not None - ), "need_target=True, but codec or codec_length is None" - - sos_eos_emb = self.audio_decoder_embedding( - torch.tensor([self.ad_sos_eos], dtype=torch.int64, device=text.device) - ) - task_id_emb = self.audio_decoder_embedding( - torch.tensor([self.ad_task_id], dtype=torch.int64, device=text.device) - ) - codec_emb = None - if codec is not None and codec_lengths is not None: - codec_emb = self.calc_dense_vector(codec, codec_lengths) - inputs_list = [] - for i, text_len in enumerate(text_lengths): - one_input = [sos_eos_emb, text[i, :text_len], task_id_emb] - if codec_emb is not None: - one_input.append(codec_emb[i, : codec_lengths[i]]) - inputs_list.append(torch.cat(one_input, dim=0)) - llm_inputs = pad_list(inputs_list, 0.0) - llm_lengths = text_lengths + 2 - if codec_emb is not None: - llm_lengths = llm_lengths + codec_lengths - - if not need_targets: - return llm_inputs, llm_lengths - - bb, tt = text.shape[0], codec_lengths.max() + 1 - llm_targets = -1 * torch.ones( - [bb, tt, self.predict_nq], dtype=torch.int64, device=text.device - ) - for i, codec_len in enumerate(codec_lengths): - llm_targets[i, :codec_len] = codec[i, :codec_len] - llm_targets[i, codec_len] = self.codebook_size + self.ad_sos_eos - - return (llm_inputs, llm_targets), (llm_lengths, codec_lengths + 1) - - def nll( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - codec: Optional[torch.Tensor] = None, - codec_lengths: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute negative log likelihood(nll) - - Normally, this function is called in batchify_nll. - Args: - text: (Batch, Length, Dim) - text_lengths: (Batch,) - codec: (Batch, Length) - codec_lengths: (Batch,) - """ - batch_size = text.size(0) - # For data parallel - text = text[:, : text_lengths.max()] - codec = codec[:, : codec_lengths.max()] - # text = self.audio_decoder_in_proj(text) - - # build inputs and targets for language model - with autocast(False): - (sequence, target), (x_lengths, y_lengths) = self.prepare_audio_decoder_io( - text, text_lengths, codec, codec_lengths, need_targets=True - ) - - # 2a. Forward Language model - # x: (Batch, Length) -> y: (Batch, Length, NVocab) - sequence = sequence[:, : x_lengths.max()] - target = target[:, : y_lengths.max()] - y, _ = self.audio_decoder(sequence, x_lengths, text_lengths + 1) - bb, tt = y.shape[0], y.shape[1] - y = y.reshape(bb, tt, self.predict_nq, -1) - # 2b. Extract real logits - logits_list = [] - for i, (text_len, codec_len) in enumerate(zip(text_lengths, codec_lengths)): - logits_list.append(y[i, text_len + 1 : text_len + 2 + codec_len]) - logits = pad_list(logits_list, 0.0) - - # 3. Calc negative log likelihood - tt = logits.shape[1] - nll = self.criterion_ce( - logits.reshape(bb, tt * self.predict_nq, -1), target.reshape(bb, tt * self.predict_nq) - ) - nll = nll.sum(-1) - # nll: (BxL,) -> (BxL,) - nll.masked_fill_(make_pad_mask(y_lengths * self.predict_nq).to(nll.device).view(-1), 0.0) - # nll: (BxL,) -> (B, L) - nll = nll.reshape(batch_size, -1).reshape(batch_size, tt, self.predict_nq) - - return nll, logits, target, codec_lengths + 1 - - def forward( - self, - speech: torch.Tensor = None, - speech_lengths: torch.Tensor = None, - input_ids: torch.Tensor = None, - attention_mask: torch.Tensor = None, - labels_ids: torch.Tensor = None, - fbank_beg: torch.Tensor = None, - fbank_mask: torch.Tensor = None, - **kwargs, - ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: - """Encoder + Decoder + Calc loss - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - # import pdb - # - # pdb.set_trace() - stats = {} - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - batch_size, token_num, dims = inputs_embeds.shape - if speech is not None: - if len(speech_lengths.size()) > 1: - speech_lengths = speech_lengths[:, 0] - - batch_size_speech, frames, _ = speech.shape - - with torch.cuda.amp.autocast(enabled=False): - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - fake_token_len = kwargs.get("fake_token_len") - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - - stats["batch_size_speech"] = batch_size_speech - stats["batch_size_x_frames"] = frames * batch_size_speech - stats["batch_size_real_frames"] = speech_lengths.sum().item() - stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] - - with torch.cuda.amp.autocast( - enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype] - ): - labels_ids[labels_ids == -1] = -100 - attention_mask[attention_mask < 0] = 0 - model_outputs = self.llm( - inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]), - attention_mask=attention_mask, - labels=labels_ids, - ) - loss = model_outputs.loss - - codec = kwargs.get("codec") - # codec_len = kwargs.get("codec_len") - # if len(codec_len.size()) > 1: - # codec_len = codec_len[:, 0] - codec_len = (codec > 0).sum(-1) - hidden_states = model_outputs.hidden_states[-1].float() - - target_ids = [] - target_ids_len = [] - hidden_states_select = [] - for batch_idx in range(labels_ids.shape[0]): - beg_i = 0 - end_i = 0 - for token_idx in range(labels_ids.shape[1]): - token_int = labels_ids[batch_idx, token_idx].item() - if token_int == self.eos: - target_ids_i = labels_ids[batch_idx, beg_i:end_i] - target_ids_len_i = end_i - beg_i - target_ids_len.append(target_ids_len_i) - target_ids.append(target_ids_i) - hidden_states_i = hidden_states[batch_idx, beg_i - 1 : end_i - 1, :] - hidden_states_select.append(hidden_states_i) - end_i += 1 - beg_i = end_i - continue - - end_i += 1 - if token_int <= 0: - beg_i += 1 - - target_ids = torch.nn.utils.rnn.pad_sequence( - target_ids, batch_first=True, padding_value=-100 - ) - hidden_states_select = torch.nn.utils.rnn.pad_sequence( - hidden_states_select, batch_first=True, padding_value=0.0 - ) - target_ids_len = torch.tensor(target_ids_len, dtype=torch.int32, device=input_ids.device) - target_ids = target_ids.to(device=input_ids.device) - target_ids[target_ids < 0] = 0 - target_emb = self.llm.model.get_input_embeddings()(target_ids) - hidden_states_select = hidden_states_select.to(device=input_ids.device) - if self.concat_emb_hidden: - if not self.concat_emb_hidden_norm: - hidden_states_select = torch.concat((hidden_states_select, target_emb), dim=-1) - hidden_states_select = self.audio_decoder_in_proj(hidden_states_select) - else: - outs = self.hidden_norm(hidden_states_select) - outs = self.fusion_dropout(self.fusion_act(outs)) - # emb = model_outputs.hidden_states[0] - emb = self.fusion_dropout(self.fusion_act(self.emb_norm(target_emb))) - outs = self.audio_decoder_in_proj(torch.cat([outs, emb], dim=-1)) - hidden_states_select = self.fusion_act(self.fusion_norm(outs)) - - nll, logits, target, target_lengths = self.nll( - hidden_states_select, target_ids_len, codec[:, :, None], codec_len - ) - output_mask = ( - ~make_pad_mask(target_lengths, maxlen=target_lengths.max()) - .to(hidden_states_select.device) - .unsqueeze(-1) - ) - total, batch_size = output_mask.sum() * self.predict_nq, nll.shape[0] * self.predict_nq - denom = total if self.length_normalized_loss else batch_size - loss = (nll * output_mask).sum() / denom - - with torch.no_grad(): - preds = torch.argmax(model_outputs.logits, -1) - acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100) - stats["acc"] = acc_att - - cc = logits.shape[-1] - for i in range(self.predict_nq): - acc = th_accuracy( - logits[:, :, i, :].reshape(-1, cc), target[:, :, i], self.ad_ignore_id - ) - stats[f"codec_acc_{i + 1}"] = acc - - stats["loss"] = torch.clone(loss.detach()) - stats["batch_size"] = batch_size - stats["batch_size_x_tokens"] = token_num * batch_size - stats["batch_size_real_tokens"] = attention_mask.sum().item() - stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] - - dialog_turns = (fbank_beg > 0).sum(-1) - dialog_turns_max = torch.max(dialog_turns).int().item() - dialog_turns_avg = dialog_turns.sum().item() / batch_size - stats["dialog_turns_max"] = dialog_turns_max - stats["dialog_turns_avg"] = dialog_turns_avg - - # force_gatherable: to-device and to-tensor if scalar for DataParallel - if self.length_normalized_loss: - batch_size = int((labels_ids > 0 + 1).sum()) - loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) - return loss, stats, weight - - def encode(self, speech, speech_lengths): - # audio encoder - encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) - - return encoder_out, encoder_out_lens - - def data_template(self, data): - system, user, assistant = [], [], [] - for i, item in enumerate(data): - role = item["role"] - content = item["content"] - if role == "system": - system.append(content) - elif role == "user": - if "audio" in item: - audio = item["audio"] - content = [content, audio] - user.append(content) - elif role == "assistant": - assistant.append(content) - - system = system * len(user) - - contents = { - "system": system, - "user": user, - "assistant": assistant, - } - - return contents - - def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs): - - system = contents["system"] - user = contents["user"] - assistant = contents["assistant"] - pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") - - input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = ( - [], - [], - [], - [], - [], - [], - [], - ) - input_source_ids = [] - for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)): - if i >= kwargs.get("multiturn_num_max", 5): - break - if len(input_ids) > kwargs.get("max_token_length", 1500): - break - - if isinstance(user_prompt, (list, tuple)): - user_prompt, audio = user_prompt - if i == 0: - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - else: - source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - - splits = pattern.split(source_input) - source_ids = [] - fbank_i = [] - fbank_mask_i = [] - fake_token_len_i = 0 - fbank_beg_i = -1 - fbank_lens_i = [] - speech, speech_lengths = [], [] - for k, sub_str in enumerate(splits): - if not sub_str.startswith("<|startofspeech|>"): - sub_token = tokenizer.encode(sub_str) - source_ids += sub_token - fbank_mask_i += [0] * len(sub_token) - else: - sub_str = sub_str.replace("<|startofspeech|>", "").replace( - "<|endofspeech|>", "" - ) - if sub_str.startswith("!"): - sub_str = sub_str[1:] - if sub_str.startswith("!"): # !!: audio sample point - sub_str = audio - try: - time1 = time.perf_counter() - data_src = load_audio_text_image_video(sub_str, fs=frontend.fs) - time2 = time.perf_counter() - meta_data["load_data"] = f"{time2 - time1:0.3f}" - except Exception as e: - logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}") - - speech, speech_lengths = extract_fbank( - data_src, - data_type=kwargs.get("data_type", "sound"), - frontend=frontend, - is_final=True, - ) # speech: [b, T, d] - - time3 = time.perf_counter() - meta_data["extract_feat"] = f"{time3 - time2:0.3f}" - meta_data["batch_data_time"] = ( - speech_lengths.sum().item() - * frontend.frame_shift - * frontend.lfr_n - / 1000 - ) - - if kwargs.get("permute", True): - speech = speech.permute(0, 2, 1) - if speech_lengths > kwargs.get("max_source_length", 5500): - # logging.info( - # f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}" - # ) - badcase_flag = True - - olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2 - olens = 1 + (olens - 3 + 2 * 1) // 2 - fake_token_len_i = (olens - 1) // 2 + 1 - fake_token = [0] * fake_token_len_i - fbank_beg_i = len(source_ids) - source_ids += fake_token - fbank_mask_i += [1] * len(fake_token) - - fbank_beg += [fbank_beg_i + len(input_ids)] - fake_token_len += [fake_token_len_i] - source_mask = [-100] * len(source_ids) - splits = pattern.split(target_out) - for k, sub_str in enumerate(splits): - if len(sub_str) < 1: - continue - if not sub_str.startswith("<|startofspeech|>"): - sub_str = f"{sub_str}<|im_end|>" - sub_token = tokenizer.encode(sub_str) - target_ids = sub_token - # target_out = f"{target_out}<|im_end|>" - # target_ids = tokenizer.encode(target_out) - input_source_ids = input_ids + source_ids - input_ids += source_ids + target_ids - labels += source_mask + target_ids - fbank_mask += fbank_mask_i - if len(speech) > 0: - fbank.append(speech[0, :, :]) - fbank_lens.append(speech_lengths) - - input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] - attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32) - labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] - - # fbank = speech[0, :, :] - # fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32) - fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32) - fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32) - fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32) - source_ids = torch.tensor(input_source_ids, dtype=torch.int64) - target_ids = torch.tensor(target_ids, dtype=torch.int64) - - if len(fbank) > 0: - speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0) - speech_lengths = torch.nn.utils.rnn.pad_sequence( - fbank_lens, batch_first=True, padding_value=-1 - ) - else: - speech = [] - speech_lengths = [] - output = { - "speech": speech, - "speech_lengths": speech_lengths, - "fbank_mask": fbank_mask[None, :], - "fbank_beg": fbank_beg[None,], - "fake_token_len": fake_token_len[None, :], - "input_ids": input_ids[None,], - "attention_mask": attention_mask[None,], - "labels_ids": labels, - "source_ids": source_ids[None, :], - "target_ids": target_ids[None, :], - } - - return output - - def inference_prepare( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - meta_data = {} - prompt = kwargs.get("prompt", None) - - if kwargs.get("batch_size", 1) > 1: - raise NotImplementedError("batch decoding is not implemented") - - contents = self.data_template(data_in[0]) - output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs) - batch = to_device(output, kwargs["device"]) - - # audio encoder - speech = batch["speech"] - if len(speech) > 0: - speech_lengths = batch["speech_lengths"][:, 0] - # fp16 - if kwargs.get("fp16", False): - speech = speech.to(torch.float16) - elif kwargs.get("bf16", False): - speech = speech.to(torch.bfloat16) - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - input_ids = batch["input_ids"] - source_ids = batch["source_ids"] - fbank_beg = batch["fbank_beg"] - fake_token_len = batch["fake_token_len"] - - if not kwargs.get("tearchforing", False): - input_ids = source_ids - - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - batch_size, token_num, dims = inputs_embeds.shape - - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - return inputs_embeds, contents, batch, source_ids, meta_data - - def inference( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare( - data_in, data_lengths, key, tokenizer, frontend, **kwargs - ) - rand_seed = kwargs.get("rand_seed", 0) - - llm_dtype = kwargs.get("llm_dtype", "fp32") - if llm_dtype == "fp32": - llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype - llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype - - with torch.cuda.amp.autocast( - enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] - ): - label = contents["assistant"][-1] - self.llm = self.llm.to(dtype_map[llm_dtype]) - inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) - - # set random seed for reproduce - set_all_random_seed(rand_seed) - generated_ids = self.llm.generate( - inputs_embeds=inputs_embeds, - max_new_tokens=kwargs.get("max_length", 512), - output_hidden_states=True, - return_dict_in_generate=True, - output_scores=True, - ) - hidden_states = generated_ids[ - "hidden_states" - ] # hidden_states: (t1, t2, ..., tn, ..., tN), tn=(l1, l2, ..., ln, ..., lN), ln: shape: 1x1x3584 - - token_num = len(hidden_states) - hidden_states_select = torch.zeros((1, token_num, 3584), dtype=torch.float32).to( - inputs_embeds.device - ) - hidden_states_out_len = torch.tensor( - [ - token_num, - ], - dtype=torch.int32, - ).to(inputs_embeds.device) - for i in range(token_num): - hidden_states_select[0, i, :] = hidden_states[i][-1][0, 0, :].to(torch.float32) - - target_ids = generated_ids["sequences"] - target_emb = self.llm.model.get_input_embeddings()(target_ids) - if self.concat_emb_hidden: - if not self.concat_emb_hidden_norm: - hidden_states_select = torch.concat((hidden_states_select, target_emb), dim=-1) - hidden_states_select = self.audio_decoder_in_proj(hidden_states_select) - else: - outs = self.hidden_norm(hidden_states_select) - outs = self.fusion_dropout(self.fusion_act(outs)) - # emb = model_outputs.hidden_states[0] - emb = self.fusion_dropout(self.fusion_act(self.emb_norm(target_emb))) - outs = self.audio_decoder_in_proj(torch.cat([outs, emb], dim=-1)) - hidden_states_select = self.fusion_act(self.fusion_norm(outs)) - - # set random seed for reproduce - set_all_random_seed(rand_seed) - speech_tokens = self.audio_decode(hidden_states_select, hidden_states_out_len)[ - :, :, 0 - ] # 1xlx1: 2,10,1023 - - # generated_ids = [ - # output_ids[len(input_id) :] - # for input_id, output_ids in zip(input_ids, generated_ids) - # ] - response = tokenizer.batch_decode( - target_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) - )[0] - - loss = None - - # synthesize waveforms - spk_emb = kwargs.get("spk_emb", None) - feat, wav = self.synthesize_waveform(speech_tokens, spk_emb, inputs_embeds.device) - - ibest_writer = None - if kwargs.get("output_dir") is not None: - if not hasattr(self, "writer"): - self.writer = DatadirWriter(kwargs.get("output_dir")) - ibest_writer = self.writer[f"{0 + 1}best_recog"] - - self.write_mel_wav(kwargs.get("output_dir"), feat, wav, key[0]) - - results = [] - response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response) - result_i = { - "key": key[0], - "text": response, - "text_tn": response_clean, - "label": label, - "speech_tokens": speech_tokens, - "wav": wav, - } - if loss is not None: - result_i["loss"] = loss - results.append(result_i) - - speech_tokens_out = "<|startofspeech|>" - for i in range(speech_tokens.shape[-1]): - tmp = speech_tokens[0, i].item() - speech_tokens_out += f"<|c{tmp}|>" - speech_tokens_out += "<|endofspeech|><|im_end|>" - if ibest_writer is not None: - ibest_writer["text"][key[0]] = response.replace("\n", " ") - ibest_writer["label"][key[0]] = label.replace("\n", " ") - ibest_writer["text_tn"][key[0]] = response_clean - ibest_writer["speech_tokens"][key[0]] = speech_tokens_out - - return results, meta_data - - def write_mel_wav(self, output_dir, feat, wav, key): - out_dir = os.path.join(output_dir, "1best_recog", "mels") - os.makedirs(out_dir, exist_ok=True) - if feat is not None: - feat = feat.cpu().numpy()[0] - np.save(os.path.join(out_dir, f"{key}.npy"), feat) - - out_dir = os.path.join(output_dir, "1best_recog", "wavs") - os.makedirs(out_dir, exist_ok=True) - if wav is not None: - path = os.path.join(out_dir, f"{key}.wav") - torchaudio.save( - path, - wav.cpu(), - sample_rate=self.vocoder.sample_rate, - encoding="PCM_S", - bits_per_sample=16, - ) - - def synthesize_waveform(self, speech_tokens, spk_emb, device): - mel_feat, wav = None, None - if self.mel_decoder is not None and spk_emb is not None: - # mel_feat in BxCxT - mel_feat = self.token2mel(speech_tokens, spk_emb, device) - if self.vocoder is not None: - wav = self.vocoder.inference(mel_feat.transpose(1, 2)) - - return mel_feat, wav - - def token2mel(self, tokens: torch.Tensor, xvec: torch.Tensor, device: torch.device): - xvec = torch.tensor(xvec).to(device).unsqueeze(0) - xvec_lens = torch.tensor([xvec.shape[1]], device=device, dtype=torch.int64) - token_lens = torch.tensor([tokens.shape[1]], device=device, dtype=torch.int64) - feat = self.mel_decoder.inference( - tokens, - token_lens, - xvec, - xvec_lens, - diff_steps=10, - temperature=1.0, - prompt=dict(prompt_text=(None, None), prompt_audio=(None, None)), - ) - return feat - - def audio_decode( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - min_length=None, - max_length: int = 30 * 25, - infer_cfg_ratio=None, - decoding_length=None, - ): - # 1. encode text - # text = self.audio_decoder_in_proj(text) - device = text.device - sos_eos_emb = self.audio_decoder_embedding( - torch.tensor([[self.ad_sos_eos]], dtype=torch.int64, device=device) - ) - task_id_emb = self.audio_decoder_embedding( - torch.tensor([[self.ad_task_id]], dtype=torch.int64, device=device) - ) - prompt = torch.cat([sos_eos_emb, text, task_id_emb], dim=1) - seq_input = torch.zeros( - [1, prompt.shape[1] + max_length, prompt.shape[2]], dtype=torch.float32, device=device - ) - seq_input[:, : prompt.shape[1], :] = prompt - out_tokens = torch.zeros([1, max_length, 1], dtype=torch.int64, device=device) - out_token_len = 0 - prompt_len = prompt.shape[1] - state, hit_eos = None, False - for i in range(max_length): - # use state for speedup - pred, (state, _) = self.audio_decoder.score( - seq_input[0, : prompt_len + out_token_len], state, prompt[0] - ) - - # sampling all `nq` token ids - pred = pred.reshape(self.predict_nq, -1) - # normalize scores - pred = torch.log_softmax(pred, dim=-1) - if min_length is not None and i < min_length: - pred[:, self.codebook_size + self.ad_sos_eos] = float(np.finfo(np.float32).min) - top_ids = self.ras_sampling(pred[0], out_tokens[0, :out_token_len, 0]) - - if torch.any(top_ids == (self.codebook_size + self.ad_sos_eos)): - hit_eos = True - out_tokens = out_tokens[:, :out_token_len, :] - break - - out_tokens[0, out_token_len, 0] = top_ids[0] - seq_input[0, prompt_len + out_token_len, :] = self.codec_embedder(top_ids)[0] - out_token_len += 1 - - if decoding_length is None: - return out_tokens - else: - return out_tokens, hit_eos - - # Repetition Aware Sampling in VALL-E 2 - def ras_sampling( - self, weighted_scores, decoded_tokens, *, top_p=0.8, top_k=25, win_size=10, tau_r=0.1 - ): - top_ids = self.nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k) - rep_num = torch.sum(decoded_tokens[-win_size:] == top_ids).item() - if rep_num >= win_size * tau_r: - top_ids = self.random_sampling(weighted_scores) - - return top_ids - - def nucleus_sampling(self, weighted_scores, top_p=0.8, top_k=25): - cum_prob = 0.0 - sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True) - i = len(sorted_idx) - for i in range(len(sorted_idx)): - # sampling both top-p and numbers. - if cum_prob < top_p and i < top_k: - cum_prob += sorted_value[i] - else: - break - prob = sorted_value[:i] - indices = sorted_idx[:i] - sampling_ids = prob.multinomial(1, replacement=True) - top_ids = indices[sampling_ids] - return top_ids - - def random_sampling(self, weighted_scores): - top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True) - return top_ids - - -@tables.register("model_classes", "LLMASR6") -class LLMASR6(nn.Module): - """ """ - - def __init__( - self, - audio_encoder: str = None, - audio_encoder_conf: dict = None, - audio_adaptor: str = None, - audio_adaptor_conf: dict = None, - llm: str = None, - llm_conf: dict = None, - input_size: int = 80, - lsm_weight: float = 0.0, - length_normalized_loss: bool = False, - audio_decoder: str = None, - audio_decoder_conf: dict = None, - **kwargs, - ): - - super().__init__() - - # audio encoder - hub = audio_encoder_conf.get("hub", None) - if hub == "ms": - from funasr import AutoModel - - model = AutoModel(model=audio_encoder, model_revision="master") - # frontend = model.kwargs.get("frontend") - audio_encoder_output_size = model.model.encoder_output_size - - audio_encoder = ( - model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder - ) - - # self.frontend = frontend - - elif hub == "hf": - pass - else: - encoder_class = tables.encoder_classes.get(audio_encoder) - audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf) - audio_encoder_output_size = audio_encoder.output_size() - freeze = audio_encoder_conf.get("freeze", True) - freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1)) - # if freeze_layer_num > 0: - # freeze_layer_num = range(freeze_layer_num) - - if freeze: - for name, param in audio_encoder.named_parameters(): - if freeze_layer_num > 0: - idx = re.search(r"\.\d+\.", name) - if idx is not None: - beg, end = idx.regs[0] - layer_id = int(name[beg + 1 : end - 1]) - if layer_id < freeze_layer_num: - param.requires_grad = False - elif "ln_post." not in name: - param.requires_grad = False - else: - param.requires_grad = False - - audio_encoder.eval() - - self.audio_encoder = audio_encoder - self.audio_encoder_activation_checkpoint = audio_encoder_conf.get( - "activation_checkpoint", False - ) - - # llm - self.llm = None - - from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig - - init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5") - - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map=None, - use_cache=None, - output_hidden_states=True, - ) - - freeze = llm_conf.get("freeze", True) - if freeze: - for name, param in model.named_parameters(): - param.requires_grad = False - model.eval() - if llm_conf.get("activation_checkpoint", False): - model.gradient_checkpointing_enable() - self.llm_dtype = llm_conf.get("llm_dtype", "fp32") - self.llm = model.to(dtype_map[self.llm_dtype]) - llm_dim = model.get_input_embeddings().weight.shape[-1] - self.tokenizer = AutoTokenizer.from_pretrained(init_param_path) - - # adaptor - adaptor_class = tables.adaptor_classes.get(audio_adaptor) - audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size - audio_adaptor_conf["llm_dim"] = llm_dim - audio_adaptor = adaptor_class(**audio_adaptor_conf) - init_param_path = audio_adaptor_conf.get("init_param_path", None) - if init_param_path is not None: - src_state = torch.load(init_param_path, map_location="cpu") - flag = audio_adaptor.load_state_dict(src_state, strict=False) - logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}") - - self.audio_adaptor = audio_adaptor - - self.error_calculator = None - - self.length_normalized_loss = length_normalized_loss - self.beam_search = None - - self.eos = kwargs.get("eos", 151645) - - # tts text tokenizer related - tts_token_type = audio_decoder_conf.get("tts_token_type", "whisper_rich_ttsfrd") - ttsfrd_res_dir = audio_decoder_conf.get("ttsfrd_res_dir", "./ttsfrd/9.5.5") - from funasr.models.llm_asr.tts_text_tokenizer.build_tokenizer import build_tokenizer - - self.tts_text_tokenizer = build_tokenizer( - tts_token_type, - bpemodel=ttsfrd_res_dir, - p_word2phn=1.0, - ) - from funasr.models.llm_asr.tts_models.e2e_model import UCTDXvecSlotModel - - from omegaconf import OmegaConf, DictConfig - - tts_model_conf = kwargs.get("tts_model_conf", {}) - if isinstance(tts_model_conf, DictConfig): - tts_model_conf = OmegaConf.to_container(tts_model_conf, resolve=True) - self.tts_model = UCTDXvecSlotModel(**tts_model_conf) - self.tts_dim_proj = nn.Linear(llm_dim, self.tts_model.output_size) - - # self.codebook_dim = audio_decoder_conf.get("codebook_dim", 1024) - # self.codebook_size = audio_decoder_conf.get("codebook_size", 4096) - # self.lm_out_voc_size = self.codebook_size + 1 - # self.audio_decoder = self.build_audio_decoder(name=audio_decoder, conf=audio_decoder_conf) - # self.concat_emb_hidden = audio_decoder_conf.get("concat_emb_hidden", False) - # self.concat_emb_hidden_norm = audio_decoder_conf.get("concat_emb_hidden_norm", False) - # if self.concat_emb_hidden_norm: - # self.hidden_norm = LayerNorm(llm_dim) - # self.fusion_dropout = nn.Dropout(audio_decoder_conf.get("fusion_drop_rate", 0.0)) - # self.emb_norm = LayerNorm(llm_dim) - # self.fusion_norm = LayerNorm(self.audio_decoder.embed_unit) - # self.fusion_act = Swish() - # audio_decoder_in_proj_dim = llm_dim * 2 if self.concat_emb_hidden else llm_dim - # self.audio_decoder_in_proj = torch.nn.Linear( - # audio_decoder_in_proj_dim, self.audio_decoder.embed_unit - # ) - # self.codec_embedder = torch.nn.Embedding(self.codebook_size, self.codebook_dim) - # self.audio_decoder_embedding = torch.nn.Embedding(2, self.audio_decoder.embed_unit) - # self.ad_sos_eos = 0 - # self.ad_task_id = 1 - # self.ad_ignore_id = -1 - # self.predict_nq = 1 - # - # from .label_smoothing_loss import LabelSmoothingLoss - # - # self.criterion_ce = LabelSmoothingLoss( - # size=self.lm_out_voc_size // self.predict_nq, - # padding_idx=self.ad_ignore_id, - # smoothing=lsm_weight, - # normalize_length=length_normalized_loss, - # reduction=False, - # ) - # - # mel_decoder_name = kwargs.get("mel_decoder", None) - # mel_decoder_conf = kwargs.get("mel_decoder_conf", None) - # self.mel_decoder = self.build_mel_decoder(name=mel_decoder_name, conf=mel_decoder_conf) - vocoder_name = kwargs.get("vocoder", None) - vocoder_conf = kwargs.get("vocoder_conf", None) - self.vocoder = self.build_vocoder(name=vocoder_name, conf=vocoder_conf) - - def build_mel_decoder(self, name: str, conf: dict): - if name is None or conf is None: - return None - if name == "MaskedDiffWithXvec": - from funasr.models.llm_asr.flow_matching import MaskedDiffWithXvec - - return MaskedDiffWithXvec(**conf) - return None - - def build_vocoder(self, name: str, conf: dict): - if name is None or conf is None: - return None - if name == "HifiGAN": - from funasr.models.llm_asr.hifigan import HifiGan - - return HifiGan(**conf) - return None - - def build_audio_decoder(self, name, conf): - if name == "transformer": - from funasr.models.llm_asr.transformer_lm import TransformerEmbedLM - - if "text_vocab_size" in conf: - lm_model = TransformerEmbedLM(vocab_size=self.lm_out_voc_size, **conf) - else: - lm_model = TransformerEmbedLM( - vocab_size=self.lm_out_voc_size, text_vocab_size=self.lm_out_voc_size, **conf - ) - else: - raise TypeError(f"Unknown codec decoder type {name}") - - return lm_model - - def calc_dense_vector(self, codec, codec_lengths): - """ - Args: - codec: (B, T, Nq) - codec_lengths: (B, ) - """ - mask = codec != self.ad_ignore_id - return self.codec_embedder(codec * mask).sum(dim=-2) * mask - - def prepare_audio_decoder_io( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - codec: Optional[torch.Tensor] = None, - codec_lengths: Optional[torch.Tensor] = None, - need_targets: bool = True, - ): - """build inputs and targets for language model - - Normally, this function is called in batchify_nll. - Args: - text: (Batch, Length, Dim) - text_lengths: (Batch,) - codec: (Batch, Length) - codec_lengths: (Batch,) - need_targets: bool, whether provide targets - """ - - if need_targets: - assert ( - codec is not None and codec_lengths is not None - ), "need_target=True, but codec or codec_length is None" - - sos_eos_emb = self.audio_decoder_embedding( - torch.tensor([self.ad_sos_eos], dtype=torch.int64, device=text.device) - ) - task_id_emb = self.audio_decoder_embedding( - torch.tensor([self.ad_task_id], dtype=torch.int64, device=text.device) - ) - codec_emb = None - if codec is not None and codec_lengths is not None: - codec_emb = self.calc_dense_vector(codec, codec_lengths) - inputs_list = [] - for i, text_len in enumerate(text_lengths): - one_input = [sos_eos_emb, text[i, :text_len], task_id_emb] - if codec_emb is not None: - one_input.append(codec_emb[i, : codec_lengths[i]]) - inputs_list.append(torch.cat(one_input, dim=0)) - llm_inputs = pad_list(inputs_list, 0.0) - llm_lengths = text_lengths + 2 - if codec_emb is not None: - llm_lengths = llm_lengths + codec_lengths - - if not need_targets: - return llm_inputs, llm_lengths - - bb, tt = text.shape[0], codec_lengths.max() + 1 - llm_targets = -1 * torch.ones( - [bb, tt, self.predict_nq], dtype=torch.int64, device=text.device - ) - for i, codec_len in enumerate(codec_lengths): - llm_targets[i, :codec_len] = codec[i, :codec_len] - llm_targets[i, codec_len] = self.codebook_size + self.ad_sos_eos - - return (llm_inputs, llm_targets), (llm_lengths, codec_lengths + 1) - - def nll( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - codec: Optional[torch.Tensor] = None, - codec_lengths: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute negative log likelihood(nll) - - Normally, this function is called in batchify_nll. - Args: - text: (Batch, Length, Dim) - text_lengths: (Batch,) - codec: (Batch, Length) - codec_lengths: (Batch,) - """ - batch_size = text.size(0) - # For data parallel - text = text[:, : text_lengths.max()] - codec = codec[:, : codec_lengths.max()] - # text = self.audio_decoder_in_proj(text) - - # build inputs and targets for language model - with autocast(False): - (sequence, target), (x_lengths, y_lengths) = self.prepare_audio_decoder_io( - text, text_lengths, codec, codec_lengths, need_targets=True - ) - - # 2a. Forward Language model - # x: (Batch, Length) -> y: (Batch, Length, NVocab) - sequence = sequence[:, : x_lengths.max()] - target = target[:, : y_lengths.max()] - y, _ = self.audio_decoder(sequence, x_lengths, text_lengths + 1) - bb, tt = y.shape[0], y.shape[1] - y = y.reshape(bb, tt, self.predict_nq, -1) - # 2b. Extract real logits - logits_list = [] - for i, (text_len, codec_len) in enumerate(zip(text_lengths, codec_lengths)): - logits_list.append(y[i, text_len + 1 : text_len + 2 + codec_len]) - logits = pad_list(logits_list, 0.0) - - # 3. Calc negative log likelihood - tt = logits.shape[1] - nll = self.criterion_ce( - logits.reshape(bb, tt * self.predict_nq, -1), target.reshape(bb, tt * self.predict_nq) - ) - nll = nll.sum(-1) - # nll: (BxL,) -> (BxL,) - nll.masked_fill_(make_pad_mask(y_lengths * self.predict_nq).to(nll.device).view(-1), 0.0) - # nll: (BxL,) -> (B, L) - nll = nll.reshape(batch_size, -1).reshape(batch_size, tt, self.predict_nq) - - return nll, logits, target, codec_lengths + 1 - - def forward( - self, - speech: torch.Tensor = None, - speech_lengths: torch.Tensor = None, - input_ids: torch.Tensor = None, - attention_mask: torch.Tensor = None, - labels_ids: torch.Tensor = None, - fbank_beg: torch.Tensor = None, - fbank_mask: torch.Tensor = None, - **kwargs, - ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: - """Encoder + Decoder + Calc loss - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - # import pdb - # - # pdb.set_trace() - stats = {} - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - batch_size, token_num, dims = inputs_embeds.shape - if speech is not None: - if len(speech_lengths.size()) > 1: - speech_lengths = speech_lengths[:, 0] - - batch_size_speech, frames, _ = speech.shape - - # with torch.cuda.amp.autocast(enabled=False): - # audio encoder - if self.audio_encoder_activation_checkpoint: - from torch.utils.checkpoint import checkpoint - - encoder_out, encoder_out_lens = checkpoint( - self.encode, speech, speech_lengths, use_reentrant=False - ) - else: - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - fake_token_len = kwargs.get("fake_token_len") - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - - stats["batch_size_speech"] = batch_size_speech - stats["batch_size_x_frames"] = frames * batch_size_speech - stats["batch_size_real_frames"] = speech_lengths.sum().item() - stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] - - with torch.cuda.amp.autocast( - enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype] - ): - labels_ids[labels_ids == -1] = -100 - attention_mask[attention_mask < 0] = 0 - model_outputs = self.llm( - inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]), - attention_mask=attention_mask, - labels=labels_ids, - ) - loss = model_outputs.loss - - # audio sampling point - audio = kwargs.get("audio") - audio_len = kwargs.get("audio_len") - - # codec - codec = kwargs.get("codec") - codec_len = (codec > 0).sum(-1) - - input_mask = kwargs.get("input_mask") - input_mask[input_mask < 0] = 0 - - hidden_states = model_outputs.hidden_states[-1].float() - hidden_states_his_select = [] - - # target, str - target_ids = [] - target_ids_len = [] - turn_id_cum = 0 - for batch_idx in range(labels_ids.shape[0]): - - try: - for turn_id in range(fbank_beg.shape[1]): - - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - beg = 0 - end = input_mask[turn_id_cum].sum(-1) - # print(f"beg: {beg}, end: {end}") - hidden_states_his_i = hidden_states[batch_idx, beg:end, :] - hidden_states_his_select.append(hidden_states_his_i) - turn_id_cum += 1 - except: - import pdb - - pdb.set_trace() - beg_i = 0 - end_i = 0 - for token_idx in range(labels_ids.shape[1]): - token_int = labels_ids[batch_idx, token_idx].item() - if token_int == self.eos: - target_ids_i = labels_ids[batch_idx, beg_i:end_i] - target_ids_len_i = end_i - beg_i - target_ids_len.append(target_ids_len_i) - target = self.tokenizer.decode(target_ids_i) - target_ids.append(target) - - end_i += 1 - beg_i = end_i - continue - - end_i += 1 - if token_int <= 0: - beg_i += 1 - - # hidden_states_his_select - hidden_states_his_select = torch.nn.utils.rnn.pad_sequence( - hidden_states_his_select, batch_first=True, padding_value=0.0 - ) - hidden_states_his_select = hidden_states_his_select.to(device=input_ids.device) - hidden_states_his_select_len = input_mask.sum(-1) - - # import pdb - # - # pdb.set_trace() - - # if self.concat_emb_hidden: - # if not self.concat_emb_hidden_norm: - # hidden_states_select = torch.concat((hidden_states_select, target_emb), dim=-1) - # hidden_states_select = self.audio_decoder_in_proj(hidden_states_select) - # else: - # outs = self.hidden_norm(hidden_states_select) - # outs = self.fusion_dropout(self.fusion_act(outs)) - # # emb = model_outputs.hidden_states[0] - # emb = self.fusion_dropout(self.fusion_act(self.emb_norm(target_emb))) - # outs = self.audio_decoder_in_proj(torch.cat([outs, emb], dim=-1)) - # hidden_states_select = self.fusion_act(self.fusion_norm(outs)) - # - # nll, logits, target, target_lengths = self.nll( - # hidden_states_select, target_ids_len, codec[:, :, None], codec_len - # ) - # output_mask = ( - # ~make_pad_mask(target_lengths, maxlen=target_lengths.max()) - # .to(hidden_states_select.device) - # .unsqueeze(-1) - # ) - # total, batch_size = output_mask.sum() * self.predict_nq, nll.shape[0] * self.predict_nq - # denom = total if self.length_normalized_loss else batch_size - # loss = (nll * output_mask).sum() / denom - # - # with torch.no_grad(): - # preds = torch.argmax(model_outputs.logits, -1) - # acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100) - # stats["acc"] = acc_att - # - # cc = logits.shape[-1] - # for i in range(self.predict_nq): - # acc = th_accuracy( - # logits[:, :, i, :].reshape(-1, cc), target[:, :, i], self.ad_ignore_id - # ) - # stats[f"codec_acc_{i + 1}"] = acc - - # nar tts model related - # import pdb; pdb.set_trace() - device = hidden_states_his_select.device - text = [ - torch.tensor(self.tts_text_tokenizer.text2tokens(x), dtype=torch.int64).to(device) - for x in target_ids - ] - text_lengths = [len(x) for x in text] - text = pad_list(text, pad_value=-1).long().to(device) - audio_len = torch.tensor(audio_len, dtype=torch.int64).to(device) - text_lengths = torch.tensor(text_lengths, dtype=torch.int64).to(device) - # mute the "da" noise. - # TODO: make sure the sample rate is 22050. - audio[:, : int(0.02 * 22050)] = 0 - hidden_states_his_select = self.tts_dim_proj(hidden_states_his_select) - tts_loss, tts_states, tts_weight = self.tts_model.forward( - text=text, - text_lengths=text_lengths, - speech_token=codec, - speech_token_lengths=codec_len, - audio=audio, - audio_lengths=audio_len, - prompt=hidden_states_his_select, - prompt_len=hidden_states_his_select_len, - ) - loss = loss + tts_loss - for key, value in tts_states.items(): - stats[f"tts_{key}"] = value - - stats["loss"] = torch.clone(loss.detach()) - stats["batch_size"] = batch_size - stats["batch_size_x_tokens"] = token_num * batch_size - stats["batch_size_real_tokens"] = attention_mask.sum().item() - stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] - - dialog_turns = (fbank_beg > 0).sum(-1) - dialog_turns_max = torch.max(dialog_turns).int().item() - dialog_turns_avg = dialog_turns.sum().item() / batch_size - stats["dialog_turns_max"] = dialog_turns_max - stats["dialog_turns_avg"] = dialog_turns_avg - - # force_gatherable: to-device and to-tensor if scalar for DataParallel - if self.length_normalized_loss: - batch_size = int((labels_ids > 0 + 1).sum()) - loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) - return loss, stats, weight - - def encode(self, speech, speech_lengths): - # audio encoder - encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) - - return encoder_out, encoder_out_lens - - def data_template(self, data): - system, user, assistant = [], [], [] - for i, item in enumerate(data): - role = item["role"] - content = item["content"] - if role == "system": - system.append(content) - elif role == "user": - if "audio" in item: - audio = item["audio"] - content = [content, audio] - user.append(content) - elif role == "assistant": - assistant.append(content) - - system = system * len(user) - - contents = { - "system": system, - "user": user, - "assistant": assistant, - } - - return contents - - def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs): - - system = contents["system"] - user = contents["user"] - assistant = contents["assistant"] - pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") - - input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = ( - [], - [], - [], - [], - [], - [], - [], - ) - input_source_ids = [] - for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)): - if i >= kwargs.get("multiturn_num_max", 5): - break - if len(input_ids) > kwargs.get("max_token_length", 1500): - break - - if isinstance(user_prompt, (list, tuple)): - user_prompt, audio = user_prompt - if i == 0: - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - else: - source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - - splits = pattern.split(source_input) - source_ids = [] - fbank_i = [] - fbank_mask_i = [] - fake_token_len_i = 0 - fbank_beg_i = -1 - fbank_lens_i = [] - speech, speech_lengths = [], [] - for k, sub_str in enumerate(splits): - if not sub_str.startswith("<|startofspeech|>"): - sub_token = tokenizer.encode(sub_str) - source_ids += sub_token - fbank_mask_i += [0] * len(sub_token) - else: - sub_str = sub_str.replace("<|startofspeech|>", "").replace( - "<|endofspeech|>", "" - ) - if sub_str.startswith("!"): - sub_str = sub_str[1:] - if sub_str.startswith("!"): # !!: audio sample point - sub_str = audio - try: - time1 = time.perf_counter() - data_src = load_audio_text_image_video(sub_str, fs=frontend.fs) - time2 = time.perf_counter() - meta_data["load_data"] = f"{time2 - time1:0.3f}" - except Exception as e: - logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}") - - speech, speech_lengths = extract_fbank( - data_src, - data_type=kwargs.get("data_type", "sound"), - frontend=frontend, - is_final=True, - ) # speech: [b, T, d] - - time3 = time.perf_counter() - meta_data["extract_feat"] = f"{time3 - time2:0.3f}" - meta_data["batch_data_time"] = ( - speech_lengths.sum().item() - * frontend.frame_shift - * frontend.lfr_n - / 1000 - ) - - if kwargs.get("permute", True): - speech = speech.permute(0, 2, 1) - if speech_lengths > kwargs.get("max_source_length", 5500): - # logging.info( - # f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}" - # ) - badcase_flag = True - - olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2 - olens = 1 + (olens - 3 + 2 * 1) // 2 - fake_token_len_i = (olens - 1) // 2 + 1 - fake_token = [0] * fake_token_len_i - fbank_beg_i = len(source_ids) - source_ids += fake_token - fbank_mask_i += [1] * len(fake_token) - - fbank_beg += [fbank_beg_i + len(input_ids)] - fake_token_len += [fake_token_len_i] - source_mask = [-100] * len(source_ids) - splits = pattern.split(target_out) - for k, sub_str in enumerate(splits): - if len(sub_str) < 1: - continue - if not sub_str.startswith("<|startofspeech|>"): - sub_str = f"{sub_str}<|im_end|>" - sub_token = tokenizer.encode(sub_str) - target_ids = sub_token - # target_out = f"{target_out}<|im_end|>" - # target_ids = tokenizer.encode(target_out) - input_source_ids = input_ids + source_ids - input_ids += source_ids + target_ids - labels += source_mask + target_ids - fbank_mask += fbank_mask_i - if len(speech) > 0: - fbank.append(speech[0, :, :]) - fbank_lens.append(speech_lengths) - - input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] - attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32) - labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] - - # fbank = speech[0, :, :] - # fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32) - fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32) - fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32) - fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32) - source_ids = torch.tensor(input_source_ids, dtype=torch.int64) - target_ids = torch.tensor(target_ids, dtype=torch.int64) - - if len(fbank) > 0: - speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0) - speech_lengths = torch.nn.utils.rnn.pad_sequence( - fbank_lens, batch_first=True, padding_value=-1 - ) - else: - speech = [] - speech_lengths = [] - output = { - "speech": speech, - "speech_lengths": speech_lengths, - "fbank_mask": fbank_mask[None, :], - "fbank_beg": fbank_beg[None,], - "fake_token_len": fake_token_len[None, :], - "input_ids": input_ids[None,], - "attention_mask": attention_mask[None,], - "labels_ids": labels, - "source_ids": source_ids[None, :], - "target_ids": target_ids[None, :], - } - - return output - - def inference_prepare( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - meta_data = {} - prompt = kwargs.get("prompt", None) - - if kwargs.get("batch_size", 1) > 1: - raise NotImplementedError("batch decoding is not implemented") - - contents = self.data_template(data_in[0]) - output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs) - batch = to_device(output, kwargs["device"]) - - # audio encoder - speech = batch["speech"] - if len(speech) > 0: - speech_lengths = batch["speech_lengths"][:, 0] - # fp16 - if kwargs.get("fp16", False): - speech = speech.to(torch.float16) - elif kwargs.get("bf16", False): - speech = speech.to(torch.bfloat16) - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - input_ids = batch["input_ids"] - source_ids = batch["source_ids"] - fbank_beg = batch["fbank_beg"] - fake_token_len = batch["fake_token_len"] - - if not kwargs.get("tearchforing", False): - input_ids = source_ids - - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - batch_size, token_num, dims = inputs_embeds.shape - - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - return inputs_embeds, contents, batch, source_ids, meta_data - - def inference( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare( - data_in, data_lengths, key, tokenizer, frontend, **kwargs - ) - rand_seed = kwargs.get("rand_seed", 0) - - llm_dtype = kwargs.get("llm_dtype", "fp32") - if llm_dtype == "fp32": - llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype - llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype - - with torch.cuda.amp.autocast( - enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] - ): - label = contents["assistant"][-1] - self.llm = self.llm.to(dtype_map[llm_dtype]) - inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) - - # set random seed for reproduce - set_all_random_seed(rand_seed) - generated_ids = self.llm.generate( - inputs_embeds=inputs_embeds, - max_new_tokens=kwargs.get("max_length", 512), - output_hidden_states=True, - return_dict_in_generate=True, - output_scores=True, - ) - hidden_states = generated_ids[ - "hidden_states" - ] # hidden_states: (t1, t2, ..., tn, ..., tN), tn=(l1, l2, ..., ln, ..., lN), ln: shape: 1x1x3584 - - token_num = len(hidden_states) - hidden_states_select = torch.zeros((1, token_num, 3584), dtype=torch.float32).to( - inputs_embeds.device - ) - hidden_states_out_len = torch.tensor( - [ - token_num, - ], - dtype=torch.int32, - ).to(inputs_embeds.device) - for i in range(token_num): - hidden_states_select[0, i, :] = hidden_states[i][-1][0, 0, :].to(torch.float32) - - target_ids = generated_ids["sequences"] - target_emb = self.llm.model.get_input_embeddings()(target_ids) - if self.concat_emb_hidden: - if not self.concat_emb_hidden_norm: - hidden_states_select = torch.concat((hidden_states_select, target_emb), dim=-1) - hidden_states_select = self.audio_decoder_in_proj(hidden_states_select) - else: - outs = self.hidden_norm(hidden_states_select) - outs = self.fusion_dropout(self.fusion_act(outs)) - # emb = model_outputs.hidden_states[0] - emb = self.fusion_dropout(self.fusion_act(self.emb_norm(target_emb))) - outs = self.audio_decoder_in_proj(torch.cat([outs, emb], dim=-1)) - hidden_states_select = self.fusion_act(self.fusion_norm(outs)) - - # set random seed for reproduce - set_all_random_seed(rand_seed) - speech_tokens = self.audio_decode(hidden_states_select, hidden_states_out_len)[ - :, :, 0 - ] # 1xlx1: 2,10,1023 - - # generated_ids = [ - # output_ids[len(input_id) :] - # for input_id, output_ids in zip(input_ids, generated_ids) - # ] - response = tokenizer.batch_decode( - target_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) - )[0] - - loss = None - - # synthesize waveforms - spk_emb = kwargs.get("spk_emb", None) - feat, wav = self.synthesize_waveform(speech_tokens, spk_emb, inputs_embeds.device) - - ibest_writer = None - if kwargs.get("output_dir") is not None: - if not hasattr(self, "writer"): - self.writer = DatadirWriter(kwargs.get("output_dir")) - ibest_writer = self.writer[f"{0 + 1}best_recog"] - - self.write_mel_wav(kwargs.get("output_dir"), feat, wav, key[0]) - - results = [] - response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response) - result_i = { - "key": key[0], - "text": response, - "text_tn": response_clean, - "label": label, - "speech_tokens": speech_tokens, - "wav": wav, - } - if loss is not None: - result_i["loss"] = loss - results.append(result_i) - - speech_tokens_out = "<|startofspeech|>" - for i in range(speech_tokens.shape[-1]): - tmp = speech_tokens[0, i].item() - speech_tokens_out += f"<|c{tmp}|>" - speech_tokens_out += "<|endofspeech|><|im_end|>" - if ibest_writer is not None: - ibest_writer["text"][key[0]] = response.replace("\n", " ") - ibest_writer["label"][key[0]] = label.replace("\n", " ") - ibest_writer["text_tn"][key[0]] = response_clean - ibest_writer["speech_tokens"][key[0]] = speech_tokens_out - - return results, meta_data - - def write_mel_wav(self, output_dir, feat, wav, key): - out_dir = os.path.join(output_dir, "1best_recog", "mels") - os.makedirs(out_dir, exist_ok=True) - if feat is not None: - feat = feat.cpu().numpy()[0] - np.save(os.path.join(out_dir, f"{key}.npy"), feat) - - out_dir = os.path.join(output_dir, "1best_recog", "wavs") - os.makedirs(out_dir, exist_ok=True) - if wav is not None: - path = os.path.join(out_dir, f"{key}.wav") - torchaudio.save( - path, - wav.cpu(), - sample_rate=self.vocoder.sample_rate, - encoding="PCM_S", - bits_per_sample=16, - ) - - def synthesize_waveform(self, speech_tokens, spk_emb, device): - mel_feat, wav = None, None - if self.mel_decoder is not None and spk_emb is not None: - # mel_feat in BxCxT - mel_feat = self.token2mel(speech_tokens, spk_emb, device) - if self.vocoder is not None: - wav = self.vocoder.inference(mel_feat.transpose(1, 2)) - - return mel_feat, wav - - def token2mel(self, tokens: torch.Tensor, xvec: torch.Tensor, device: torch.device): - xvec = torch.tensor(xvec).to(device).unsqueeze(0) - xvec_lens = torch.tensor([xvec.shape[1]], device=device, dtype=torch.int64) - token_lens = torch.tensor([tokens.shape[1]], device=device, dtype=torch.int64) - feat = self.mel_decoder.inference( - tokens, - token_lens, - xvec, - xvec_lens, - diff_steps=10, - temperature=1.0, - prompt=dict(prompt_text=(None, None), prompt_audio=(None, None)), - ) - return feat - - def audio_decode( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - min_length=None, - max_length: int = 30 * 25, - infer_cfg_ratio=None, - decoding_length=None, - ): - # 1. encode text - # text = self.audio_decoder_in_proj(text) - device = text.device - sos_eos_emb = self.audio_decoder_embedding( - torch.tensor([[self.ad_sos_eos]], dtype=torch.int64, device=device) - ) - task_id_emb = self.audio_decoder_embedding( - torch.tensor([[self.ad_task_id]], dtype=torch.int64, device=device) - ) - prompt = torch.cat([sos_eos_emb, text, task_id_emb], dim=1) - seq_input = torch.zeros( - [1, prompt.shape[1] + max_length, prompt.shape[2]], dtype=torch.float32, device=device - ) - seq_input[:, : prompt.shape[1], :] = prompt - out_tokens = torch.zeros([1, max_length, 1], dtype=torch.int64, device=device) - out_token_len = 0 - prompt_len = prompt.shape[1] - state, hit_eos = None, False - for i in range(max_length): - # use state for speedup - pred, (state, _) = self.audio_decoder.score( - seq_input[0, : prompt_len + out_token_len], state, prompt[0] - ) - - # sampling all `nq` token ids - pred = pred.reshape(self.predict_nq, -1) - # normalize scores - pred = torch.log_softmax(pred, dim=-1) - if min_length is not None and i < min_length: - pred[:, self.codebook_size + self.ad_sos_eos] = float(np.finfo(np.float32).min) - top_ids = self.ras_sampling(pred[0], out_tokens[0, :out_token_len, 0]) - - if torch.any(top_ids == (self.codebook_size + self.ad_sos_eos)): - hit_eos = True - out_tokens = out_tokens[:, :out_token_len, :] - break - - out_tokens[0, out_token_len, 0] = top_ids[0] - seq_input[0, prompt_len + out_token_len, :] = self.codec_embedder(top_ids)[0] - out_token_len += 1 - - if decoding_length is None: - return out_tokens - else: - return out_tokens, hit_eos - - # Repetition Aware Sampling in VALL-E 2 - def ras_sampling( - self, weighted_scores, decoded_tokens, *, top_p=0.8, top_k=25, win_size=10, tau_r=0.1 - ): - top_ids = self.nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k) - rep_num = torch.sum(decoded_tokens[-win_size:] == top_ids).item() - if rep_num >= win_size * tau_r: - top_ids = self.random_sampling(weighted_scores) - - return top_ids - - def nucleus_sampling(self, weighted_scores, top_p=0.8, top_k=25): - cum_prob = 0.0 - sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True) - i = len(sorted_idx) - for i in range(len(sorted_idx)): - # sampling both top-p and numbers. - if cum_prob < top_p and i < top_k: - cum_prob += sorted_value[i] - else: - break - prob = sorted_value[:i] - indices = sorted_idx[:i] - sampling_ids = prob.multinomial(1, replacement=True) - top_ids = indices[sampling_ids] - return top_ids - - def random_sampling(self, weighted_scores): - top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True) - return top_ids - - -@tables.register("model_classes", "LLMASR7") -class LLMASR7(nn.Module): - """ """ - - def __init__( - self, - audio_encoder: str = None, - audio_encoder_conf: dict = None, - audio_adaptor: str = None, - audio_adaptor_conf: dict = None, - llm: str = None, - llm_conf: dict = None, - input_size: int = 80, - lsm_weight: float = 0.0, - length_normalized_loss: bool = False, - audio_decoder: str = None, - audio_decoder_conf: dict = None, - **kwargs, - ): - - super().__init__() - - # audio encoder - hub = audio_encoder_conf.get("hub", None) - if hub == "ms": - from funasr import AutoModel - - model = AutoModel(model=audio_encoder, model_revision="master") - # frontend = model.kwargs.get("frontend") - audio_encoder_output_size = model.model.encoder_output_size - - audio_encoder = ( - model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder - ) - - # self.frontend = frontend - - elif hub == "hf": - pass - else: - encoder_class = tables.encoder_classes.get(audio_encoder) - audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf) - audio_encoder_output_size = audio_encoder.output_size() - freeze = audio_encoder_conf.get("freeze", True) - freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1)) - # if freeze_layer_num > 0: - # freeze_layer_num = range(freeze_layer_num) - - if freeze: - for name, param in audio_encoder.named_parameters(): - if freeze_layer_num > 0: - idx = re.search(r"\.\d+\.", name) - if idx is not None: - beg, end = idx.regs[0] - layer_id = int(name[beg + 1 : end - 1]) - if layer_id < freeze_layer_num: - param.requires_grad = False - elif "ln_post." not in name: - param.requires_grad = False - else: - param.requires_grad = False - - audio_encoder.eval() - - self.audio_encoder = audio_encoder - self.audio_encoder_activation_checkpoint = audio_encoder_conf.get( - "activation_checkpoint", False - ) - - # llm - self.llm = None - - from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig - - init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5") - - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map=None, - use_cache=None, - output_hidden_states=True, - ) - - freeze = llm_conf.get("freeze", True) - if freeze: - for name, param in model.named_parameters(): - param.requires_grad = False - model.eval() - if llm_conf.get("activation_checkpoint", False): - model.gradient_checkpointing_enable() - self.llm_dtype = llm_conf.get("llm_dtype", "fp32") - self.llm = model.to(dtype_map[self.llm_dtype]) - llm_dim = model.get_input_embeddings().weight.shape[-1] - self.tokenizer = AutoTokenizer.from_pretrained(init_param_path) - - # adaptor - adaptor_class = tables.adaptor_classes.get(audio_adaptor) - audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size - audio_adaptor_conf["llm_dim"] = llm_dim - audio_adaptor = adaptor_class(**audio_adaptor_conf) - init_param_path = audio_adaptor_conf.get("init_param_path", None) - if init_param_path is not None: - src_state = torch.load(init_param_path, map_location="cpu") - flag = audio_adaptor.load_state_dict(src_state, strict=False) - logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}") - - self.audio_adaptor = audio_adaptor - - self.error_calculator = None - - self.length_normalized_loss = length_normalized_loss - self.beam_search = None - - self.eos = kwargs.get("eos", 151645) - - # tts text tokenizer related - tts_token_type = audio_decoder_conf.get("tts_token_type", "whisper_rich_ttsfrd") - ttsfrd_res_dir = audio_decoder_conf.get("ttsfrd_res_dir", "./ttsfrd/9.5.5") - from funasr.models.llm_asr.tts_text_tokenizer.build_tokenizer import build_tokenizer - - self.tts_text_tokenizer = build_tokenizer( - tts_token_type, - bpemodel=ttsfrd_res_dir, - p_word2phn=1.0, - ) - from funasr.models.llm_asr.tts_models.e2e_model import UCTDXvecSlotModel - - from omegaconf import OmegaConf, DictConfig - - tts_model_conf = kwargs.get("tts_model_conf", {}) - if isinstance(tts_model_conf, DictConfig): - tts_model_conf = OmegaConf.to_container(tts_model_conf, resolve=True) - self.tts_model = UCTDXvecSlotModel(**tts_model_conf) - self.tts_dim_proj = nn.Linear(llm_dim, self.tts_model.output_size) - - # self.codebook_dim = audio_decoder_conf.get("codebook_dim", 1024) - # self.codebook_size = audio_decoder_conf.get("codebook_size", 4096) - # self.lm_out_voc_size = self.codebook_size + 1 - # self.audio_decoder = self.build_audio_decoder(name=audio_decoder, conf=audio_decoder_conf) - # self.concat_emb_hidden = audio_decoder_conf.get("concat_emb_hidden", False) - # self.concat_emb_hidden_norm = audio_decoder_conf.get("concat_emb_hidden_norm", False) - # if self.concat_emb_hidden_norm: - # self.hidden_norm = LayerNorm(llm_dim) - # self.fusion_dropout = nn.Dropout(audio_decoder_conf.get("fusion_drop_rate", 0.0)) - # self.emb_norm = LayerNorm(llm_dim) - # self.fusion_norm = LayerNorm(self.audio_decoder.embed_unit) - # self.fusion_act = Swish() - # audio_decoder_in_proj_dim = llm_dim * 2 if self.concat_emb_hidden else llm_dim - # self.audio_decoder_in_proj = torch.nn.Linear( - # audio_decoder_in_proj_dim, self.audio_decoder.embed_unit - # ) - # self.codec_embedder = torch.nn.Embedding(self.codebook_size, self.codebook_dim) - # self.audio_decoder_embedding = torch.nn.Embedding(2, self.audio_decoder.embed_unit) - # self.ad_sos_eos = 0 - # self.ad_task_id = 1 - # self.ad_ignore_id = -1 - # self.predict_nq = 1 - # - # from .label_smoothing_loss import LabelSmoothingLoss - # - # self.criterion_ce = LabelSmoothingLoss( - # size=self.lm_out_voc_size // self.predict_nq, - # padding_idx=self.ad_ignore_id, - # smoothing=lsm_weight, - # normalize_length=length_normalized_loss, - # reduction=False, - # ) - # - # mel_decoder_name = kwargs.get("mel_decoder", None) - # mel_decoder_conf = kwargs.get("mel_decoder_conf", None) - # self.mel_decoder = self.build_mel_decoder(name=mel_decoder_name, conf=mel_decoder_conf) - vocoder_name = kwargs.get("vocoder", None) - vocoder_conf = kwargs.get("vocoder_conf", None) - self.vocoder = self.build_vocoder(name=vocoder_name, conf=vocoder_conf) - - def build_mel_decoder(self, name: str, conf: dict): - if name is None or conf is None: - return None - if name == "MaskedDiffWithXvec": - from funasr.models.llm_asr.flow_matching import MaskedDiffWithXvec - - return MaskedDiffWithXvec(**conf) - return None - - def build_vocoder(self, name: str, conf: dict): - if name is None or conf is None: - return None - if name == "HifiGAN": - from funasr.models.llm_asr.hifigan import HifiGan - - return HifiGan(**conf) - return None - - def build_audio_decoder(self, name, conf): - if name == "transformer": - from funasr.models.llm_asr.transformer_lm import TransformerEmbedLM - - if "text_vocab_size" in conf: - lm_model = TransformerEmbedLM(vocab_size=self.lm_out_voc_size, **conf) - else: - lm_model = TransformerEmbedLM( - vocab_size=self.lm_out_voc_size, text_vocab_size=self.lm_out_voc_size, **conf - ) - else: - raise TypeError(f"Unknown codec decoder type {name}") - - return lm_model - - def calc_dense_vector(self, codec, codec_lengths): - """ - Args: - codec: (B, T, Nq) - codec_lengths: (B, ) - """ - mask = codec != self.ad_ignore_id - return self.codec_embedder(codec * mask).sum(dim=-2) * mask - - def prepare_audio_decoder_io( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - codec: Optional[torch.Tensor] = None, - codec_lengths: Optional[torch.Tensor] = None, - need_targets: bool = True, - ): - """build inputs and targets for language model - - Normally, this function is called in batchify_nll. - Args: - text: (Batch, Length, Dim) - text_lengths: (Batch,) - codec: (Batch, Length) - codec_lengths: (Batch,) - need_targets: bool, whether provide targets - """ - - if need_targets: - assert ( - codec is not None and codec_lengths is not None - ), "need_target=True, but codec or codec_length is None" - - sos_eos_emb = self.audio_decoder_embedding( - torch.tensor([self.ad_sos_eos], dtype=torch.int64, device=text.device) - ) - task_id_emb = self.audio_decoder_embedding( - torch.tensor([self.ad_task_id], dtype=torch.int64, device=text.device) - ) - codec_emb = None - if codec is not None and codec_lengths is not None: - codec_emb = self.calc_dense_vector(codec, codec_lengths) - inputs_list = [] - for i, text_len in enumerate(text_lengths): - one_input = [sos_eos_emb, text[i, :text_len], task_id_emb] - if codec_emb is not None: - one_input.append(codec_emb[i, : codec_lengths[i]]) - inputs_list.append(torch.cat(one_input, dim=0)) - llm_inputs = pad_list(inputs_list, 0.0) - llm_lengths = text_lengths + 2 - if codec_emb is not None: - llm_lengths = llm_lengths + codec_lengths - - if not need_targets: - return llm_inputs, llm_lengths - - bb, tt = text.shape[0], codec_lengths.max() + 1 - llm_targets = -1 * torch.ones( - [bb, tt, self.predict_nq], dtype=torch.int64, device=text.device - ) - for i, codec_len in enumerate(codec_lengths): - llm_targets[i, :codec_len] = codec[i, :codec_len] - llm_targets[i, codec_len] = self.codebook_size + self.ad_sos_eos - - return (llm_inputs, llm_targets), (llm_lengths, codec_lengths + 1) - - def nll( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - codec: Optional[torch.Tensor] = None, - codec_lengths: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute negative log likelihood(nll) - - Normally, this function is called in batchify_nll. - Args: - text: (Batch, Length, Dim) - text_lengths: (Batch,) - codec: (Batch, Length) - codec_lengths: (Batch,) - """ - batch_size = text.size(0) - # For data parallel - text = text[:, : text_lengths.max()] - codec = codec[:, : codec_lengths.max()] - # text = self.audio_decoder_in_proj(text) - - # build inputs and targets for language model - with autocast(False): - (sequence, target), (x_lengths, y_lengths) = self.prepare_audio_decoder_io( - text, text_lengths, codec, codec_lengths, need_targets=True - ) - - # 2a. Forward Language model - # x: (Batch, Length) -> y: (Batch, Length, NVocab) - sequence = sequence[:, : x_lengths.max()] - target = target[:, : y_lengths.max()] - y, _ = self.audio_decoder(sequence, x_lengths, text_lengths + 1) - bb, tt = y.shape[0], y.shape[1] - y = y.reshape(bb, tt, self.predict_nq, -1) - # 2b. Extract real logits - logits_list = [] - for i, (text_len, codec_len) in enumerate(zip(text_lengths, codec_lengths)): - logits_list.append(y[i, text_len + 1 : text_len + 2 + codec_len]) - logits = pad_list(logits_list, 0.0) - - # 3. Calc negative log likelihood - tt = logits.shape[1] - nll = self.criterion_ce( - logits.reshape(bb, tt * self.predict_nq, -1), target.reshape(bb, tt * self.predict_nq) - ) - nll = nll.sum(-1) - # nll: (BxL,) -> (BxL,) - nll.masked_fill_(make_pad_mask(y_lengths * self.predict_nq).to(nll.device).view(-1), 0.0) - # nll: (BxL,) -> (B, L) - nll = nll.reshape(batch_size, -1).reshape(batch_size, tt, self.predict_nq) - - return nll, logits, target, codec_lengths + 1 - - def forward( - self, - speech: torch.Tensor = None, - speech_lengths: torch.Tensor = None, - input_ids: torch.Tensor = None, - attention_mask: torch.Tensor = None, - labels_ids: torch.Tensor = None, - fbank_beg: torch.Tensor = None, - fbank_mask: torch.Tensor = None, - **kwargs, - ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: - """Encoder + Decoder + Calc loss - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - # import pdb - # - # pdb.set_trace() - stats = {} - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - batch_size, token_num, dims = inputs_embeds.shape - if speech is not None: - if len(speech_lengths.size()) > 1: - speech_lengths = speech_lengths[:, 0] - - batch_size_speech, frames, _ = speech.shape - - # with torch.cuda.amp.autocast(enabled=False): - # audio encoder - if self.audio_encoder_activation_checkpoint: - from torch.utils.checkpoint import checkpoint - - encoder_out, encoder_out_lens = checkpoint( - self.encode, speech, speech_lengths, use_reentrant=False - ) - else: - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - fake_token_len = kwargs.get("fake_token_len") - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - - stats["batch_size_speech"] = batch_size_speech - stats["batch_size_x_frames"] = frames * batch_size_speech - stats["batch_size_real_frames"] = speech_lengths.sum().item() - stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] - - with torch.cuda.amp.autocast( - enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype] - ): - labels_ids[labels_ids == -1] = -100 - attention_mask[attention_mask < 0] = 0 - model_outputs = self.llm( - inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]), - attention_mask=attention_mask, - labels=labels_ids, - ) - loss = model_outputs.loss - - # audio sampling point - audio = kwargs.get("audio") - audio_len = kwargs.get("audio_len") - audio_len = audio_len[-1:] - audio = audio[-1:, : audio_len[0]] - - # codec - codec = kwargs.get("codec") - codec_len = (codec > 0).sum(-1) - codec_len = codec_len[-1:] - codec = codec[-1:, : codec_len[0]] - - input_mask = kwargs.get("input_mask") - input_mask[input_mask < 0] = 0 - - hidden_states = model_outputs.hidden_states[-1].float() - hidden_states_his_select = [] - - # target, str - # target_ids = [] - # target_ids_len = [] - turn_id_cum = 0 - for batch_idx in range(labels_ids.shape[0]): - - for turn_id in range(fbank_beg.shape[1]): - - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - beg = 0 - end = input_mask[turn_id_cum].sum(-1) - # print(f"beg: {beg}, end: {end}") - hidden_states_his_i = hidden_states[batch_idx, beg:end, :] - hidden_states_his_select.append(hidden_states_his_i) - turn_id_cum += 1 - - target = kwargs.get("turn_targets") - target_ids = target[-1:] - - # hidden_states_his_select - hidden_states_his_select = torch.nn.utils.rnn.pad_sequence( - hidden_states_his_select[-1:], batch_first=True, padding_value=0.0 - ) - hidden_states_his_select = hidden_states_his_select.to(device=input_ids.device) - hidden_states_his_select_len = torch.tensor( - [hidden_states_his_select.shape[1]], dtype=torch.int64 - ).to(hidden_states_his_select.device) - - device = hidden_states_his_select.device - text = [ - torch.tensor(self.tts_text_tokenizer.text2tokens(x), dtype=torch.int64).to(device) - for x in target_ids - ] - text_lengths = [len(x) for x in text] - text = pad_list(text, pad_value=-1).long().to(device) - audio_len = torch.tensor(audio_len, dtype=torch.int64).to(device) - text_lengths = torch.tensor(text_lengths, dtype=torch.int64).to(device) - # mute the "da" noise. - # TODO: make sure the sample rate is 22050. - audio[:, : int(0.02 * 22050)] = 0 - hidden_states_his_select = self.tts_dim_proj(hidden_states_his_select) - tts_loss, tts_states, tts_weight = self.tts_model.forward( - text=text, - text_lengths=text_lengths, - speech_token=codec, - speech_token_lengths=codec_len, - audio=audio, - audio_lengths=audio_len, - prompt=hidden_states_his_select, - prompt_len=hidden_states_his_select_len, - ) - loss = tts_loss # loss + tts_loss - for key, value in tts_states.items(): - stats[f"tts_{key}"] = value - - stats["loss"] = torch.clone(loss.detach()) - stats["batch_size"] = batch_size - stats["batch_size_x_tokens"] = token_num * batch_size - stats["batch_size_real_tokens"] = attention_mask.sum().item() - stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] - - dialog_turns = (fbank_beg > 0).sum(-1) - dialog_turns_max = torch.max(dialog_turns).int().item() - dialog_turns_avg = dialog_turns.sum().item() / batch_size - stats["dialog_turns_max"] = dialog_turns_max - stats["dialog_turns_avg"] = dialog_turns_avg - - # force_gatherable: to-device and to-tensor if scalar for DataParallel - if self.length_normalized_loss: - batch_size = int((labels_ids > 0 + 1).sum()) - loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) - return loss, stats, weight - - def encode(self, speech, speech_lengths): - # audio encoder - encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) - - return encoder_out, encoder_out_lens - - def data_template(self, data): - system, user, assistant = [], [], [] - for i, item in enumerate(data): - role = item["role"] - content = item["content"] - if role == "system": - system.append(content) - elif role == "user": - if "audio" in item: - audio = item["audio"] - content = [content, audio] - user.append(content) - elif role == "assistant": - assistant.append(content) - - system = system * len(user) - - contents = { - "system": system, - "user": user, - "assistant": assistant, - } - - return contents - - def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs): - - system = contents["system"] - user = contents["user"] - assistant = contents["assistant"] - pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") - - input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = ( - [], - [], - [], - [], - [], - [], - [], - ) - input_source_ids = [] - for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)): - if i >= kwargs.get("multiturn_num_max", 5): - break - if len(input_ids) > kwargs.get("max_token_length", 1500): - break - - if isinstance(user_prompt, (list, tuple)): - user_prompt, audio = user_prompt - if i == 0: - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - else: - source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - - splits = pattern.split(source_input) - source_ids = [] - fbank_i = [] - fbank_mask_i = [] - fake_token_len_i = 0 - fbank_beg_i = -1 - fbank_lens_i = [] - speech, speech_lengths = [], [] - for k, sub_str in enumerate(splits): - if not sub_str.startswith("<|startofspeech|>"): - sub_token = tokenizer.encode(sub_str) - source_ids += sub_token - fbank_mask_i += [0] * len(sub_token) - else: - sub_str = sub_str.replace("<|startofspeech|>", "").replace( - "<|endofspeech|>", "" - ) - if sub_str.startswith("!"): - sub_str = sub_str[1:] - if sub_str.startswith("!"): # !!: audio sample point - sub_str = audio - try: - time1 = time.perf_counter() - data_src = load_audio_text_image_video(sub_str, fs=frontend.fs) - time2 = time.perf_counter() - meta_data["load_data"] = f"{time2 - time1:0.3f}" - except Exception as e: - logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}") - - speech, speech_lengths = extract_fbank( - data_src, - data_type=kwargs.get("data_type", "sound"), - frontend=frontend, - is_final=True, - ) # speech: [b, T, d] - - time3 = time.perf_counter() - meta_data["extract_feat"] = f"{time3 - time2:0.3f}" - meta_data["batch_data_time"] = ( - speech_lengths.sum().item() - * frontend.frame_shift - * frontend.lfr_n - / 1000 - ) - - if kwargs.get("permute", True): - speech = speech.permute(0, 2, 1) - if speech_lengths > kwargs.get("max_source_length", 5500): - # logging.info( - # f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}" - # ) - badcase_flag = True - - olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2 - olens = 1 + (olens - 3 + 2 * 1) // 2 - fake_token_len_i = (olens - 1) // 2 + 1 - fake_token = [0] * fake_token_len_i - fbank_beg_i = len(source_ids) - source_ids += fake_token - fbank_mask_i += [1] * len(fake_token) - - fbank_beg += [fbank_beg_i + len(input_ids)] - fake_token_len += [fake_token_len_i] - source_mask = [-100] * len(source_ids) - splits = pattern.split(target_out) - for k, sub_str in enumerate(splits): - if len(sub_str) < 1: - continue - if not sub_str.startswith("<|startofspeech|>"): - sub_str = f"{sub_str}<|im_end|>" - sub_token = tokenizer.encode(sub_str) - target_ids = sub_token - # target_out = f"{target_out}<|im_end|>" - # target_ids = tokenizer.encode(target_out) - input_source_ids = input_ids + source_ids - input_ids += source_ids + target_ids - labels += source_mask + target_ids - fbank_mask += fbank_mask_i - if len(speech) > 0: - fbank.append(speech[0, :, :]) - fbank_lens.append(speech_lengths) - - input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] - attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32) - labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] - - # fbank = speech[0, :, :] - # fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32) - fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32) - fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32) - fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32) - source_ids = torch.tensor(input_source_ids, dtype=torch.int64) - target_ids = torch.tensor(target_ids, dtype=torch.int64) - - if len(fbank) > 0: - speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0) - speech_lengths = torch.nn.utils.rnn.pad_sequence( - fbank_lens, batch_first=True, padding_value=-1 - ) - else: - speech = [] - speech_lengths = [] - output = { - "speech": speech, - "speech_lengths": speech_lengths, - "fbank_mask": fbank_mask[None, :], - "fbank_beg": fbank_beg[None,], - "fake_token_len": fake_token_len[None, :], - "input_ids": input_ids[None,], - "attention_mask": attention_mask[None,], - "labels_ids": labels, - "source_ids": source_ids[None, :], - "target_ids": target_ids[None, :], - } - - return output - - def inference_prepare( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - meta_data = {} - prompt = kwargs.get("prompt", None) - - if kwargs.get("batch_size", 1) > 1: - raise NotImplementedError("batch decoding is not implemented") - - contents = self.data_template(data_in[0]) - output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs) - batch = to_device(output, kwargs["device"]) - - # audio encoder - speech = batch["speech"] - if len(speech) > 0: - speech_lengths = batch["speech_lengths"][:, 0] - # fp16 - if kwargs.get("fp16", False): - speech = speech.to(torch.float16) - elif kwargs.get("bf16", False): - speech = speech.to(torch.bfloat16) - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - input_ids = batch["input_ids"] - source_ids = batch["source_ids"] - fbank_beg = batch["fbank_beg"] - fake_token_len = batch["fake_token_len"] - - if not kwargs.get("tearchforing", False): - input_ids = source_ids - - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - batch_size, token_num, dims = inputs_embeds.shape - - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - return inputs_embeds, contents, batch, source_ids, meta_data - - def inference( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare( - data_in, data_lengths, key, tokenizer, frontend, **kwargs - ) - rand_seed = kwargs.get("rand_seed", 0) - - llm_dtype = kwargs.get("llm_dtype", "fp32") - if llm_dtype == "fp32": - llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype - llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype - - with torch.cuda.amp.autocast( - enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] - ): - label = contents["assistant"][-1] - self.llm = self.llm.to(dtype_map[llm_dtype]) - inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) - - # set random seed for reproduce - set_all_random_seed(rand_seed) - generated_ids = self.llm.generate( - inputs_embeds=inputs_embeds, - max_new_tokens=kwargs.get("max_length", 512), - output_hidden_states=True, - return_dict_in_generate=True, - output_scores=True, - ) - hidden_states = generated_ids[ - "hidden_states" - ] # hidden_states: (t1, t2, ..., tn, ..., tN), tn=(l1, l2, ..., ln, ..., lN), ln: shape: 1x1x3584 - - token_num = len(hidden_states) - hidden_states_select = torch.zeros((1, token_num, 3584), dtype=torch.float32).to( - inputs_embeds.device - ) - hidden_states_out_len = torch.tensor( - [ - token_num, - ], - dtype=torch.int32, - ).to(inputs_embeds.device) - for i in range(token_num): - hidden_states_select[0, i, :] = hidden_states[i][-1][0, 0, :].to(torch.float32) - - target_ids = generated_ids["sequences"] - target_emb = self.llm.model.get_input_embeddings()(target_ids) - if self.concat_emb_hidden: - if not self.concat_emb_hidden_norm: - hidden_states_select = torch.concat((hidden_states_select, target_emb), dim=-1) - hidden_states_select = self.audio_decoder_in_proj(hidden_states_select) - else: - outs = self.hidden_norm(hidden_states_select) - outs = self.fusion_dropout(self.fusion_act(outs)) - # emb = model_outputs.hidden_states[0] - emb = self.fusion_dropout(self.fusion_act(self.emb_norm(target_emb))) - outs = self.audio_decoder_in_proj(torch.cat([outs, emb], dim=-1)) - hidden_states_select = self.fusion_act(self.fusion_norm(outs)) - - # set random seed for reproduce - set_all_random_seed(rand_seed) - speech_tokens = self.audio_decode(hidden_states_select, hidden_states_out_len)[ - :, :, 0 - ] # 1xlx1: 2,10,1023 - - # generated_ids = [ - # output_ids[len(input_id) :] - # for input_id, output_ids in zip(input_ids, generated_ids) - # ] - response = tokenizer.batch_decode( - target_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) - )[0] - - loss = None - - # synthesize waveforms - spk_emb = kwargs.get("spk_emb", None) - feat, wav = self.synthesize_waveform(speech_tokens, spk_emb, inputs_embeds.device) - - ibest_writer = None - if kwargs.get("output_dir") is not None: - if not hasattr(self, "writer"): - self.writer = DatadirWriter(kwargs.get("output_dir")) - ibest_writer = self.writer[f"{0 + 1}best_recog"] - - self.write_mel_wav(kwargs.get("output_dir"), feat, wav, key[0]) - - results = [] - response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response) - result_i = { - "key": key[0], - "text": response, - "text_tn": response_clean, - "label": label, - "speech_tokens": speech_tokens, - "wav": wav, - } - if loss is not None: - result_i["loss"] = loss - results.append(result_i) - - speech_tokens_out = "<|startofspeech|>" - for i in range(speech_tokens.shape[-1]): - tmp = speech_tokens[0, i].item() - speech_tokens_out += f"<|c{tmp}|>" - speech_tokens_out += "<|endofspeech|><|im_end|>" - if ibest_writer is not None: - ibest_writer["text"][key[0]] = response.replace("\n", " ") - ibest_writer["label"][key[0]] = label.replace("\n", " ") - ibest_writer["text_tn"][key[0]] = response_clean - ibest_writer["speech_tokens"][key[0]] = speech_tokens_out - - return results, meta_data - - def write_mel_wav(self, output_dir, feat, wav, key): - out_dir = os.path.join(output_dir, "1best_recog", "mels") - os.makedirs(out_dir, exist_ok=True) - if feat is not None: - feat = feat.cpu().numpy()[0] - np.save(os.path.join(out_dir, f"{key}.npy"), feat) - - out_dir = os.path.join(output_dir, "1best_recog", "wavs") - os.makedirs(out_dir, exist_ok=True) - if wav is not None: - path = os.path.join(out_dir, f"{key}.wav") - torchaudio.save( - path, - wav.cpu(), - sample_rate=self.vocoder.sample_rate, - encoding="PCM_S", - bits_per_sample=16, - ) - - def synthesize_waveform(self, speech_tokens, spk_emb, device): - mel_feat, wav = None, None - if self.mel_decoder is not None and spk_emb is not None: - # mel_feat in BxCxT - mel_feat = self.token2mel(speech_tokens, spk_emb, device) - if self.vocoder is not None: - wav = self.vocoder.inference(mel_feat.transpose(1, 2)) - - return mel_feat, wav - - def token2mel(self, tokens: torch.Tensor, xvec: torch.Tensor, device: torch.device): - xvec = torch.tensor(xvec).to(device).unsqueeze(0) - xvec_lens = torch.tensor([xvec.shape[1]], device=device, dtype=torch.int64) - token_lens = torch.tensor([tokens.shape[1]], device=device, dtype=torch.int64) - feat = self.mel_decoder.inference( - tokens, - token_lens, - xvec, - xvec_lens, - diff_steps=10, - temperature=1.0, - prompt=dict(prompt_text=(None, None), prompt_audio=(None, None)), - ) - return feat - - def audio_decode( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - min_length=None, - max_length: int = 30 * 25, - infer_cfg_ratio=None, - decoding_length=None, - ): - # 1. encode text - # text = self.audio_decoder_in_proj(text) - device = text.device - sos_eos_emb = self.audio_decoder_embedding( - torch.tensor([[self.ad_sos_eos]], dtype=torch.int64, device=device) - ) - task_id_emb = self.audio_decoder_embedding( - torch.tensor([[self.ad_task_id]], dtype=torch.int64, device=device) - ) - prompt = torch.cat([sos_eos_emb, text, task_id_emb], dim=1) - seq_input = torch.zeros( - [1, prompt.shape[1] + max_length, prompt.shape[2]], dtype=torch.float32, device=device - ) - seq_input[:, : prompt.shape[1], :] = prompt - out_tokens = torch.zeros([1, max_length, 1], dtype=torch.int64, device=device) - out_token_len = 0 - prompt_len = prompt.shape[1] - state, hit_eos = None, False - for i in range(max_length): - # use state for speedup - pred, (state, _) = self.audio_decoder.score( - seq_input[0, : prompt_len + out_token_len], state, prompt[0] - ) - - # sampling all `nq` token ids - pred = pred.reshape(self.predict_nq, -1) - # normalize scores - pred = torch.log_softmax(pred, dim=-1) - if min_length is not None and i < min_length: - pred[:, self.codebook_size + self.ad_sos_eos] = float(np.finfo(np.float32).min) - top_ids = self.ras_sampling(pred[0], out_tokens[0, :out_token_len, 0]) - - if torch.any(top_ids == (self.codebook_size + self.ad_sos_eos)): - hit_eos = True - out_tokens = out_tokens[:, :out_token_len, :] - break - - out_tokens[0, out_token_len, 0] = top_ids[0] - seq_input[0, prompt_len + out_token_len, :] = self.codec_embedder(top_ids)[0] - out_token_len += 1 - - if decoding_length is None: - return out_tokens - else: - return out_tokens, hit_eos - - # Repetition Aware Sampling in VALL-E 2 - def ras_sampling( - self, weighted_scores, decoded_tokens, *, top_p=0.8, top_k=25, win_size=10, tau_r=0.1 - ): - top_ids = self.nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k) - rep_num = torch.sum(decoded_tokens[-win_size:] == top_ids).item() - if rep_num >= win_size * tau_r: - top_ids = self.random_sampling(weighted_scores) - - return top_ids - - def nucleus_sampling(self, weighted_scores, top_p=0.8, top_k=25): - cum_prob = 0.0 - sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True) - i = len(sorted_idx) - for i in range(len(sorted_idx)): - # sampling both top-p and numbers. - if cum_prob < top_p and i < top_k: - cum_prob += sorted_value[i] - else: - break - prob = sorted_value[:i] - indices = sorted_idx[:i] - sampling_ids = prob.multinomial(1, replacement=True) - top_ids = indices[sampling_ids] - return top_ids - - def random_sampling(self, weighted_scores): - top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True) - return top_ids - - -@tables.register("model_classes", "LLMVAD") -class LLMVAD(nn.Module): - """ """ - - def __init__( - self, - audio_encoder: str = None, - audio_encoder_conf: dict = None, - audio_adaptor: str = None, - audio_adaptor_conf: dict = None, - llm: str = None, - llm_conf: dict = None, - input_size: int = 80, - length_normalized_loss: bool = False, - **kwargs, - ): - - super().__init__() - - # audio encoder - hub = audio_encoder_conf.get("hub", None) - self.audio_encoder_activation_checkpoint = audio_encoder_conf.get( - "activation_checkpoint", False - ) - if hub == "ms": - from funasr import AutoModel - - model = AutoModel(model=audio_encoder, model_revision="master") - # frontend = model.kwargs.get("frontend") - audio_encoder_output_size = model.model.encoder_output_size - - audio_encoder = ( - model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder - ) - - # self.frontend = frontend - - elif hub == "hf": - pass - else: - encoder_class = tables.encoder_classes.get(audio_encoder) - audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf) - audio_encoder_output_size = audio_encoder.output_size() - freeze = audio_encoder_conf.get("freeze", True) - freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1)) - # if freeze_layer_num > 0: - # freeze_layer_num = range(freeze_layer_num) - - if freeze: - for name, param in audio_encoder.named_parameters(): - if freeze_layer_num > 0: - idx = re.search(r"\.\d+\.", name) - if idx is not None: - beg, end = idx.regs[0] - layer_id = int(name[beg + 1 : end - 1]) - if layer_id < freeze_layer_num: - param.requires_grad = False - elif "ln_post." not in name: - param.requires_grad = False - else: - param.requires_grad = False - - audio_encoder.eval() - - self.audio_encoder = audio_encoder - - # llm - self.llm = None - - from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig - - init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5") - logging.info(f"Loading llm ckpt: {init_param_path}") - model = AutoModelForCausalLM.from_pretrained( - init_param_path, - load_in_8bit=None, - device_map=None, - use_cache=None, - ) - logging.info(f"llm ckpt loaded: {init_param_path}") - - freeze = llm_conf.get("freeze", True) - if freeze: - for name, param in model.named_parameters(): - param.requires_grad = False - model.eval() - - logging.info(f"use_lora: {llm_conf.get('use_lora', False)}") - if llm_conf.get("use_lora", False): - from omegaconf import OmegaConf, DictConfig - - lora_conf = llm_conf.get("lora_conf", {}) - if isinstance(lora_conf, (OmegaConf, DictConfig)): - lora_conf = OmegaConf.to_container(lora_conf, resolve=True) - from peft import get_peft_model, LoraConfig, TaskType, PeftConfig, PeftModel - - lora_init_param_path = lora_conf.get("init_param_path", None) - if lora_init_param_path is not None: - model = PeftModel.from_pretrained(model, lora_init_param_path) - else: - peft_config = LoraConfig(**lora_conf) - model = get_peft_model(model, peft_config) - model.print_trainable_parameters() - - if llm_conf.get("activation_checkpoint", False): - model.gradient_checkpointing_enable() - - self.llm_dtype = llm_conf.get("llm_dtype", "fp32") - self.llm = model.to(dtype_map[self.llm_dtype]) - llm_dim = model.get_input_embeddings().weight.shape[-1] - - # adaptor - adaptor_class = tables.adaptor_classes.get(audio_adaptor) - audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size - audio_adaptor_conf["llm_dim"] = llm_dim - audio_adaptor = adaptor_class(**audio_adaptor_conf) - init_param_path = audio_adaptor_conf.get("init_param_path", None) - if init_param_path is not None: - src_state = torch.load(init_param_path, map_location="cpu") - flag = audio_adaptor.load_state_dict(src_state, strict=False) - logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}") - freeze = audio_adaptor_conf.get("freeze", False) - if freeze: - for name, param in audio_adaptor.named_parameters(): - param.requires_grad = False - audio_adaptor.eval() - - self.audio_adaptor = audio_adaptor - - self.error_calculator = None - - self.length_normalized_loss = length_normalized_loss - self.beam_search = None - - self.loss_fct = CrossEntropyLoss() - - print("self.llm.config:", self.llm.config) - from transformers.models.qwen2.modeling_qwen2 import Qwen2DecoderLayer - from copy import deepcopy - - self.task_decoder_layer_config = deepcopy(self.llm.config) - self.task_decoder_layer_config.hidden_size = self.llm.config.hidden_size // 4 - self.task_decoder_layer_config.intermediate_size = self.llm.config.intermediate_size // 4 - self.task_decoder_layer_config.num_attention_heads = ( - self.llm.config.num_attention_heads // 4 - ) - self.task_decoder_layer_config.num_key_value_heads = ( - self.llm.config.num_key_value_heads // 4 - ) - print("self.task_decoder_layer_config:", self.task_decoder_layer_config) - self.down_proj = nn.Linear( - self.llm.config.hidden_size, self.task_decoder_layer_config.hidden_size, bias=False - ).to(dtype_map[self.llm_dtype]) - self.task_decoder_layer = Qwen2DecoderLayer( - self.task_decoder_layer_config, self.llm.config.num_hidden_layers - ).to(dtype_map[self.llm_dtype]) - if getattr(self.llm.config, "classifier_dropout", None) is not None: - classifier_dropout = self.llm.config.classifier_dropout - elif getattr(self.llm.config, "hidden_dropout", None) is not None: - classifier_dropout = self.llm.config.hidden_dropout - else: - classifier_dropout = 0.1 - self.dropout = nn.Dropout(classifier_dropout) - self.barge_in_num_labels = 2 - self.turn_taking_num_labels = 2 - self.barge_in_score = nn.Linear( - self.task_decoder_layer_config.hidden_size, self.barge_in_num_labels - ).to(dtype_map[self.llm_dtype]) - self.turn_taking_score = nn.Linear( - self.task_decoder_layer_config.hidden_size, self.turn_taking_num_labels - ).to(dtype_map[self.llm_dtype]) - - def forward( - self, - speech: torch.Tensor = None, - speech_lengths: torch.Tensor = None, - input_ids: torch.Tensor = None, - attention_mask: torch.Tensor = None, - labels_ids: torch.Tensor = None, - fbank_beg: torch.Tensor = None, - fbank_mask: torch.Tensor = None, - turn_taking_labels: torch.Tensor = None, - barge_in_labels: torch.Tensor = None, - **kwargs, - ): - """Encoder + Decoder + Calc loss - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - # import pdb - # - # pdb.set_trace() - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - if speech is not None: - if len(speech_lengths.size()) > 1: - speech_lengths = speech_lengths[:, 0] - - batch_size_speech, frames, _ = speech.shape - batch_size, token_num = input_ids.shape - - # with torch.cuda.amp.autocast(enabled=False): - # audio encoder - if self.audio_encoder_activation_checkpoint: - from torch.utils.checkpoint import checkpoint - - encoder_out, encoder_out_lens = checkpoint( - self.encode, speech, speech_lengths, use_reentrant=False - ) - else: - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - batch_size, token_num, dims = inputs_embeds.shape - fake_token_len = kwargs.get("fake_token_len") - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - - with torch.cuda.amp.autocast( - enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype] - ): - labels_ids[labels_ids == -1] = -100 - attention_mask[attention_mask < 0] = 0 - model_outputs = self.llm( - inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]), - attention_mask=attention_mask, - labels=labels_ids, - output_hidden_states=True, - ) - output_attentions = kwargs.get("output_attentions", None) - past_key_values = kwargs.get("past_key_values", None) - past_key_values_length = kwargs.get("past_key_values_length", 0) - position_ids = kwargs.get("position_ids", None) - use_cache = kwargs.get("use_cache", None) - seq_length = token_num - if position_ids is None: - device = input_ids.device if input_ids is not None else inputs_embeds.device - position_ids = torch.arange( - past_key_values_length, - seq_length + past_key_values_length, - dtype=torch.long, - device=device, - ) - position_ids = position_ids.unsqueeze(0).view(-1, seq_length) - else: - position_ids = position_ids.view(-1, seq_length).long() - from transformers.modeling_attn_mask_utils import ( - _prepare_4d_causal_attention_mask, - _prepare_4d_causal_attention_mask_for_sdpa, - ) - - if self.llm.config._attn_implementation == "flash_attention_2": - # 2d mask is passed through the layers - causal_attention_mask = ( - attention_mask if (attention_mask is not None and 0 in attention_mask) else None - ) - elif self.llm.config._attn_implementation == "sdpa" and not output_attentions: - # output_attentions=True can not be supported when using SDPA, and we fall back on - # the manual implementation that requires a 4D causal mask in all cases. - causal_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( - attention_mask, - (batch_size, seq_length), - inputs_embeds, - past_key_values_length, - sliding_window=self.llm.config.sliding_window, - ) - else: - # 4d mask is passed through the layers - causal_attention_mask = _prepare_4d_causal_attention_mask( - attention_mask, - (batch_size, seq_length), - inputs_embeds, - past_key_values_length, - sliding_window=self.llm.config.sliding_window, - ) - - sequence_output = model_outputs.hidden_states[-1] - sequence_output = self.down_proj(sequence_output) - if self.llm.model.gradient_checkpointing and self.llm.model.training: - layer_outputs = self.llm._gradient_checkpointing_func( - self.task_decoder_layer.__call__, - sequence_output, - causal_attention_mask, - position_ids, - past_key_values, - output_attentions, - use_cache, - ) - else: - layer_outputs = self.task_decoder_layer( - sequence_output, - attention_mask=causal_attention_mask, - position_ids=position_ids, - past_key_value=past_key_values, - output_attentions=output_attentions, - use_cache=use_cache, - ) - - sequence_output = layer_outputs[0] - - sequence_output = self.dropout(sequence_output) - turn_taking_logits = self.turn_taking_score(sequence_output) - barge_in_logits = self.barge_in_score(sequence_output) - - loss = None - if barge_in_labels is not None: - barge_in_labels[barge_in_labels == -1] = -100 - barge_in_loss = self.loss_fct( - barge_in_logits.view(-1, self.barge_in_num_labels), barge_in_labels.view(-1) - ) - loss = barge_in_loss - if turn_taking_labels is not None: - turn_taking_labels[turn_taking_labels == -1] = -100 - turn_taking_loss = self.loss_fct( - turn_taking_logits.view(-1, self.turn_taking_num_labels), - turn_taking_labels.view(-1), - ) - loss = turn_taking_loss if loss is None else loss + turn_taking_loss - - stats = {} - # with torch.no_grad(): - # preds = torch.argmax(model_outputs.logits, -1) - # acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100) - # stats["acc"] = acc_att - if turn_taking_labels is not None: - stats["turn_taking_loss"] = torch.clone(turn_taking_loss.detach()) - with torch.no_grad(): - turn_taking_preds = torch.argmax(turn_taking_logits, -1) - turn_taking_acc = compute_accuracy( - turn_taking_preds, turn_taking_labels, ignore_label=-100 - ) - stats["turn_taking_acc"] = turn_taking_acc - if barge_in_labels is not None: - stats["barge_in_loss"] = torch.clone(barge_in_loss.detach()) - with torch.no_grad(): - barge_in_preds = torch.argmax(barge_in_logits, -1) - barge_in_acc = compute_accuracy(barge_in_preds, barge_in_labels, ignore_label=-100) - stats["barge_in_acc"] = barge_in_acc - stats["loss"] = torch.clone(loss.detach()) - stats["batch_size"] = batch_size - stats["batch_size_speech"] = batch_size_speech - stats["batch_size_x_frames"] = frames * batch_size_speech - stats["batch_size_real_frames"] = speech_lengths.sum().item() - stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] - stats["batch_size_x_tokens"] = token_num * batch_size - stats["batch_size_real_tokens"] = attention_mask.sum().item() - stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] - - dialog_turns = (fbank_beg > 0).sum(-1) - dialog_turns_max = torch.max(dialog_turns).int().item() - dialog_turns_avg = dialog_turns.sum().item() / batch_size - stats["dialog_turns_max"] = dialog_turns_max - stats["dialog_turns_avg"] = dialog_turns_avg - - # force_gatherable: to-device and to-tensor if scalar for DataParallel - if self.length_normalized_loss: - batch_size = int((labels_ids > 0 + 1).sum()) - loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) - return loss, stats, weight - - def vad_inference( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - inputs_embeds, contents, batch, source_ids, meta_data = self.vad_inference_prepare( - data_in, data_lengths, key, tokenizer, frontend, **kwargs - ) - task = contents.get("task", "vad") - fbank_beg = batch["fbank_beg"] - fake_token_len = batch["fake_token_len"] - fbank_mask = batch["fbank_mask"] - batch_size, token_num, dims = inputs_embeds.shape - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - llm_dtype = kwargs.get("llm_dtype", "fp32") - if llm_dtype == "fp32": - llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype - llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype - - stats = { - "turn_taking_preds": [], - "barge_in_preds": [], - "turn_taking_labels": [], - "barge_in_labels": [], - "task": task, - } - with torch.cuda.amp.autocast( - enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] - ): - self.llm = self.llm.to(dtype_map[llm_dtype]) - self.down_proj = self.down_proj.to(dtype_map[llm_dtype]) - self.task_decoder_layer = self.task_decoder_layer.to(dtype_map[llm_dtype]) - self.turn_taking_score = self.turn_taking_score.to(dtype_map[llm_dtype]) - self.barge_in_score = self.barge_in_score.to(dtype_map[llm_dtype]) - - inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) - llm_kwargs = kwargs.get("llm_kwargs", {}) - - attention_mask = batch.get("attention_mask", None) - # attention_mask = attention_mask.to(dtype_map[llm_dtype]) - model_outputs = self.llm( - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - labels=None, - output_hidden_states=True, - **llm_kwargs, - ) - output_attentions = llm_kwargs.get("output_attentions", None) - past_key_values = llm_kwargs.get("past_key_values", None) - past_key_values_length = llm_kwargs.get("past_key_values_length", 0) - position_ids = llm_kwargs.get("position_ids", None) - use_cache = llm_kwargs.get("use_cache", None) - seq_length = token_num - if position_ids is None: - device = inputs_embeds.device - position_ids = torch.arange( - past_key_values_length, - seq_length + past_key_values_length, - dtype=torch.long, - device=device, - ) - position_ids = position_ids.unsqueeze(0).view(-1, seq_length) - else: - position_ids = position_ids.view(-1, seq_length).long() - - from transformers.modeling_attn_mask_utils import ( - _prepare_4d_causal_attention_mask, - _prepare_4d_causal_attention_mask_for_sdpa, - ) - - if self.llm.config._attn_implementation == "flash_attention_2": - # 2d mask is passed through the layers - attention_mask = ( - attention_mask if (attention_mask is not None and 0 in attention_mask) else None - ) - elif self.llm.config._attn_implementation == "sdpa" and not output_attentions: - # output_attentions=True can not be supported when using SDPA, and we fall back on - # the manual implementation that requires a 4D causal mask in all cases. - attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( - attention_mask, - (batch_size, seq_length), - inputs_embeds, - past_key_values_length, - sliding_window=self.llm.config.sliding_window, - ) - else: - # 4d mask is passed through the layers - attention_mask = _prepare_4d_causal_attention_mask( - attention_mask, - (batch_size, seq_length), - inputs_embeds, - past_key_values_length, - sliding_window=self.llm.config.sliding_window, - ) - - sequence_output = model_outputs.hidden_states[-1] - sequence_output = self.down_proj(sequence_output) - - layer_outputs = self.task_decoder_layer( - sequence_output, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_value=past_key_values, - output_attentions=output_attentions, - use_cache=use_cache, - ) - - sequence_output = layer_outputs[0] - - sequence_output = self.dropout(sequence_output) - turn_taking_logits = self.turn_taking_score(sequence_output) - barge_in_logits = self.barge_in_score(sequence_output) - - turn_taking_labels = batch.get("turn_taking_labels", None) - barge_in_labels = batch.get("barge_in_labels", None) - # print(f'batch: {batch}') - # print(f"fake_token_len: {fake_token_len}") - # print(f"turn taking labels: {turn_taking_labels}") - # print(f"barge in labels: {barge_in_labels}") - turn_taking_preds_res = [] - barge_in_preds_res = [] - turn_taking_labels_res = [] - barge_in_labels_res = [] - with torch.no_grad(): - turn_taking_preds = torch.argmax(turn_taking_logits, -1) - barge_in_preds = torch.argmax(barge_in_logits, -1) - for batch_idx in range(batch_size): - fbank_begin_index = fbank_beg[batch_idx, -1].item() - fbank_end_index = fbank_begin_index + fake_token_len[batch_idx, -1].item() - turn_taking_preds_last = ( - turn_taking_preds[batch_idx, fbank_begin_index:fbank_end_index] - .cpu() - .numpy() - .tolist() - ) - turn_taking_preds_res.append(turn_taking_preds_last) - # print(f"turn_taking_labels: {turn_taking_labels}") - turn_taking_labels_last = ( - turn_taking_labels[batch_idx, fbank_begin_index:fbank_end_index] - .cpu() - .numpy() - .tolist() - ) - turn_taking_labels_res.append(turn_taking_labels_last) - # print(f"turn_taking_preds: {turn_taking_preds_last}") - barge_in_preds_last = ( - barge_in_preds[batch_idx, fbank_begin_index:fbank_end_index] - .cpu() - .numpy() - .tolist() - ) - barge_in_preds_res.append(barge_in_preds_last) - # print(f"barge_in_labels: {barge_in_labels}") - barge_in_labels_last = ( - barge_in_labels[batch_idx, fbank_begin_index:fbank_end_index] - .cpu() - .numpy() - .tolist() - ) - barge_in_labels_res.append(barge_in_labels_last) - - turn_taking_acc = compute_accuracy( - turn_taking_preds, turn_taking_labels, ignore_label=-100 - ) - stats["turn_taking_acc"] = turn_taking_acc.item() - - barge_in_acc = compute_accuracy(barge_in_preds, barge_in_labels, ignore_label=-100) - stats["barge_in_acc"] = barge_in_acc.item() - stats["turn_taking_preds"].append(turn_taking_preds_res) - stats["barge_in_preds"].append(barge_in_preds_res) - stats["turn_taking_labels"].append(turn_taking_labels_res) - stats["barge_in_labels"].append(barge_in_labels_res) - return turn_taking_logits, barge_in_logits, meta_data, stats - - def encode(self, speech, speech_lengths): - # audio encoder - encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) - - return encoder_out, encoder_out_lens - - def vad_data_template(self, sample): - data = sample["messages"] - system, user, assistant = [], [], [] - for i, item in enumerate(data): - role = item["role"] - content = item["content"] - if role == "system": - system.append(content) - elif role == "user": - if "audio" in item: - audio = item["audio"] - content = [content, audio] - user.append(content) - elif role == "assistant": - assistant.append(content) - - system = system * len(user) - assistant.append("") - contents = { - "system": system, - "user": user, - "assistant": assistant, - } - - if "task" in sample: - task = sample["task"] - last_total_time = data[-1]["end_time"] - data[-1]["start_time"] - if task == "turn-taking": - true_time_span = data[-1]["turn-taking-gap_time-added"] - elif task == "barge-in": - true_time_span = last_total_time - data[-1]["barge-in-0"] - else: - raise ValueError("task must be turn-taking or barge-in") - contents["true_time_span"] = true_time_span - contents["last_total_time"] = last_total_time - contents["task"] = sample["task"] - return contents - - def data_template(self, data): - system, user, assistant = [], [], [] - for i, item in enumerate(data): - role = item["role"] - content = item["content"] - if role == "system": - system.append(content) - elif role == "user": - if "audio" in item: - audio = item["audio"] - content = [content, audio] - user.append(content) - elif role == "assistant": - assistant.append(content) - - system = system * len(user) - - contents = { - "system": system, - "user": user, - "assistant": assistant, - } - - return contents - - def vad_data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs): - - system = contents["system"] - user = contents["user"] - assistant = contents["assistant"] - pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") - - input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = ( - [], - [], - [], - [], - [], - [], - [], - ) - input_source_ids = [] - for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)): - if isinstance(user_prompt, (list, tuple)): - user_prompt, audio = user_prompt - if i == 0: - source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - elif i == len(system) - 1: - source_input = f"<|im_start|>user\n{user_prompt}" - else: - source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n" - - splits = pattern.split(source_input) - source_ids = [] - fbank_i = [] - fbank_mask_i = [] - fake_token_len_i = 0 - fbank_beg_i = -1 - fbank_lens_i = [] - speech, speech_lengths = [], [] - for k, sub_str in enumerate(splits): - if not sub_str.startswith("<|startofspeech|>"): - sub_token = tokenizer.encode(sub_str) - source_ids += sub_token - fbank_mask_i += [0] * len(sub_token) - else: - sub_str = sub_str.replace("<|startofspeech|>", "").replace( - "<|endofspeech|>", "" - ) - if sub_str.startswith("!"): - sub_str = sub_str[1:] - if sub_str.startswith("!"): # !!: audio sample point - sub_str = audio - try: - time1 = time.perf_counter() - data_src = load_audio_text_image_video(sub_str, fs=frontend.fs) - time2 = time.perf_counter() - meta_data["load_data"] = f"{time2 - time1:0.3f}" - except Exception as e: - logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}") - - speech, speech_lengths = extract_fbank( - data_src, - data_type=kwargs.get("data_type", "sound"), - frontend=frontend, - is_final=True, - ) # speech: [b, T, d] - - time3 = time.perf_counter() - meta_data["extract_feat"] = f"{time3 - time2:0.3f}" - meta_data["batch_data_time"] = ( - speech_lengths.sum().item() - * frontend.frame_shift - * frontend.lfr_n - / 1000 - ) - - if kwargs.get("permute", True): - speech = speech.permute(0, 2, 1) - if speech_lengths > kwargs.get("max_source_length", 5500): - # logging.info( - # f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}" - # ) - badcase_flag = True - - olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2 - olens = 1 + (olens - 3 + 2 * 1) // 2 - fake_token_len_i = (olens - 1) // 2 + 1 - fake_token = [0] * fake_token_len_i - fbank_beg_i = len(source_ids) - source_ids += fake_token - fbank_mask_i += [1] * len(fake_token) - - fbank_beg += [fbank_beg_i + len(input_ids)] - fake_token_len += [fake_token_len_i] - source_mask = [-100] * len(source_ids) - # target_out = f"{target_out}<|im_end|>" - # target_ids = tokenizer.encode(target_out) - target_ids = [] - input_source_ids = input_ids + source_ids - input_ids += source_ids + target_ids - labels += source_mask + target_ids - fbank_mask += fbank_mask_i - if len(speech) > 0: - fbank.append(speech[0, :, :]) - fbank_lens.append(speech_lengths) - - turn_taking_labels = [-100] * len(labels) - barge_in_labels = [-100] * len(labels) - last_vad = [0] * fake_token_len[-1] - if "true_time_span" in contents: - true_time_span = contents["true_time_span"] - last_time_span = contents["last_total_time"] - pos_vad = math.ceil(fake_token_len[-1] * (true_time_span / last_time_span)) - assert pos_vad <= fake_token_len[-1] - if pos_vad > 0: - last_vad[-pos_vad:] = [1] * pos_vad - turn_taking_labels[-fake_token_len[-1] :] = last_vad - barge_in_labels[-fake_token_len[-1] :] = last_vad - - input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] - attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32) - labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] - turn_taking_labels = torch.tensor( - [turn_taking_labels], dtype=torch.int64 - ) # [: self.max_token_length] - barge_in_labels = torch.tensor( - [barge_in_labels], dtype=torch.int64 - ) # [: self.max_token_length] - - # fbank = speech[0, :, :] - # fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32) - fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32) - fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32) - fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32) - source_ids = torch.tensor(input_source_ids, dtype=torch.int64) - target_ids = torch.tensor(target_ids, dtype=torch.int64) - - if len(fbank) > 0: - speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0) - speech_lengths = torch.nn.utils.rnn.pad_sequence( - fbank_lens, batch_first=True, padding_value=-1 - ) - else: - speech = [] - speech_lengths = [] - output = { - "speech": speech, - "speech_lengths": speech_lengths, - "fbank_mask": fbank_mask[None, :], - "fbank_beg": fbank_beg[None,], - "fake_token_len": fake_token_len[None, :], - "input_ids": input_ids[None,], - "attention_mask": attention_mask[None,], - "labels_ids": labels, - "source_ids": source_ids[None, :], - "target_ids": target_ids[None, :], - "turn_taking_labels": turn_taking_labels, - "barge_in_labels": barge_in_labels, - } - - return output - - def vad_inference_prepare( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - meta_data = {} - prompt = kwargs.get("prompt", None) - - if kwargs.get("batch_size", 1) > 1: - raise NotImplementedError("batch decoding is not implemented") - - contents = self.vad_data_template(data_in[0]) - output = self.vad_data_load_speech( - contents, tokenizer, frontend, meta_data=meta_data, **kwargs - ) - batch = to_device(output, kwargs["device"]) - - # audio encoder - speech = batch["speech"] - if len(speech) > 0: - speech_lengths = batch["speech_lengths"][:, 0] - # fp16 - if kwargs.get("fp16", False): - speech = speech.to(torch.float16) - elif kwargs.get("bf16", False): - speech = speech.to(torch.bfloat16) - # audio encoder - encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) - - # audio_adaptor - encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens) - - input_ids = batch["input_ids"] - source_ids = batch["source_ids"] - fbank_beg = batch["fbank_beg"] - fake_token_len = batch["fake_token_len"] - - if not kwargs.get("tearchforing", False): - input_ids = source_ids - - input_ids[input_ids < 0] = 0 - inputs_embeds = self.llm.model.get_input_embeddings()(input_ids) - - batch_size, token_num, dims = inputs_embeds.shape - - fake_token_len[fake_token_len < 0] = 0 - fbank_beg[fbank_beg < 0] = 0 - - speech_idx = 0 - for batch_idx in range(batch_size): - - for turn_id in range(fbank_beg.shape[1]): - fbank_beg_idx = fbank_beg[batch_idx, turn_id].item() - if fbank_beg_idx > 0: - speech_token_len = fake_token_len[batch_idx, turn_id] - speech_token = encoder_out[speech_idx, :speech_token_len, :] - - try: - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - except Exception as e: - # - logging.error(f"{str(e)}, {traceback.format_exc()}") - logging.info( - f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}" - ) - # import pdb; - # pdb.set_trace() - speech_token_len = encoder_out_lens[speech_idx].item() - speech_token = encoder_out[speech_idx, :speech_token_len, :] - inputs_embeds[ - batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, : - ] = speech_token - - speech_idx += 1 - return inputs_embeds, contents, batch, source_ids, meta_data - - def inference( - self, - data_in, - data_lengths=None, - key: list = None, - tokenizer=None, - frontend=None, - **kwargs, - ): - - inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare( - data_in, data_lengths, key, tokenizer, frontend, **kwargs - ) - - llm_dtype = kwargs.get("llm_dtype", "fp32") - if llm_dtype == "fp32": - llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype - llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype - - with torch.cuda.amp.autocast( - enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype] - ): - label = contents["assistant"][-1] - self.llm = self.llm.to(dtype_map[llm_dtype]) - inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype]) - llm_kwargs = kwargs.get("llm_kwargs", {}) - if not kwargs.get("tearchforing", False): - - generated_ids = self.llm.generate( - inputs_embeds=inputs_embeds, - max_new_tokens=kwargs.get("max_length", 512), - **llm_kwargs, - ) - # generated_ids = [ - # output_ids[len(input_id) :] - # for input_id, output_ids in zip(input_ids, generated_ids) - # ] - response = tokenizer.batch_decode( - generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) - )[0] - - loss = None - else: - - labels_ids = batch["labels_ids"] - labels_ids[labels_ids == -1] = -100 - attention_mask = batch.get("attention_mask", None) - # attention_mask = attention_mask.to(dtype_map[llm_dtype]) - model_outputs = self.llm( - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - labels=labels_ids, - **llm_kwargs, - ) - - preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :] - response = tokenizer.batch_decode( - preds, - add_special_tokens=False, - skip_special_tokens=kwargs.get("skip_special_tokens", True), - )[0] - loss = model_outputs.loss.item() - - ibest_writer = None - if kwargs.get("output_dir") is not None: - if not hasattr(self, "writer"): - self.writer = DatadirWriter(kwargs.get("output_dir")) - ibest_writer = self.writer[f"{0 + 1}best_recog"] - - results = [] - response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response) - result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label} - if loss is not None: - result_i["loss"] = loss - results.append(result_i) - - if ibest_writer is not None: - ibest_writer["text"][key[0]] = response.replace("\n", " ") - ibest_writer["label"][key[0]] = label.replace("\n", " ") - ibest_writer["text_tn"][key[0]] = response_clean - - return results, meta_data diff --git a/funasr/models/llm_asr/tts_models/__init__.py b/funasr/models/llm_asr/tts_models/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/funasr/models/llm_asr/tts_models/campp_encoder.py b/funasr/models/llm_asr/tts_models/campp_encoder.py deleted file mode 100644 index 8d49e7dee..000000000 --- a/funasr/models/llm_asr/tts_models/campp_encoder.py +++ /dev/null @@ -1,365 +0,0 @@ -# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. -# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) - -from collections import OrderedDict - -import torch -from torch import nn -import torch.nn.functional as F - -# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. -# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) - -import torch -import torch.nn.functional as F -from torch import nn - - -def get_nonlinear(config_str, channels): - nonlinear = nn.Sequential() - for name in config_str.split('-'): - if name == 'relu': - nonlinear.add_module('relu', nn.ReLU(inplace=True)) - elif name == 'prelu': - nonlinear.add_module('prelu', nn.PReLU(channels)) - elif name == 'batchnorm': - nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels)) - elif name == 'batchnorm_': - nonlinear.add_module('batchnorm', - nn.BatchNorm1d(channels, affine=False)) - else: - raise ValueError('Unexpected module ({}).'.format(name)) - return nonlinear - - -def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2): - mean = x.mean(dim=dim) - std = x.std(dim=dim, unbiased=unbiased) - stats = torch.cat([mean, std], dim=-1) - if keepdim: - stats = stats.unsqueeze(dim=dim) - return stats - - -class StatsPool(nn.Module): - def forward(self, x): - return statistics_pooling(x) - - -class TDNNLayer(nn.Module): - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - bias=False, - config_str='batchnorm-relu'): - super(TDNNLayer, self).__init__() - if padding < 0: - assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format( - kernel_size) - padding = (kernel_size - 1) // 2 * dilation - self.linear = nn.Conv1d(in_channels, - out_channels, - kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias) - self.nonlinear = get_nonlinear(config_str, out_channels) - - def forward(self, x): - x = self.linear(x) - x = self.nonlinear(x) - return x - - -class CAMLayer(nn.Module): - def __init__(self, - bn_channels, - out_channels, - kernel_size, - stride, - padding, - dilation, - bias, - reduction=2): - super(CAMLayer, self).__init__() - self.linear_local = nn.Conv1d(bn_channels, - out_channels, - kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias) - self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1) - self.relu = nn.ReLU(inplace=True) - self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1) - self.sigmoid = nn.Sigmoid() - - def forward(self, x): - y = self.linear_local(x) - context = x.mean(-1, keepdim=True) + self.seg_pooling(x) - context = self.relu(self.linear1(context)) - m = self.sigmoid(self.linear2(context)) - return y * m - - def seg_pooling(self, x, seg_len=100, stype='avg'): - if stype == 'avg': - seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True) - elif stype == 'max': - seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True) - else: - raise ValueError('Wrong segment pooling type.') - shape = seg.shape - seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1) - seg = seg[..., :x.shape[-1]] - return seg - - -class CAMDenseTDNNLayer(nn.Module): - def __init__(self, - in_channels, - out_channels, - bn_channels, - kernel_size, - stride=1, - dilation=1, - bias=False, - config_str='batchnorm-relu', - memory_efficient=False): - super(CAMDenseTDNNLayer, self).__init__() - assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format( - kernel_size) - padding = (kernel_size - 1) // 2 * dilation - self.memory_efficient = memory_efficient - self.nonlinear1 = get_nonlinear(config_str, in_channels) - self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False) - self.nonlinear2 = get_nonlinear(config_str, bn_channels) - self.cam_layer = CAMLayer(bn_channels, - out_channels, - kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias) - - def bn_function(self, x): - return self.linear1(self.nonlinear1(x)) - - def forward(self, x): - x = self.bn_function(x) - x = self.cam_layer(self.nonlinear2(x)) - return x - - -class CAMDenseTDNNBlock(nn.ModuleList): - def __init__(self, - num_layers, - in_channels, - out_channels, - bn_channels, - kernel_size, - stride=1, - dilation=1, - bias=False, - config_str='batchnorm-relu', - memory_efficient=False): - super(CAMDenseTDNNBlock, self).__init__() - for i in range(num_layers): - layer = CAMDenseTDNNLayer(in_channels=in_channels + i * out_channels, - out_channels=out_channels, - bn_channels=bn_channels, - kernel_size=kernel_size, - stride=stride, - dilation=dilation, - bias=bias, - config_str=config_str, - memory_efficient=memory_efficient) - self.add_module('tdnnd%d' % (i + 1), layer) - - def forward(self, x): - for layer in self: - x = torch.cat([x, layer(x)], dim=1) - return x - - -class TransitLayer(nn.Module): - def __init__(self, - in_channels, - out_channels, - bias=True, - config_str='batchnorm-relu'): - super(TransitLayer, self).__init__() - self.nonlinear = get_nonlinear(config_str, in_channels) - self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias) - - def forward(self, x): - x = self.nonlinear(x) - x = self.linear(x) - return x - - -class DenseLayer(nn.Module): - def __init__(self, - in_channels, - out_channels, - bias=False, - config_str='batchnorm-relu'): - super(DenseLayer, self).__init__() - self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias) - self.nonlinear = get_nonlinear(config_str, out_channels) - - def forward(self, x): - if len(x.shape) == 2: - x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1) - else: - x = self.linear(x) - x = self.nonlinear(x) - return x - - -class BasicResBlock(nn.Module): - expansion = 1 - - def __init__(self, in_planes, planes, stride=1): - super(BasicResBlock, self).__init__() - self.conv1 = nn.Conv2d(in_planes, - planes, - kernel_size=3, - stride=(stride, 1), - padding=1, - bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, - planes, - kernel_size=3, - stride=1, - padding=1, - bias=False) - self.bn2 = nn.BatchNorm2d(planes) - - self.shortcut = nn.Sequential() - if stride != 1 or in_planes != self.expansion * planes: - self.shortcut = nn.Sequential( - nn.Conv2d(in_planes, - self.expansion * planes, - kernel_size=1, - stride=(stride, 1), - bias=False), - nn.BatchNorm2d(self.expansion * planes)) - - def forward(self, x): - out = F.relu(self.bn1(self.conv1(x))) - out = self.bn2(self.conv2(out)) - out += self.shortcut(x) - out = F.relu(out) - return out - - -class FCM(nn.Module): - def __init__(self, - block=BasicResBlock, - num_blocks=[2, 2], - m_channels=32, - feat_dim=80): - super(FCM, self).__init__() - self.in_planes = m_channels - self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(m_channels) - - self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2) - self.layer2 = self._make_layer(block, m_channels, num_blocks[1], stride=2) - - self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(m_channels) - self.out_channels = m_channels * (feat_dim // 8) - - def _make_layer(self, block, planes, num_blocks, stride): - strides = [stride] + [1] * (num_blocks - 1) - layers = [] - for stride in strides: - layers.append(block(self.in_planes, planes, stride)) - self.in_planes = planes * block.expansion - return nn.Sequential(*layers) - - def forward(self, x): - x = x.unsqueeze(1) - out = F.relu(self.bn1(self.conv1(x))) - out = self.layer1(out) - out = self.layer2(out) - out = F.relu(self.bn2(self.conv2(out))) - - shape = out.shape - out = out.reshape(shape[0], shape[1] * shape[2], shape[3]) - return out - - -class CAMPPlus(nn.Module): - def __init__(self, - feat_dim=80, - embedding_size=512, - growth_rate=32, - bn_size=4, - init_channels=128, - config_str='batchnorm-relu', - memory_efficient=True): - super(CAMPPlus, self).__init__() - - self.head = FCM(feat_dim=feat_dim) - channels = self.head.out_channels - - self.xvector = nn.Sequential( - OrderedDict([ - - ('tdnn', - TDNNLayer(channels, - init_channels, - 5, - stride=2, - dilation=1, - padding=-1, - config_str=config_str)), - ])) - channels = init_channels - for i, (num_layers, kernel_size, - dilation) in enumerate(zip((12, 24, 16), (3, 3, 3), (1, 2, 2))): - block = CAMDenseTDNNBlock(num_layers=num_layers, - in_channels=channels, - out_channels=growth_rate, - bn_channels=bn_size * growth_rate, - kernel_size=kernel_size, - dilation=dilation, - config_str=config_str, - memory_efficient=memory_efficient) - self.xvector.add_module('block%d' % (i + 1), block) - channels = channels + num_layers * growth_rate - self.xvector.add_module( - 'transit%d' % (i + 1), - TransitLayer(channels, - channels // 2, - bias=False, - config_str=config_str)) - channels //= 2 - - self.xvector.add_module( - 'out_nonlinear', get_nonlinear(config_str, channels)) - - self.xvector.add_module('stats', StatsPool()) - self.xvector.add_module( - 'dense', - DenseLayer(channels * 2, embedding_size, config_str='prelu')) - - for m in self.modules(): - if isinstance(m, (nn.Conv1d, nn.Linear)): - nn.init.kaiming_normal_(m.weight.data) - if m.bias is not None: - nn.init.zeros_(m.bias) - - def forward(self, x): - x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T) - x = self.head(x) - x = self.xvector(x) - return x diff --git a/funasr/models/llm_asr/tts_models/ctc_alignment.py b/funasr/models/llm_asr/tts_models/ctc_alignment.py deleted file mode 100644 index 4d5386267..000000000 --- a/funasr/models/llm_asr/tts_models/ctc_alignment.py +++ /dev/null @@ -1,258 +0,0 @@ -import torch -import logging -import torch.nn.functional as F - - -class CTC(torch.nn.Module): - """CTC module. - - Args: - odim: dimension of outputs - encoder_output_size: number of encoder projection units - dropout_rate: dropout rate (0.0 ~ 1.0) - ctc_type: builtin or warpctc - reduce: reduce the CTC loss into a scalar - """ - - def __init__( - self, - odim: int, - encoder_output_size: int, - dropout_rate: float = 0.0, - ctc_type: str = "builtin", - reduce: bool = True, - ignore_nan_grad: bool = True, - length_normalize: str = None, - ): - super().__init__() - eprojs = encoder_output_size - self.dropout_rate = dropout_rate - self.ctc_lo = torch.nn.Linear(eprojs, odim) - self.ctc_type = ctc_type - self.ignore_nan_grad = ignore_nan_grad - self.length_normalize = length_normalize - - if self.ctc_type == "builtin": - self.ctc_loss = torch.nn.CTCLoss(reduction="none") - else: - raise ValueError( - f'ctc_type must be "builtin": {self.ctc_type}' - ) - - self.reduce = reduce - - def loss_fn(self, th_pred, th_target, th_ilen, th_olen) -> torch.Tensor: - if self.ctc_type == "builtin": - th_pred = th_pred.log_softmax(2) - loss = self.ctc_loss(th_pred, th_target, th_ilen, th_olen) - - if loss.requires_grad and self.ignore_nan_grad: - # ctc_grad: (L, B, O) - ctc_grad = loss.grad_fn(torch.ones_like(loss)) - ctc_grad = ctc_grad.sum([0, 2]) - indices = torch.isfinite(ctc_grad) - size = indices.long().sum() - if size == 0: - # Return as is - logging.warning( - "All samples in this mini-batch got nan grad." - " Returning nan value instead of CTC loss" - ) - elif size != th_pred.size(1): - logging.warning( - f"{th_pred.size(1) - size}/{th_pred.size(1)}" - " samples got nan grad." - " These were ignored for CTC loss." - ) - - # Create mask for target - target_mask = torch.full( - [th_target.size(0)], - 1, - dtype=torch.bool, - device=th_target.device, - ) - s = 0 - for ind, le in enumerate(th_olen): - if not indices[ind]: - target_mask[s : s + le] = 0 - s += le - - # Calc loss again using maksed data - loss = self.ctc_loss( - th_pred[:, indices, :], - th_target[target_mask], - th_ilen[indices], - th_olen[indices], - ) - th_ilen, th_olen = th_ilen[indices], th_olen[indices] - else: - size = th_pred.size(1) - - if self.length_normalize is not None: - if self.length_normalize == "olen": - loss = loss / th_olen - else: - loss = loss / th_ilen - - if self.reduce: - # Batch-size average - loss = loss.sum() / size - else: - loss = loss / size - return loss - - elif self.ctc_type == "warpctc": - # warpctc only supports float32 - th_pred = th_pred.to(dtype=torch.float32) - - th_target = th_target.cpu().int() - th_ilen = th_ilen.cpu().int() - th_olen = th_olen.cpu().int() - loss = self.ctc_loss(th_pred, th_target, th_ilen, th_olen) - if self.reduce: - # NOTE: sum() is needed to keep consistency since warpctc - # return as tensor w/ shape (1,) - # but builtin return as tensor w/o shape (scalar). - loss = loss.sum() - return loss - - elif self.ctc_type == "gtnctc": - log_probs = torch.nn.functional.log_softmax(th_pred, dim=2) - return self.ctc_loss(log_probs, th_target, th_ilen, 0, "none") - - else: - raise NotImplementedError - - def forward(self, hs_pad, hlens, ys_pad, ys_lens): - """Calculate CTC loss. - - Args: - hs_pad: batch of padded hidden state sequences (B, Tmax, D) - hlens: batch of lengths of hidden state sequences (B) - ys_pad: batch of padded character id sequence tensor (B, Lmax) - ys_lens: batch of lengths of character sequence (B) - """ - # hs_pad: (B, L, NProj) -> ys_hat: (B, L, Nvocab) - ys_hat = self.ctc_lo(F.dropout(hs_pad, p=self.dropout_rate)) - - if self.ctc_type == "gtnctc": - # gtn expects list form for ys - ys_true = [y[y != -1] for y in ys_pad] # parse padded ys - else: - # ys_hat: (B, L, D) -> (L, B, D) - ys_hat = ys_hat.transpose(0, 1) - # (B, L) -> (BxL,) - ys_true = torch.cat([ys_pad[i, :l] for i, l in enumerate(ys_lens)]) - - loss = self.loss_fn(ys_hat, ys_true, hlens, ys_lens).to( - device=hs_pad.device, dtype=hs_pad.dtype - ) - - return loss - - def softmax(self, hs_pad): - """softmax of frame activations - - Args: - Tensor hs_pad: 3d tensor (B, Tmax, eprojs) - Returns: - torch.Tensor: softmax applied 3d tensor (B, Tmax, odim) - """ - return F.softmax(self.ctc_lo(hs_pad), dim=2) - - def log_softmax(self, hs_pad): - """log_softmax of frame activations - - Args: - Tensor hs_pad: 3d tensor (B, Tmax, eprojs) - Returns: - torch.Tensor: log softmax applied 3d tensor (B, Tmax, odim) - """ - return F.log_softmax(self.ctc_lo(hs_pad), dim=2) - - def argmax(self, hs_pad): - """argmax of frame activations - - Args: - torch.Tensor hs_pad: 3d tensor (B, Tmax, eprojs) - Returns: - torch.Tensor: argmax applied 2d tensor (B, Tmax) - """ - return torch.argmax(self.ctc_lo(hs_pad), dim=2) - - -def ctc_forced_align( - log_probs: torch.Tensor, - targets: torch.Tensor, - input_lengths: torch.Tensor, - target_lengths: torch.Tensor, - blank: int = 0, - ignore_id: int = -1, -) -> torch.Tensor: - """Align a CTC label sequence to an emission. - - Args: - log_probs (Tensor): log probability of CTC emission output. - Tensor of shape `(B, T, C)`. where `B` is the batch size, `T` is the input length, - `C` is the number of characters in alphabet including blank. - targets (Tensor): Target sequence. Tensor of shape `(B, L)`, - where `L` is the target length. - input_lengths (Tensor): - Lengths of the inputs (max value must each be <= `T`). 1-D Tensor of shape `(B,)`. - target_lengths (Tensor): - Lengths of the targets. 1-D Tensor of shape `(B,)`. - blank_id (int, optional): The index of blank symbol in CTC emission. (Default: 0) - ignore_id (int, optional): The index of ignore symbol in CTC emission. (Default: -1) - """ - targets[targets == ignore_id] = blank - - batch_size, input_time_size, _ = log_probs.size() - bsz_indices = torch.arange(batch_size, device=input_lengths.device) - - _t_a_r_g_e_t_s_ = torch.cat( - ( - torch.stack((torch.full_like(targets, blank), targets), dim=-1).flatten(start_dim=1), - torch.full_like(targets[:, :1], blank), - ), - dim=-1, - ) - diff_labels = torch.cat( - ( - torch.as_tensor([[False, False]], device=targets.device).expand(batch_size, -1), - _t_a_r_g_e_t_s_[:, 2:] != _t_a_r_g_e_t_s_[:, :-2], - ), - dim=1, - ) - - neg_inf = torch.tensor(float("-inf"), device=log_probs.device, dtype=log_probs.dtype) - padding_num = 2 - padded_t = padding_num + _t_a_r_g_e_t_s_.size(-1) - best_score = torch.full((batch_size, padded_t), neg_inf, device=log_probs.device, dtype=log_probs.dtype) - best_score[:, padding_num + 0] = log_probs[:, 0, blank] - best_score[:, padding_num + 1] = log_probs[bsz_indices, 0, _t_a_r_g_e_t_s_[:, 1]] - - backpointers = torch.zeros((batch_size, input_time_size, padded_t), device=log_probs.device, dtype=targets.dtype) - - for t in range(1, input_time_size): - prev = torch.stack( - (best_score[:, 2:], best_score[:, 1:-1], torch.where(diff_labels, best_score[:, :-2], neg_inf)) - ) - prev_max_value, prev_max_idx = prev.max(dim=0) - best_score[:, padding_num:] = log_probs[:, t].gather(-1, _t_a_r_g_e_t_s_) + prev_max_value - backpointers[:, t, padding_num:] = prev_max_idx - - l1l2 = best_score.gather( - -1, torch.stack((padding_num + target_lengths * 2 - 1, padding_num + target_lengths * 2), dim=-1) - ) - - path = torch.zeros((batch_size, input_time_size), device=best_score.device, dtype=torch.long) - path[bsz_indices, input_lengths - 1] = padding_num + target_lengths * 2 - 1 + l1l2.argmax(dim=-1) - - for t in range(input_time_size - 1, 0, -1): - target_indices = path[:, t] - prev_max_idx = backpointers[bsz_indices, t, target_indices] - path[:, t - 1] += target_indices - prev_max_idx - - alignments = _t_a_r_g_e_t_s_.gather(dim=-1, index=(path - padding_num).clamp(min=0)) - return alignments diff --git a/funasr/models/llm_asr/tts_models/e2e_model.py b/funasr/models/llm_asr/tts_models/e2e_model.py deleted file mode 100644 index 482328407..000000000 --- a/funasr/models/llm_asr/tts_models/e2e_model.py +++ /dev/null @@ -1,1032 +0,0 @@ -import logging -import time - -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import List, Tuple, Dict, Optional, Union -from funasr.train_utils.device_funcs import force_gatherable -from funasr.utils.hinter import hint_once -from funasr.models.transformer.utils.nets_utils import pad_list -import numpy as np -import random -from funasr.train_utils.set_all_random_seed import set_all_random_seed - - -def norm_and_sample_xvec(xvec, xvec_lengths): - xvec_list = [] - for i, ilen in enumerate(xvec_lengths): - idx = random.randint(0, ilen - 1) - while torch.any(~torch.isfinite(xvec[i, idx])): - idx = random.randint(0, ilen - 1) - xvec_list.append(xvec[i, idx]) - rand_xvec = torch.vstack(xvec_list) - rand_xvec = F.normalize(rand_xvec, dim=1) - - return rand_xvec - - -class UpsampleCtcTokenDiffModel(nn.Module): - def __init__( - self, - input_size: int, - output_size: int, - vocab_size: int, - token_list: list, - token_vocab_size: int, - endofprompt_token_id: int = None, - text_encoder_conf: dict = None, - aggregator_conf: dict = None, - am_config: dict = None, - fm_config: dict = None, - xvec_size: int = None, - **kwargs - ): - super().__init__() - self.input_size = input_size - self.output_size = output_size - self.text_vocab_size = vocab_size - self.token_list = token_list - self.token_vocab_size = token_vocab_size - self.endofprompt_token_id = endofprompt_token_id - self.text_encoder_conf = text_encoder_conf - self.aggregator_conf = aggregator_conf - self.am_config = am_config - self.fm_config = fm_config - self.xvec_size = xvec_size - - # build nn - self.text_embedding = nn.Embedding(vocab_size, output_size) - self.xvec_proj = None - if xvec_size is not None: - self.xvec_proj = nn.Linear(xvec_size, output_size) - self.text_encoder = self.build_text_encoder() - self.am_model = self.build_am_model() - self.fm_model = self.build_fm_model() - self.am_aggregator = self.build_aggregator() - self.fm_aggregator = self.build_aggregator() - - # set optional parameters - self.xvec_drop_rate = kwargs.get('xvec_drop_rate', None) - self.use_prompt_as_xvec = kwargs.get('use_prompt_as_xvec', False) - if self.use_prompt_as_xvec: - self.spk_aggregator = self.build_aggregator() - spk_query = torch.randn(1, 1, self.output_size) - torch.nn.init.xavier_normal_(spk_query) - self.spk_query = torch.nn.Parameter(spk_query, requires_grad=True) - - def build_aggregator(self): - name = self.aggregator_conf.pop("name", None) - model = None - if name == "transformer": - from funasr.models.llm_asr.tts_models.transformer_decoder import TransformerDecoder - model = TransformerDecoder(self.output_size, **self.aggregator_conf) - self.aggregator_conf["name"] = name - - return model - - def build_text_encoder(self): - name = self.text_encoder_conf.pop("name", None) - model = None - if name == "transformer": - from funasr.models.llm_asr.conformer_encoder import ConformerEncoder - model = ConformerEncoder( - **self.text_encoder_conf, - input_size=self.output_size, - use_cnn_module=False, - macaron_style=False, - ) - elif name == "conformer": - from funasr.models.llm_asr.conformer_encoder import ConformerEncoder - model = ConformerEncoder( - **self.text_encoder_conf, - input_size=self.output_size, - ) - - self.text_encoder_conf["name"] = name - return model - - def build_am_model(self): - name = self.am_config.pop("name", None) - model = None - if name == "nar_ctc_model": - from funasr.models.llm_asr.tts_models.nar_acoustic_model import NARCTCModel - model = NARCTCModel(**self.am_config) - elif name == "nar_ctc_prob_model": - from funasr.models.llm_asr.tts_models.nar_acoustic_model import NARCTCProbModel - model = NARCTCProbModel(**self.am_config) - - self.am_config["name"] = name - return model - - def build_fm_model(self): - name = self.fm_config.pop("name", None) - model = None - if name == "masked_diff_with_xvec": - from funasr.models.llm_asr.flow_matching import MaskedDiffWithXvec - model = MaskedDiffWithXvec(**self.fm_config) - - self.fm_config["name"] = name - return model - - def split_prompt( - self, - text_emb: torch.Tensor, - text_emb_lens: torch.Tensor, - text: torch.Tensor, - text_lens: torch.Tensor, - ): - prompts, prompt_lens = [], [] - outs, outs_lens = [], [] - batch_size = text.shape[0] - for i in range(batch_size): - delta = text_emb_lens[i] - text_lens[i] - # 1 for exclude <|endofprompt|> token - pos = torch.where(text[i] == self.endofprompt_token_id)[0][0].item() + delta + 1 - _x = text_emb[i, pos:text_emb_lens[i]] - outs.append(_x) - outs_lens.append(_x.shape[0]) - - _prompt = text_emb[i, :pos] - prompts.append(_prompt) - prompt_lens.append(_prompt.shape[0]) - - outs = pad_list(outs, pad_value=0.0) - outs_lens = torch.tensor(outs_lens, dtype=torch.int64, device=text_emb.device) - - prompts = pad_list(prompts, pad_value=0.0) - prompt_lens = torch.tensor(prompt_lens, dtype=torch.int64, device=text_emb.device) - - return prompts, prompt_lens, outs, outs_lens - - def forward( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - speech_token: torch.Tensor, - speech_token_lengths: torch.Tensor, - audio: torch.Tensor, - audio_lengths: torch.Tensor, - xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, - **kwargs - ): - text = text[:, :text_lengths.max()] - speech_token = speech_token[:, :speech_token_lengths.max()] - audio = audio[:, :audio_lengths.max()] - batch_size = text.shape[0] - - # embed text inputs - mask = (text != -1).float().unsqueeze(-1) - text_emb = self.text_embedding(torch.clamp(text, min=0)) * mask - text_emb_lengths = text_lengths - - prompt, prompt_lens, text_emb, text_emb_lengths = self.split_prompt( - text_emb, text_emb_lengths, text, text_lengths - ) - - if self.use_prompt_as_xvec: - prompt_xvec, _ = self.spk_aggregator( - prompt, prompt_lens, - self.spk_query.expand([batch_size, -1, -1]), torch.tensor([1]*batch_size).to(prompt_lens) - ) - endofprompt_emb = self.text_embedding(torch.tensor([self.endofprompt_token_id]*batch_size).to(text).unsqueeze(1)) - prompt = torch.cat([prompt_xvec, endofprompt_emb], dim=1) - prompt_lens = torch.tensor([2]*batch_size).to(prompt_lens) - hint_once("using prompt as speaker embedding.", "use_prompt_spk_emb") - - # random select a xvec from xvec matrix - if not self.use_prompt_as_xvec and self.xvec_proj is not None and xvec is not None: - xvec = xvec[:, :xvec_lengths.max()] - rand_xvec = norm_and_sample_xvec(xvec, xvec_lengths) - rand_xvec = self.xvec_proj(rand_xvec) - if self.xvec_drop_rate is not None: - xvec_mask = (torch.rand((rand_xvec.shape[0], 1)) >= self.xvec_drop_rate).to(rand_xvec) - rand_xvec = rand_xvec * xvec_mask - hint_once(f"randomly drop out xvec with mask {xvec_mask.squeeze()}", "xvec_drop_out") - rand_xvec = rand_xvec.unsqueeze(1) - prompt = torch.cat([rand_xvec, prompt], dim=1) - prompt_lens = prompt_lens + 1 - hint_once("using speaker embedding as slot.", "use_spk_emb") - - # remove prompt text - # prompt, prompt_lens, text_emb, text_emb_lengths = self.split_prompt( - # text_emb, text_emb_lengths, text, text_lengths - # ) - outs_tuple = self.text_encoder(text_emb, ilens=text_emb_lengths) - text_enc = outs_tuple[0] - text_enc_lens = text_emb_lengths - text_enc, _ = self.am_aggregator( - prompt, prompt_lens, - text_enc, text_enc_lens - ) - - states = dict( - batch_size=float(batch_size), - text_len=float(text_emb.shape[1]), - speech_len=float(speech_token.shape[1]), - token_text_ratio=float(speech_token.shape[1]) / float(text_emb.shape[1]), - ) - # forward AM model - am_retvals = self.am_model.force_align_text( - speech_token, speech_token_lengths, - text_enc, text_enc_lens, - **kwargs - ) - am_loss, aligned_token_emb, am_states = am_retvals - # update AM states - for key, val in am_states.items(): - states[f"am_{key}"] = val - - aligned_token_emb, _ = self.fm_aggregator( - prompt, prompt_lens, - aligned_token_emb, speech_token_lengths - ) - - # forward FM model - fm_loss, fm_states, _ = self.fm_model.forward( - aligned_token_emb, speech_token_lengths, - audio, audio_lengths, - **kwargs, - ) - # update FM states - for key, val in fm_states.items(): - states[f"fm_{key}"] = val - - loss = am_loss + fm_loss - states["loss"] = loss.item() - - loss, states, weight = force_gatherable((loss, states, batch_size), loss.device) - - return loss, states, weight - - def inference( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, - **kwargs - ): - blank_penalty = kwargs.get("blank_penalty", 0.0) - sampling = kwargs.get("sampling", "greedy") - prompt_dict = kwargs.get("prompt_dict", {}) - prompt_token = prompt_dict.get("prompt_token", (None, None)) - prompt_audio = prompt_dict.get("prompt_audio", (None, None)) - # fully un-causal mode - use_causal_prob = kwargs.get("use_causal_prob", 1.0) - # embed text inputs - mask = (text != -1).float().unsqueeze(-1) - text_emb = self.text_embedding(torch.clamp(text, min=0)) * mask - text_emb_lengths = text_lengths - batch_size = text.shape[0] - - prompt, prompt_lens, text_emb, text_emb_lengths = self.split_prompt( - text_emb, text_emb_lengths, text, text_lengths - ) - - if self.use_prompt_as_xvec: - prompt_xvec, _ = self.spk_aggregator( - prompt, prompt_lens, - self.spk_query.expand([batch_size, -1, -1]), torch.tensor([1] * batch_size).to(prompt_lens) - ) - endofprompt_emb = self.text_embedding( - torch.tensor([self.endofprompt_token_id] * batch_size).to(text).unsqueeze(1)) - prompt = torch.cat([prompt_xvec, endofprompt_emb], dim=1) - prompt_lens = torch.tensor([2] * batch_size).to(prompt_lens) - hint_once("using prompt as speaker embedding.", "use_prompt_spk_emb") - - # using the xvec - if self.xvec_proj is not None and not self.use_prompt_as_xvec: - if xvec is not None: - hint_once("using speaker embedding as slot.", "use_spk_emb") - xvec = xvec[:, :xvec_lengths.max()] - rand_xvec = norm_and_sample_xvec(xvec, xvec_lengths) - rand_xvec = self.xvec_proj(rand_xvec) - rand_xvec = rand_xvec.unsqueeze(1) - else: - hint_once("using zeros as speaker embedding.", "use_spk_emb") - rand_xvec = torch.zeros([text_emb.shape[0], 1, self.output_size]).to(text_emb) - prompt = torch.cat([rand_xvec, prompt], dim=1) - prompt_lens = prompt_lens + 1 - - outs_tuple = self.text_encoder(text_emb, ilens=text_emb_lengths) - text_enc = outs_tuple[0] - text_enc_lens = text_emb_lengths - text_enc, _ = self.am_aggregator( - prompt, prompt_lens, - text_enc, text_enc_lens - ) - - # forward AM model - tokens, aligned_token_emb, aligned_token_lens = self.am_model.inference( - text_enc, text_enc_lens, - sampling=sampling, - blank_penalty=blank_penalty, - text_is_embedding=True, - return_hidden=True, - use_causal_prob=use_causal_prob, - ) - if isinstance(tokens, tuple): - tokens, fa_tokens = tokens - - aligned_token_emb, _ = self.fm_aggregator( - prompt, prompt_lens, - aligned_token_emb, aligned_token_lens - ) - - # forward FM model - set_all_random_seed(0) - feat = self.fm_model.inference( - aligned_token_emb, aligned_token_lens, - prompt=dict( - prompt_text=prompt_token, - prompt_audio=prompt_audio, - ), - **kwargs, - ) - feat = self.rms_rescale_feat(feat) - - return tokens, feat - - def rms_rescale_feat(self, feat, target_feat_rms=3.5, feat_sil_th=0.1): - feat_power = feat.exp().sum(1) - # not silence - if feat_power.max() > feat_sil_th: - mask = feat_power > feat_sil_th - feat_rms = torch.sqrt(torch.mean(torch.square(feat_power))) - feat = feat + mask.unsqueeze(1) * np.log(target_feat_rms / feat_rms.cpu().numpy().item()) - - return feat - - def get_hop_lens(self, fa_tokens, lookahead_size): - if lookahead_size == 0: - return 0, 0 - - fa_tokens = fa_tokens[0].cpu().tolist() - upsample_rate = np.cumprod(self.am_model.encoder.upsample_ratios)[-1] - lookahead_tokens = [[x-1] for x in fa_tokens[-lookahead_size*upsample_rate:] if x > 0] - lookahead_token_len = len(lookahead_tokens) - return lookahead_token_len - - def streaming_inference( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, - **kwargs - ): - device = text.device - use_causal_prob = kwargs.get("use_causal_prob", 1.0) - # streaming related config - chunk_size = kwargs.get("streaming_chunk_size", 1) - chunk_size_maxium = kwargs.get("chunk_size_maxium", 16) - try: - lookahead_size = self.am_model.encoder.pre_lookahead_len - except AttributeError: - lookahead_size = 0 - hint_once(f"chunk_size={chunk_size}, chunk_size_maxium={chunk_size_maxium}, " - f"pre lookahead size={lookahead_size}.", - "pre_lookahead_len") - given_rtf = kwargs.get("given_rtf", 0.5) - - blank_penalty = kwargs.get("blank_penalty", 0.0) - sampling = kwargs.get("sampling", "greedy") - prompt_dict = kwargs.get("prompt_dict", {}) - prompt_token = list(prompt_dict.get("prompt_token", [None, None])) - prompt_audio = list(prompt_dict.get("prompt_audio", [None, None])) - streaming_mode = kwargs.get("streaming_mode", "v2") - - if prompt_token[0] is None: - prompt_token[0] = torch.zeros([1, 0, self.output_size], device=device, dtype=torch.float32) - prompt_token[1] = torch.tensor([0], device=device, dtype=torch.long) - if prompt_audio[0] is None: - prompt_audio[0] = torch.zeros( - [1, 0, self.fm_model.mel_extractor.num_mels], - device=device, dtype=torch.float32 - ) - prompt_audio[1] = torch.tensor([0], device=device, dtype=torch.long) - - # embed text inputs - mask = (text != -1).float().unsqueeze(-1) - text_emb = self.text_embedding(torch.clamp(text, min=0)) * mask - text_emb_lengths = text_lengths - - batch_size = text.shape[0] - - prompt, prompt_lens, text_emb, text_emb_lengths = self.split_prompt( - text_emb, text_emb_lengths, text, text_lengths - ) - - if self.use_prompt_as_xvec: - prompt_xvec, _ = self.spk_aggregator( - prompt, prompt_lens, - self.spk_query.expand([batch_size, -1, -1]), torch.tensor([1] * batch_size).to(prompt_lens) - ) - endofprompt_emb = self.text_embedding( - torch.tensor([self.endofprompt_token_id] * batch_size).to(text).unsqueeze(1)) - prompt = torch.cat([prompt_xvec, endofprompt_emb], dim=1) - prompt_lens = torch.tensor([2] * batch_size).to(prompt_lens) - hint_once("using prompt as speaker embedding.", "use_prompt_spk_emb") - - # using the xvec - if self.xvec_proj is not None and not self.use_prompt_as_xvec: - if xvec is not None: - hint_once("using speaker embedding as slot.", "use_spk_emb") - xvec = xvec[:, :xvec_lengths.max()] - rand_xvec = norm_and_sample_xvec(xvec, xvec_lengths) - rand_xvec = self.xvec_proj(rand_xvec) - rand_xvec = rand_xvec.unsqueeze(1) - else: - hint_once("using zeros as speaker embedding.", "use_spk_emb") - rand_xvec = torch.zeros([text_emb.shape[0], 1, self.output_size]).to(text_emb) - prompt = torch.cat([rand_xvec, prompt], dim=1) - prompt_lens = prompt_lens + 1 - - chunk_id = 0 - chunk_start = 0 - while True: - _st_time = time.time() - _size = max(int(round(chunk_size / (given_rtf ** chunk_id))), chunk_size_maxium) - chunk_end = chunk_start + _size - chunk_text_emb = text_emb[:, :chunk_end+lookahead_size] - chunk_text_emb_lengths = torch.tensor([chunk_text_emb.shape[1]], dtype=torch.long, device=device) - - outs_tuple = self.text_encoder(chunk_text_emb, ilens=chunk_text_emb_lengths) - text_enc = outs_tuple[0] - text_enc_lens = chunk_text_emb_lengths - text_enc, _ = self.am_aggregator( - prompt, prompt_lens, - text_enc, text_enc_lens - ) - - # forward AM model - tokens, aligned_token_emb, aligned_token_lens = self.am_model.inference( - text_enc, text_enc_lens, - sampling=sampling, - blank_penalty=blank_penalty, - text_is_embedding=True, - return_hidden=True, - use_causal_prob=use_causal_prob, - ) - token_hop_len, mel_hop_len = 0, 0 - if isinstance(tokens, tuple): - tokens, fa_tokens = tokens - token_hop_len = self.get_hop_lens(fa_tokens, lookahead_size) - mel_hop_len = int(round(token_hop_len * self.fm_model.length_normalizer_ratio)) - - # exclude empty tokens. - if aligned_token_emb.shape[1] > prompt_token[0].shape[1]: - aligned_token_emb, _ = self.fm_aggregator( - prompt, prompt_lens, - aligned_token_emb, aligned_token_lens - ) - cur_token = aligned_token_emb[:, prompt_token[0].shape[1]:] - cur_token_len = aligned_token_lens - prompt_token[1] - - # v2: excluding lookahead tokens for not-last packages - if streaming_mode == "v2": - if chunk_end + lookahead_size < text_emb.shape[1]: - cur_token = cur_token[:, :cur_token.shape[1]-token_hop_len, :] - cur_token_len = cur_token_len - token_hop_len - - # forward FM model - feat = self.fm_model.inference( - cur_token, cur_token_len, - prompt=dict( - prompt_text=prompt_token, - prompt_audio=prompt_audio, - ), - **kwargs, - ) - feat = self.rms_rescale_feat(feat) - cost = time.time() - _st_time - if chunk_id == 0: - logging.info(f"First package delay: {cost*1000.0:.2f}ms") - print_token = tokens.cpu().squeeze().tolist() - logging.info(f"pack {chunk_id}: valid_tokens: {print_token[:len(print_token)-token_hop_len]}, " - f"pad_tokens: {print_token[len(print_token)-token_hop_len:]}.") - - if streaming_mode == "v1": - # v1: excluding lookahead parts for not-last packages - if chunk_end + lookahead_size < text_emb.shape[1]: - cur_token = cur_token[:, :cur_token.shape[1]-token_hop_len, :] - feat = feat[:, :, :feat.shape[2] - mel_hop_len] - - if streaming_mode == "v2": - # v2: reback token and mel feat - if chunk_end + lookahead_size < text_emb.shape[1]: - text_reback = 2 if chunk_id == 0 else 4 - token_hop_len_2 = self.get_hop_lens(fa_tokens, lookahead_size + text_reback) - token_reback = token_hop_len_2 - token_hop_len - cur_token = cur_token[:, :cur_token.shape[1] - token_reback, :] - feat_reback = int(round(token_reback * self.fm_model.length_normalizer_ratio)) - feat = feat[:, :, :feat.shape[2] - feat_reback] - chunk_end = chunk_end - text_reback - - # update values and lens of prompt token and audio - prompt_token[1] = prompt_token[1] + cur_token.shape[1] - prompt_token[0] = torch.concat([prompt_token[0], cur_token], dim=1) - prompt_audio[1] = prompt_audio[1] + feat.shape[2] - prompt_audio[0] = torch.concat([prompt_audio[0], feat.transpose(1, 2)], dim=1) - - chunk_id += 1 - chunk_start = chunk_end - if chunk_end + lookahead_size >= text_emb.shape[1]: - break - - return tokens, prompt_audio[0].transpose(1, 2) - - def collect_feats(self, **batch: torch.Tensor) -> Dict[str, torch.Tensor]: - pass - - -class UCTDXvecSlotModel(UpsampleCtcTokenDiffModel): - def __init__(self, input_size: int, output_size: int, vocab_size: int, token_list: list, token_vocab_size: int, - endofprompt_token_id: int = None, text_encoder_conf: dict = None, aggregator_conf: dict = None, - am_config: dict = None, fm_config: dict = None, xvec_size: int = None, **kwargs): - super().__init__(input_size, output_size, vocab_size, token_list, token_vocab_size, endofprompt_token_id, - text_encoder_conf, aggregator_conf, am_config, fm_config, xvec_size, **kwargs) - # remove am and fm aggregator - self.am_aggregator = None - self.fm_aggregator = None - - # build speaker aggregator for Prompt Text - self.spk_aggregator = self.build_aggregator() - spk_query = torch.randn(1, 1, self.output_size) - torch.nn.init.xavier_normal_(spk_query) - self.spk_query = torch.nn.Parameter(spk_query, requires_grad=True) - self.prompt_xvec_proj = nn.Linear(self.output_size, self.xvec_size) - self.outside_prompt_dim = kwargs.get("outside_prompt_dim", None) - if self.outside_prompt_dim is not None: - self.outside_prompt_poj = nn.Linear(self.outside_prompt_dim, self.output_size) - - def forward(self, text: torch.Tensor, text_lengths: torch.Tensor, speech_token: torch.Tensor, - speech_token_lengths: torch.Tensor, audio: torch.Tensor, audio_lengths: torch.Tensor, - xvec: Optional[torch.Tensor] = None, xvec_lengths: Optional[torch.Tensor] = None, **kwargs): - text = text[:, :text_lengths.max()] - speech_token = speech_token[:, :speech_token_lengths.max()] - audio = audio[:, :audio_lengths.max()] - batch_size = text.shape[0] - - # embed text inputs - mask = (text != -1).float().unsqueeze(-1) - text_emb = self.text_embedding(torch.clamp(text, min=0)) * mask - text_emb_lengths = text_lengths - - prompt, prompt_lens, text_emb, text_emb_lengths = self.split_prompt( - text_emb, text_emb_lengths, text, text_lengths - ) - if "outside_prompt" in kwargs and "outside_prompt_lengths" in kwargs: - prompt = kwargs["outside_prompt"] - prompt_lens = kwargs["outside_prompt_lengths"] - prompt = self.outside_prompt_poj(prompt) - hint_once("use outside_prompt", "outside_prompt") - - # textual prompt xvec - if self.text_mel_xvec_rand_ratios[0] > 0: - prompt_xvec = self.spk_aggregator( - prompt, prompt_lens, - self.spk_query.expand([batch_size, -1, -1]), torch.tensor([1] * batch_size).to(prompt_lens) - )[0] - prompt_xvec = self.prompt_xvec_proj(prompt_xvec) - else: - prompt_xvec = torch.zeros([batch_size, 1, self.xvec_size]).to(text_emb) - - # mel prompt xvec - if self.text_mel_xvec_rand_ratios[1] > 0: - audio_rand_lens = torch.rand_like(audio_lengths, dtype=torch.float32) * ( - self.audio_prompt_lens[1] - self.audio_prompt_lens[0]) + self.audio_prompt_lens[0] - audio_rand_lens = (audio_rand_lens * audio_lengths).round().long() - audio_rand_lens = torch.clamp(audio_rand_lens, min=round(self.mel_spec_fn.sampling_rate * 0.5)) - audio_prompt = [audio[i, :audio_rand_lens[i]] for i in range(batch_size)] - audio_rand_lens = torch.tensor([x.shape[0] for x in audio_prompt]).to(text_lengths) - audio_prompt = pad_list(audio_prompt, 0.0) - mel_feat, feat_lens = self.mel_spec_fn(audio_prompt, audio_rand_lens) - mel_xvec = self.mel_xvec_fn(mel_feat).unsqueeze(1) - else: - mel_xvec = torch.zeros([batch_size, 1, self.xvec_size]).to(text_emb) - - if xvec is not None: - # random select a xvec from xvec matrix - xvec = xvec[:, :xvec_lengths.max()] - else: - xvec = torch.zeros([batch_size, 1, self.xvec_size]).to(text_emb) - - # random using prompt, mel and input xvecs - mixup_rand = torch.tensor(self.text_mel_xvec_rand_ratios).to(text_emb).multinomial(batch_size, - replacement=True).unsqueeze( - 1).unsqueeze(2) - hint_once(f"xvec mixup prob: {mixup_rand}", "xvec mixup prob") - rand_xvec = ( - (mixup_rand == 0) * prompt_xvec + - (mixup_rand == 1) * mel_xvec + - (mixup_rand == 2) * xvec - ) - rand_xvec_lens = torch.tensor([1] * batch_size).to(xvec_lengths) - - outs_tuple = self.text_encoder(text_emb, ilens=text_emb_lengths) - text_enc = outs_tuple[0] - text_enc_lens = text_emb_lengths - - states = dict( - batch_size=float(batch_size), - text_len=float(text_emb.shape[1]), - speech_len=float(speech_token.shape[1]), - token_text_ratio=float(speech_token.shape[1]) / float(text_emb.shape[1]), - ) - # forward AM model - am_retvals = self.am_model.force_align_text( - speech_token, speech_token_lengths, - text_enc, text_enc_lens, - rand_xvec, rand_xvec_lens, - **kwargs - ) - am_loss, aligned_token_emb, am_states = am_retvals - # update AM states - for key, val in am_states.items(): - states[f"am_{key}"] = val - - # forward FM model - fm_loss, fm_states, _ = self.fm_model.forward( - aligned_token_emb, speech_token_lengths, - audio, audio_lengths, - rand_xvec, rand_xvec_lens, - **kwargs, - ) - # update FM states - for key, val in fm_states.items(): - states[f"fm_{key}"] = val - - loss = am_loss + fm_loss - states["loss"] = loss.item() - - loss, states, weight = force_gatherable((loss, states, batch_size), loss.device) - - return loss, states, weight - - def inference(self, text: torch.Tensor, text_lengths: torch.Tensor, xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, **kwargs): - blank_penalty = kwargs.get("blank_penalty", 0.0) - sampling = kwargs.get("sampling", "greedy") - prompt_dict = kwargs.get("prompt_dict", {}) - prompt_token = prompt_dict.get("prompt_token", (None, None)) - prompt_audio = prompt_dict.get("prompt_audio", (None, None)) - # fully un-causal mode - use_causal_prob = kwargs.get("use_causal_prob", 1.0) - # embed text inputs - mask = (text != -1).float().unsqueeze(-1) - text_emb = self.text_embedding(torch.clamp(text, min=0)) * mask - text_emb_lengths = text_lengths - batch_size = text.shape[0] - - prompt, prompt_lens, text_emb, text_emb_lengths = self.split_prompt( - text_emb, text_emb_lengths, text, text_lengths - ) - if "outside_prompt" in kwargs: - prompt = kwargs["outside_prompt"].to(text.device) - if "outside_prompt_lengths" in kwargs: - prompt_lens = kwargs["outside_prompt_lengths"] - else: - prompt_lens = torch.tensor([prompt.shape[1]]).to(text_lengths) - prompt = self.outside_prompt_poj(prompt) - hint_once("use outside_prompt", "outside_prompt") - - if xvec is not None: - # using the xvec - hint_once("using speaker embedding for slot.", "use_spk_emb") - xvec = xvec[:, :xvec_lengths.max()] - else: - # textual prompt xvec - hint_once("using textual prompt for slot.", "use_spk_emb") - prompt_xvec = self.spk_aggregator( - prompt, prompt_lens, - self.spk_query.expand([batch_size, -1, -1]), torch.tensor([1] * batch_size).to(prompt_lens) - )[0] - xvec = self.prompt_xvec_proj(prompt_xvec) - xvec_lengths = torch.tensor([1] * batch_size).to(text_lengths) - - outs_tuple = self.text_encoder(text_emb, ilens=text_emb_lengths) - text_enc = outs_tuple[0] - text_enc_lens = text_emb_lengths - - # forward AM model - tokens, aligned_token_emb, aligned_token_lens = self.am_model.inference( - text_enc, text_enc_lens, - xvec, xvec_lengths, - sampling=sampling, - blank_penalty=blank_penalty, - text_is_embedding=True, - return_hidden=True, - use_causal_prob=use_causal_prob, - ) - if isinstance(tokens, tuple): - tokens, fa_tokens = tokens - - # forward FM model - feat = self.fm_model.inference( - aligned_token_emb, aligned_token_lens, - xvec, xvec_lengths, - prompt=dict( - prompt_text=prompt_token, - prompt_audio=prompt_audio, - ), - **kwargs, - ) - feat = self.rms_rescale_feat(feat) - - return tokens, feat - - def streaming_inference(self, text: torch.Tensor, text_lengths: torch.Tensor, xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, **kwargs): - device = text.device - use_causal_prob = kwargs.get("use_causal_prob", 1.0) - # streaming related config - chunk_size = kwargs.get("streaming_chunk_size", 4) - chunk_size_maxium = kwargs.get("chunk_size_maxium", 16) - try: - lookahead_size = self.am_model.encoder.pre_lookahead_len - except AttributeError: - lookahead_size = 0 - hint_once(f"chunk_size={chunk_size}, chunk_size_maxium={chunk_size_maxium}, " - f"pre lookahead size={lookahead_size}.", - "pre_lookahead_len") - given_rtf = kwargs.get("given_rtf", 0.5) - - blank_penalty = kwargs.get("blank_penalty", 0.0) - sampling = kwargs.get("sampling", "greedy") - prompt_dict = kwargs.get("prompt_dict", {}) - prompt_token = list(prompt_dict.get("prompt_token", [None, None])) - prompt_audio = list(prompt_dict.get("prompt_audio", [None, None])) - streaming_mode = kwargs.get("streaming_mode", "v2") - - ftype = self.text_embedding.weight.dtype - if prompt_token[0] is None: - prompt_token[0] = torch.zeros([1, 0, self.output_size], device=device, dtype=ftype) - prompt_token[1] = torch.tensor([0], device=device, dtype=torch.long) - if prompt_audio[0] is None: - prompt_audio[0] = torch.zeros( - [1, 0, self.fm_model.mel_extractor.num_mels], - device=device, dtype=ftype - ) - prompt_audio[1] = torch.tensor([0], device=device, dtype=torch.long) - - # embed text inputs - mask = (text != -1).float().unsqueeze(-1) - text_emb = self.text_embedding(torch.clamp(text, min=0)) * mask - text_emb_lengths = text_lengths - - batch_size = text.shape[0] - - prompt, prompt_lens, text_emb, text_emb_lengths = self.split_prompt( - text_emb, text_emb_lengths, text, text_lengths - ) - if "outside_prompt" in kwargs: - prompt = kwargs["outside_prompt"].to(device) - if "outside_prompt_lengths" in kwargs: - prompt_lens = kwargs["outside_prompt_lengths"] - else: - prompt_lens = torch.tensor([prompt.shape[1]]).to(text_lengths) - prompt = self.outside_prompt_poj(prompt) - hint_once("use outside_prompt", "outside_prompt") - - if xvec is not None: - # using speaker embedding - hint_once("using speaker embedding for slot.", "use_spk_emb") - xvec = xvec[:, :xvec_lengths.max()] - else: - # textual prompt xvec - hint_once("using textual prompt for slot.", "use_spk_emb") - prompt_xvec = self.spk_aggregator( - prompt, prompt_lens, - self.spk_query.expand([batch_size, -1, -1]), torch.tensor([1] * batch_size).to(prompt_lens) - )[0] - xvec = self.prompt_xvec_proj(prompt_xvec) - xvec_lengths = torch.tensor([1] * batch_size).to(text_lengths) - - chunk_id = 0 - chunk_start = 0 - while True: - _st_time = time.time() - _size = max(int(round(chunk_size / (given_rtf ** chunk_id))), chunk_size_maxium) - chunk_end = chunk_start + _size - chunk_text_emb = text_emb[:, :chunk_end + lookahead_size] - chunk_text_emb_lengths = torch.tensor([chunk_text_emb.shape[1]], dtype=torch.long, device=device) - - text_enc_st_time = time.time() - outs_tuple = self.text_encoder(chunk_text_emb, ilens=chunk_text_emb_lengths) - if chunk_id == 0: - logging.info(f"text_enc cost time: {(time.time() - text_enc_st_time) * 1000.0:.2f} ms") - text_enc = outs_tuple[0] - text_enc_lens = chunk_text_emb_lengths - - # forward AM model - am_st_time = time.time() - tokens, aligned_token_emb, aligned_token_lens = self.am_model.inference( - text_enc, text_enc_lens, - xvec, xvec_lengths, - sampling=sampling, - blank_penalty=blank_penalty, - text_is_embedding=True, - return_hidden=True, - use_causal_prob=use_causal_prob, - ) - if chunk_id == 0: - logging.info(f"am cost time: {(time.time() - am_st_time) * 1000.0:.2f} ms") - token_hop_len, mel_hop_len = 0, 0 - if isinstance(tokens, tuple): - tokens, fa_tokens = tokens - token_hop_len = self.get_hop_lens(fa_tokens, lookahead_size) - mel_hop_len = int(round(token_hop_len * self.fm_model.length_normalizer_ratio)) - - # exclude empty tokens. - if aligned_token_emb.shape[1] > prompt_token[0].shape[1]: - cur_token = aligned_token_emb[:, prompt_token[0].shape[1]:] - cur_token_len = aligned_token_lens - prompt_token[1] - - # v2: excluding lookahead tokens for not-last packages - if streaming_mode == "v2": - if chunk_end + lookahead_size < text_emb.shape[1]: - cur_token = cur_token[:, :cur_token.shape[1] - token_hop_len, :] - cur_token_len = cur_token_len - token_hop_len - - # forward FM model - fm_st_time = time.time() - feat = self.fm_model.inference( - cur_token, cur_token_len, - xvec, xvec_lengths, - prompt=dict( - prompt_text=prompt_token, - prompt_audio=prompt_audio, - ), - **kwargs, - ) - if chunk_id == 0: - logging.info(f"fm cost time: {(time.time() - fm_st_time) * 1000.0:.2f} ms") - feat = self.rms_rescale_feat(feat) - cost = time.time() - _st_time - if chunk_id == 0: - logging.info(f"First package delay: {cost * 1000.0:.2f}ms") - print_token = tokens.cpu().squeeze().tolist() - logging.info(f"pack {chunk_id}: valid_tokens: {print_token[:len(print_token) - token_hop_len]}, " - f"pad_tokens: {print_token[len(print_token) - token_hop_len:]}.") - - if streaming_mode == "v1": - # v1: excluding lookahead parts for not-last packages - if chunk_end + lookahead_size < text_emb.shape[1]: - cur_token = cur_token[:, :cur_token.shape[1] - token_hop_len, :] - feat = feat[:, :, :feat.shape[2] - mel_hop_len] - - if streaming_mode == "v2": - # v2: reback token and mel feat - if chunk_end + lookahead_size < text_emb.shape[1]: - text_reback = 2 if chunk_id == 0 else 4 - token_hop_len_2 = self.get_hop_lens(fa_tokens, lookahead_size + text_reback) - token_reback = token_hop_len_2 - token_hop_len - cur_token = cur_token[:, :cur_token.shape[1] - token_reback, :] - feat_reback = int(round(token_reback * self.fm_model.length_normalizer_ratio)) - feat = feat[:, :, :feat.shape[2] - feat_reback] - chunk_end = chunk_end - text_reback - - # update values and lens of prompt token and audio - prompt_token[1] = prompt_token[1] + cur_token.shape[1] - prompt_token[0] = torch.concat([prompt_token[0], cur_token], dim=1) - prompt_audio[1] = prompt_audio[1] + feat.shape[2] - prompt_audio[0] = torch.concat([prompt_audio[0], feat.transpose(1, 2)], dim=1) - - chunk_id += 1 - chunk_start = chunk_end - if chunk_end + lookahead_size >= text_emb.shape[1]: - break - - return tokens, prompt_audio[0].transpose(1, 2) - - def streaming_one_step( - self, text: torch.Tensor, text_lengths: torch.Tensor, - xvec: Optional[torch.Tensor] = None, xvec_lengths: Optional[torch.Tensor] = None, - chunk_idx=0, - **kwargs - ): - device = text.device - use_causal_prob = kwargs.get("use_causal_prob", 1.0) - # streaming related config - chunk_size = kwargs.get("streaming_chunk_size", 4) - chunk_size_maxium = kwargs.get("chunk_size_maxium", 16) - lookahead_size = self.am_model.encoder.pre_lookahead_len - hint_once(f"chunk_size={chunk_size}, chunk_size_maxium={chunk_size_maxium}, " - f"pre lookahead size={lookahead_size}.", - "pre_lookahead_len") - - blank_penalty = kwargs.get("blank_penalty", 0.0) - sampling = kwargs.get("sampling", "greedy") - prompt_dict = kwargs.get("prompt_dict", {}) - prompt_token = list(prompt_dict.get("prompt_token", [None, None])) - prompt_audio = list(prompt_dict.get("prompt_audio", [None, None])) - - ftype = self.text_embedding.weight.dtype - if prompt_token[0] is None: - prompt_token[0] = torch.zeros([1, 0, self.output_size], device=device, dtype=ftype) - prompt_token[1] = torch.tensor([0], device=device, dtype=torch.long) - if prompt_audio[0] is None: - prompt_audio[0] = torch.zeros( - [1, 0, self.fm_model.mel_extractor.num_mels], - device=device, dtype=ftype - ) - prompt_audio[1] = torch.tensor([0], device=device, dtype=torch.long) - - # embed text inputs - mask = (text != -1).float().unsqueeze(-1) - text_emb = self.text_embedding(torch.clamp(text, min=0)) * mask - text_emb_lengths = text_lengths - - batch_size = text.shape[0] - - prompt, prompt_lens, text_emb, text_emb_lengths = self.split_prompt( - text_emb, text_emb_lengths, text, text_lengths - ) - if "outside_prompt" in kwargs: - prompt = kwargs["outside_prompt"].to(device) - if "outside_prompt_lengths" in kwargs: - prompt_lens = kwargs["outside_prompt_lengths"] - else: - prompt_lens = torch.tensor([prompt.shape[1]]).to(text_lengths) - prompt = self.outside_prompt_poj(prompt) - hint_once("use outside_prompt", "outside_prompt") - - if xvec is not None: - # using speaker embedding - hint_once("using speaker embedding for slot.", "use_spk_emb") - xvec = xvec[:, :xvec_lengths.max()] - else: - # textual prompt xvec - hint_once("using textual prompt for slot.", "use_spk_emb") - prompt_xvec = self.spk_aggregator( - prompt, prompt_lens, - self.spk_query.expand([batch_size, -1, -1]), torch.tensor([1] * batch_size).to(prompt_lens) - )[0] - xvec = self.prompt_xvec_proj(prompt_xvec) - xvec_lengths = torch.tensor([1] * batch_size).to(text_lengths) - - chunk_text_emb = text_emb - chunk_text_emb_lengths = torch.tensor([chunk_text_emb.shape[1]], dtype=torch.long, device=device) - - outs_tuple = self.text_encoder(chunk_text_emb, ilens=chunk_text_emb_lengths) - text_enc = outs_tuple[0] - text_enc_lens = chunk_text_emb_lengths - - # forward AM model - tokens, aligned_token_emb, aligned_token_lens = self.am_model.inference( - text_enc, text_enc_lens, - xvec, xvec_lengths, - sampling=sampling, - blank_penalty=blank_penalty, - text_is_embedding=True, - return_hidden=True, - use_causal_prob=use_causal_prob, - ) - token_hop_len, mel_hop_len = 0, 0 - if isinstance(tokens, tuple): - tokens, fa_tokens = tokens - token_hop_len = self.get_hop_lens(fa_tokens, lookahead_size) - mel_hop_len = int(round(token_hop_len * self.fm_model.length_normalizer_ratio)) - - cur_token, feat = None, None - # exclude empty tokens. - if aligned_token_emb.shape[1] > prompt_token[0].shape[1]: - cur_token = aligned_token_emb[:, prompt_token[0].shape[1]:] - cur_token_len = aligned_token_lens - prompt_token[1] - - # v2: excluding lookahead tokens for not-last packages - if text[0, -1] != self.endofprompt_token_id+1: - cur_token = cur_token[:, :cur_token.shape[1] - token_hop_len, :] - cur_token_len = cur_token_len - token_hop_len - - # forward FM model - # set_all_random_seed(0) - if cur_token_len[0] < 1: - return None, None - feat = self.fm_model.inference( - cur_token, cur_token_len, - xvec, xvec_lengths, - prompt=dict( - prompt_text=prompt_token, - prompt_audio=prompt_audio, - ), - **kwargs, - ) - feat = self.rms_rescale_feat(feat) - print_token = tokens.cpu().squeeze(0).squeeze(-1).tolist() - logging.info(f"valid_tokens: {print_token[:len(print_token) - token_hop_len]}, " - f"pad_tokens: {print_token[len(print_token) - token_hop_len:]}.") - - # v2: reback token and mel feat - if text[0, -1] != self.endofprompt_token_id+1: - text_reback = 2 if chunk_idx == 0 else 4 - token_hop_len_2 = self.get_hop_lens(fa_tokens, lookahead_size + text_reback) - token_reback = token_hop_len_2 - token_hop_len - cur_token = cur_token[:, :cur_token.shape[1] - token_reback, :] - feat_reback = int(round(token_reback * self.fm_model.length_normalizer_ratio)) - feat = feat[:, :, :feat.shape[2] - feat_reback] - - return cur_token, feat diff --git a/funasr/models/llm_asr/tts_models/encoders.py b/funasr/models/llm_asr/tts_models/encoders.py deleted file mode 100644 index a41fb67c7..000000000 --- a/funasr/models/llm_asr/tts_models/encoders.py +++ /dev/null @@ -1,542 +0,0 @@ -import logging -from typing import List -from typing import Optional -from typing import Tuple -from typing import Union -import torch -from torch import nn -from funasr.models.transformer.attention import ( - MultiHeadedAttention, # noqa: H301 - RelPositionMultiHeadedAttention, # noqa: H301 - LegacyRelPositionMultiHeadedAttention, # noqa: H301 -) -from funasr.models.transformer.embedding import ( - PositionalEncoding, # noqa: H301 - ScaledPositionalEncoding, # noqa: H301 - RelPositionalEncoding, # noqa: H301 - LegacyRelPositionalEncoding, # noqa: H301 -) -from funasr.models.transformer.layer_norm import LayerNorm -from funasr.models.transformer.utils.multi_layer_conv import Conv1dLinear -from funasr.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d -from funasr.models.transformer.utils.nets_utils import get_activation -from funasr.models.transformer.utils.nets_utils import make_pad_mask -from funasr.models.transformer.utils.mask import subsequent_mask, causal_block_mask -from funasr.models.transformer.positionwise_feed_forward import ( - PositionwiseFeedForward, # noqa: H301 -) -from funasr.models.transformer.utils.repeat import repeat -from funasr.models.transformer.utils.subsampling import ( - Conv2dSubsampling, Conv2dSubsampling2, Conv2dSubsampling6, Conv2dSubsampling8, TooShortUttError, - check_short_utt, Conv2dSubsamplingPad -) -import torch.nn.functional as F -from funasr.models.llm_asr.conformer_encoder import ConvolutionModule, EncoderLayer -from funasr.models.ctc.ctc import CTC - - -class Upsample1D(nn.Module): - """A 1D upsampling layer with an optional convolution. - - Parameters: - channels (`int`): - number of channels in the inputs and outputs. - use_conv (`bool`, default `False`): - option to use a convolution. - use_conv_transpose (`bool`, default `False`): - option to use a convolution transpose. - out_channels (`int`, optional): - number of output channels. Defaults to `channels`. - """ - - def __init__(self, channels, use_conv=False, use_conv_transpose=True, - out_channels=None, name="conv", channel_first=True, stride=2, causal=False): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_conv_transpose = use_conv_transpose - self.name = name - self.channel_first = channel_first - self.stride = stride - self.causal = causal - - self.conv = None - if use_conv_transpose: - # transpose conv doesn't support causal mode. - assert not causal - kernel_size = stride*2 + stride % 2 - padding = (kernel_size - stride) // 2 - self.conv = nn.ConvTranspose1d(channels, self.out_channels, kernel_size, stride, padding) - elif use_conv: - # In this mode, first repeat interpolate, than conv with stride=1 - self.conv = nn.Conv1d( - self.channels, self.out_channels, stride*2+1, stride=1, - padding=0, - ) - - def forward(self, inputs, input_lengths=None): - if not self.channel_first: - inputs = inputs.transpose(1, 2).contiguous() - assert inputs.shape[1] == self.channels - if self.use_conv_transpose: - outputs = self.conv(inputs) - if not self.channel_first: - outputs = outputs.transpose(1, 2).contiguous() - return outputs, input_lengths * self.stride - - outputs = F.interpolate(inputs, scale_factor=self.stride, mode="nearest") - - if self.use_conv: - if not self.causal: - outputs = F.pad(outputs, (self.stride, self.stride)) - else: - outputs = F.pad(outputs, (self.stride*2, 0)) - outputs = self.conv(outputs) - - if not self.channel_first: - outputs = outputs.transpose(1, 2).contiguous() - return outputs, input_lengths * self.stride - - -class PreLookaheadLayer(nn.Module): - def __init__(self, channels: int, pre_lookahead_len:int = 1): - super().__init__() - self.channels = channels - self.pre_lookahead_len = pre_lookahead_len - self.conv1 = nn.Conv1d( - channels, channels, - kernel_size=pre_lookahead_len+1, - stride=1, padding=0, - ) - self.conv2 = nn.Conv1d( - channels, channels, - kernel_size=3, stride=1, padding=0, - ) - - def forward(self, inputs, ilens): - """ - inputs: (batch_size, seq_len, channels) - """ - outputs = inputs.transpose(1, 2).contiguous() - # look ahead - outputs = F.pad(outputs, (0, self.pre_lookahead_len), mode='constant', value=0) - outputs = F.leaky_relu(self.conv1(outputs)) - # outputs - outputs = F.pad(outputs, (2, 0), mode='constant', value=0) - outputs = self.conv2(outputs) - outputs = outputs.transpose(1, 2).contiguous() - - mask = (~make_pad_mask(ilens).unsqueeze(-1).to(inputs.device)) - # residual connection - outputs = (outputs + inputs) * mask - - return outputs, ilens - - -class UpsampleConformerEncoder(nn.Module): - """Progressive upsampling Conformer encoder module. - - Args: - input_size (int): Input dimension. - output_size (int): Dimension of attention. - attention_heads (int): The number of heads of multi head attention. - linear_units (int): The number of units of position-wise feed forward. - num_blocks (int): The number of decoder blocks. - dropout_rate (float): Dropout rate. - attention_dropout_rate (float): Dropout rate in attention. - positional_dropout_rate (float): Dropout rate after adding positional encoding. - input_layer (Union[str, torch.nn.Module]): Input layer type. - normalize_before (bool): Whether to use layer_norm before the first block. - concat_after (bool): Whether to concat attention layer's input and output. - If True, additional linear will be applied. - i.e. x -> x + linear(concat(x, att(x))) - If False, no additional linear will be applied. i.e. x -> x + att(x) - positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear". - positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer. - rel_pos_type (str): Whether to use the latest relative positional encoding or - the legacy one. The legacy relative positional encoding will be deprecated - in the future. More Details can be found in - https://github.com/espnet/espnet/pull/2816. - encoder_pos_enc_layer_type (str): Encoder positional encoding layer type. - encoder_attn_layer_type (str): Encoder attention layer type. - activation_type (str): Encoder activation function type. - macaron_style (bool): Whether to use macaron style for positionwise layer. - use_cnn_module (bool): Whether to use convolution module. - zero_triu (bool): Whether to zero the upper triangular part of attention matrix. - cnn_module_kernel (int): Kernerl size of convolution module. - padding_idx (int): Padding idx for input_layer=embed. - - """ - - def __init__( - self, - input_size: int, - output_size: int = 256, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - upsample_blocks: int = 3, - upsample_attn_layers: int = 2, - upsample_ratios: tuple = None, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - attention_dropout_rate: float = 0.0, - input_layer: Optional[str] = "conv2d", - normalize_before: bool = True, - concat_after: bool = False, - positionwise_layer_type: str = "linear", - positionwise_conv_kernel_size: int = 3, - macaron_style: bool = False, - rel_pos_type: str = "legacy", - pos_enc_layer_type: str = "rel_pos", - selfattention_layer_type: str = "rel_selfattn", - activation_type: str = "swish", - use_cnn_module: bool = True, - zero_triu: bool = False, - cnn_module_kernel: int = 31, - padding_idx: int = -1, - causal: bool = False, - skip: bool = False, - channel_first: bool = False, - use_causal_prob: float = None, - pre_lookahead_len: int = None, - ): - super().__init__() - self._output_size = output_size - self.causal = causal - self.skip = skip - self.channel_first = channel_first - self.pre_lookahead_len = pre_lookahead_len - self.use_causal_prob = use_causal_prob - - if rel_pos_type == "legacy": - if pos_enc_layer_type == "rel_pos": - pos_enc_layer_type = "legacy_rel_pos" - if selfattention_layer_type == "rel_selfattn": - selfattention_layer_type = "legacy_rel_selfattn" - elif rel_pos_type == "latest": - assert selfattention_layer_type != "legacy_rel_selfattn" - assert pos_enc_layer_type != "legacy_rel_pos" - else: - raise ValueError("unknown rel_pos_type: " + rel_pos_type) - - activation = get_activation(activation_type) - if pos_enc_layer_type == "abs_pos": - pos_enc_class = PositionalEncoding - elif pos_enc_layer_type == "scaled_abs_pos": - pos_enc_class = ScaledPositionalEncoding - elif pos_enc_layer_type == "rel_pos": - assert selfattention_layer_type == "rel_selfattn" - pos_enc_class = RelPositionalEncoding - elif pos_enc_layer_type == "legacy_rel_pos": - assert selfattention_layer_type == "legacy_rel_selfattn" - pos_enc_class = LegacyRelPositionalEncoding - logging.warning( - "Using legacy_rel_pos and it will be deprecated in the future." - ) - else: - raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) - - if input_layer == "linear": - self.embed = torch.nn.Sequential( - torch.nn.Linear(input_size, output_size), - torch.nn.LayerNorm(output_size), - torch.nn.Dropout(dropout_rate), - pos_enc_class(output_size, positional_dropout_rate), - ) - elif input_layer == "conv2d": - self.embed = Conv2dSubsampling( - input_size, - output_size, - dropout_rate, - pos_enc_class(output_size, positional_dropout_rate), - ) - elif input_layer == "conv2dpad": - self.embed = Conv2dSubsamplingPad( - input_size, - output_size, - dropout_rate, - pos_enc_class(output_size, positional_dropout_rate), - ) - elif input_layer == "conv2d2": - self.embed = Conv2dSubsampling2( - input_size, - output_size, - dropout_rate, - pos_enc_class(output_size, positional_dropout_rate), - ) - elif input_layer == "conv2d6": - self.embed = Conv2dSubsampling6( - input_size, - output_size, - dropout_rate, - pos_enc_class(output_size, positional_dropout_rate), - ) - elif input_layer == "conv2d8": - self.embed = Conv2dSubsampling8( - input_size, - output_size, - dropout_rate, - pos_enc_class(output_size, positional_dropout_rate), - ) - elif input_layer == "embed": - self.embed = torch.nn.Sequential( - torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), - pos_enc_class(output_size, positional_dropout_rate), - ) - elif isinstance(input_layer, torch.nn.Module): - self.embed = torch.nn.Sequential( - input_layer, - pos_enc_class(output_size, positional_dropout_rate), - ) - elif input_layer is None: - self.embed = torch.nn.Sequential( - pos_enc_class(output_size, positional_dropout_rate) - ) - else: - raise ValueError("unknown input_layer: " + input_layer) - self.normalize_before = normalize_before - if positionwise_layer_type == "linear": - positionwise_layer = PositionwiseFeedForward - positionwise_layer_args = ( - output_size, - linear_units, - dropout_rate, - activation, - ) - elif positionwise_layer_type == "conv1d": - positionwise_layer = MultiLayeredConv1d - positionwise_layer_args = ( - output_size, - linear_units, - positionwise_conv_kernel_size, - dropout_rate, - ) - elif positionwise_layer_type == "conv1d-linear": - positionwise_layer = Conv1dLinear - positionwise_layer_args = ( - output_size, - linear_units, - positionwise_conv_kernel_size, - dropout_rate, - ) - else: - raise NotImplementedError("Support only linear or conv1d.") - - if pre_lookahead_len is not None: - self.pre_lookahead_layer = PreLookaheadLayer(output_size, pre_lookahead_len) - - if selfattention_layer_type == "selfattn": - encoder_selfattn_layer = MultiHeadedAttention - encoder_selfattn_layer_args = ( - attention_heads, - output_size, - attention_dropout_rate, - ) - elif selfattention_layer_type == "legacy_rel_selfattn": - assert pos_enc_layer_type == "legacy_rel_pos" - encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention - encoder_selfattn_layer_args = ( - attention_heads, - output_size, - attention_dropout_rate, - ) - logging.warning( - "Using legacy_rel_selfattn and it will be deprecated in the future." - ) - elif selfattention_layer_type == "rel_selfattn": - assert pos_enc_layer_type == "rel_pos" - encoder_selfattn_layer = RelPositionMultiHeadedAttention - encoder_selfattn_layer_args = ( - attention_heads, - output_size, - attention_dropout_rate, - zero_triu, - ) - else: - raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type) - - convolution_layer = ConvolutionModule - convolution_layer_args = (output_size, cnn_module_kernel, activation) - - self.encoders = repeat( - num_blocks, - lambda lnum: EncoderLayer( - output_size, - encoder_selfattn_layer(*encoder_selfattn_layer_args), - positionwise_layer(*positionwise_layer_args), - positionwise_layer(*positionwise_layer_args) if macaron_style else None, - convolution_layer(*convolution_layer_args) if use_cnn_module else None, - dropout_rate, - normalize_before, - concat_after, - stochastic_depth_rate=0.0, - ), - ) - self.upsample_blocks = nn.ModuleList() - if upsample_ratios is None: - upsample_ratios = [2] * upsample_blocks - self.upsample_ratios = upsample_ratios - assert upsample_blocks == len(upsample_ratios) - for i in range(upsample_blocks): - if not causal: - upsample_conv_block = Upsample1D( - channels=output_size, use_conv=False, use_conv_transpose=True, - out_channels=output_size, channel_first=False, stride=upsample_ratios[i], causal=False, - ) - else: - upsample_conv_block = Upsample1D( - channels=output_size, use_conv=True, use_conv_transpose=False, - out_channels=output_size, channel_first=False, stride=upsample_ratios[i], causal=True, - ) - upsample_attn_block = repeat( - upsample_attn_layers, - lambda lnum: EncoderLayer( - output_size, - encoder_selfattn_layer(*encoder_selfattn_layer_args), - positionwise_layer(*positionwise_layer_args), - positionwise_layer(*positionwise_layer_args) if macaron_style else None, - convolution_layer(*convolution_layer_args) if use_cnn_module else None, - dropout_rate, - normalize_before, - concat_after, - stochastic_depth_rate=0.0, - ), - ) - attn_input_layer = torch.nn.Sequential( - torch.nn.Linear(output_size, output_size), - torch.nn.LayerNorm(output_size), - torch.nn.Dropout(dropout_rate), - pos_enc_class(output_size, positional_dropout_rate), - ) - self.upsample_blocks.append(nn.ModuleList([upsample_conv_block, attn_input_layer, upsample_attn_block])) - - if self.normalize_before: - self.after_norm = LayerNorm(output_size) - - def output_size(self) -> int: - return self._output_size - - def rand_mix_masks(self, causal, noncausal): - use_causal = (torch.rand([causal.shape[0], 1, 1]) <= self.uni_encoder_prob).to(causal) - masks = use_causal * causal + (1 - use_causal) * noncausal - return masks - - def forward( - self, - xs_pad: torch.Tensor, - ilens: torch.Tensor = None, - prev_states: torch.Tensor = None, - ctc: CTC = None, - ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: - """Calculate forward propagation. - - Args: - xs_pad (torch.Tensor): Input tensor (#batch, L, input_size). - ilens (torch.Tensor): Input length (#batch). - prev_states (torch.Tensor): Not to be used now. - - Returns: - torch.Tensor: Output tensor (#batch, L, output_size). - torch.Tensor: Output length (#batch). - torch.Tensor: Not to be used now. - - """ - raw_input = xs_pad - if self.channel_first: - xs_pad = xs_pad.permute(0, 2, 1) - - if ilens is not None: - masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) - else: - masks = torch.ones( - xs_pad.shape[0], 1, xs_pad.shape[1], - dtype=torch.bool, device=xs_pad.device - ) - - if self.use_causal_prob is not None: - use_causal = (torch.rand([xs_pad.shape[0], 1, 1]) <= self.use_causal_prob).to(xs_pad) - else: - use_causal = torch.ones([xs_pad.shape[0], 1, 1]).to(xs_pad) - - if self.causal: - causal_mask = subsequent_mask( - xs_pad.shape[1], device=xs_pad.device, dtype=masks.dtype - ).unsqueeze(0) - causal_mask = masks & causal_mask - # whether to train causal & non-causal in a single model - masks = use_causal * causal_mask + (1 - use_causal) * masks - - if ( - isinstance(self.embed, Conv2dSubsampling) - or isinstance(self.embed, Conv2dSubsampling2) - or isinstance(self.embed, Conv2dSubsampling6) - or isinstance(self.embed, Conv2dSubsampling8) - or isinstance(self.embed, Conv2dSubsamplingPad) - ): - short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) - if short_status: - raise TooShortUttError( - f"has {xs_pad.size(1)} frames and is too short for subsampling " - + f"(it needs more than {limit_size} frames), return empty results", - xs_pad.size(1), - limit_size, - ) - xs_pad, masks = self.embed(xs_pad, masks) - else: - xs_pad = self.embed(xs_pad) - - if self.pre_lookahead_len is not None: - xs = xs_pad - if isinstance(xs_pad, tuple): - xs = xs_pad[0] - xs, _ = self.pre_lookahead_layer(xs, ilens) - if isinstance(xs_pad, tuple): - xs_pad = (xs, xs_pad[1]) - - # 1. modeling on inputs - intermediate_outs = [] - xs_pad, masks = self.encoders(xs_pad, masks) - - # 2. progressive upsampling - outs, olens = xs_pad, ilens - total_ratio = 1 - for up_ratio, layer in zip(self.upsample_ratios, self.upsample_blocks): - up_layer, attn_input_layer, attn_layer = layer - - if isinstance(outs, tuple): - outs = outs[0] - outs, olens = up_layer(outs, olens) - masks = (~make_pad_mask(olens)[:, None, :]).to(outs.device) - total_ratio = total_ratio * up_ratio - if self.causal: - causal_mask = causal_block_mask( - outs.shape[1], total_ratio, device=outs.device, dtype=masks.dtype - ).unsqueeze(0) - causal_mask = masks & causal_mask - masks = use_causal * causal_mask + (1 - use_causal) * masks - outs = attn_input_layer(outs) - outs, _ = attn_layer(outs, masks) - - xs_pad = outs - if isinstance(xs_pad, tuple): - xs_pad = xs_pad[0] - if self.normalize_before: - xs_pad = self.after_norm(xs_pad) - - if self.channel_first: - xs_pad = xs_pad.permute(0, 2, 1) - - if self.skip: - xs_pad = xs_pad + raw_input - - # olens = masks.squeeze(1).sum(1) - if len(intermediate_outs) > 0: - return (xs_pad, intermediate_outs), olens, None - - if ilens is not None: - return xs_pad, olens, None - else: - return xs_pad - diff --git a/funasr/models/llm_asr/tts_models/nar_acoustic_model.py b/funasr/models/llm_asr/tts_models/nar_acoustic_model.py deleted file mode 100644 index 1f4d7d6c6..000000000 --- a/funasr/models/llm_asr/tts_models/nar_acoustic_model.py +++ /dev/null @@ -1,625 +0,0 @@ -import logging -from typing import List, Tuple, Dict, Optional, Union -import torch -import torch.nn as nn -from funasr.models.transformer.utils.nets_utils import make_pad_mask -import torch.nn.functional as F -from funasr.train_utils.device_funcs import force_gatherable -from funasr.models.llm_asr.label_smoothing_loss import LabelSmoothingLoss -from copy import deepcopy -from funasr.metrics.compute_acc import th_accuracy -from funasr.models.transformer.utils.nets_utils import pad_list -import random -import numpy as np -from funasr.utils.hinter import hint_once -from funasr.models.transformer.utils.add_sos_eos import add_sos_eos -from funasr.models.llm_asr.tts_models.ctc_alignment import ctc_forced_align -from torch.nn.utils.rnn import pad_sequence -import itertools -from distutils.version import LooseVersion -from contextlib import contextmanager -if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): - from torch.cuda.amp import autocast -else: - # Nothing to do if torch<1.6.0 - @contextmanager - def autocast(enabled=True): - yield - - -class NARCTCModel(nn.Module): - def __init__( - self, - input_size: int, - vocab_size: int, - encoder: Union[nn.Module, dict], - decoder: Optional[nn.Module] = None, - ctc_weight: float = 0.5, - ignore_id: int = -1, - lsm_weight: float = 0.0, - length_normalized_loss: bool = False, - **kwargs, - ): - super().__init__() - self.input_size = input_size - self.decoder = decoder - self.encoder = encoder if isinstance(encoder, nn.Module) else self.build_encoder(encoder) - self.output_size = self.encoder.output_size() - self.ignore_id = ignore_id - self.vocab_size = vocab_size - self.ctc_weight = ctc_weight - - # build ctc module - from funasr.models.llm_asr.tts_models.ctc_alignment import CTC - ctc_conf = kwargs.pop("ctc_conf", {}) - self.ctc = CTC(vocab_size, encoder_output_size=self.output_size, **ctc_conf) - - self.text_embedding = torch.nn.Embedding(self.vocab_size, input_size) - self.token_embedding = torch.nn.Embedding(vocab_size, input_size) - xvec_size = kwargs.get("xvec_size", None) - if xvec_size is not None: - self.xvec_proj = torch.nn.Linear(xvec_size, input_size) - else: - self.xvec_proj = None - self.criterion_att = LabelSmoothingLoss( - size=vocab_size, - padding_idx=ignore_id, - smoothing=lsm_weight, - normalize_length=length_normalized_loss, - ) - self.sos = vocab_size - 2 - self.eos = vocab_size - 1 - self.length_regulator_conf = kwargs.get("length_regulator_conf", None) - if self.length_regulator_conf is not None: - self.length_regulator = self.build_length_regulator() - else: - self.length_regulator = None - - def build_encoder(self, encoder_conf: dict): - if encoder_conf is None: - assert hasattr(self, "encoder_conf"), \ - "function param encoder_conf is None and model doesn't has encoder_conf attribute either." - encoder_conf = self.encoder_conf - - encoder_name = encoder_conf.pop("name", "transformer") - model = None - if encoder_name == "transformer": - from funasr.models.llm_asr.conformer_encoder import ConformerEncoder - model = ConformerEncoder( - **encoder_conf, - input_size=self.input_size, - use_cnn_module=False, - macaron_style=False, - ) - elif encoder_name == "conformer": - from funasr.models.llm_asr.conformer_encoder import ConformerEncoder - model = ConformerEncoder( - **encoder_conf, - input_size=self.input_size, - ) - elif encoder_name == "upsampling_conformer": - from funasr.models.llm_asr.tts_models.encoders import UpsampleConformerEncoder - model = UpsampleConformerEncoder( - **encoder_conf, - input_size=self.input_size, - ) - - encoder_conf["name"] = encoder_name - - return model - - def build_length_regulator(self): - name = self.length_regulator_conf.pop("name", None) - model = None - if name == "upsampling": - from funasr.models.llm_asr.diffusion_models.length_regulator import UpSamplingRegulator - model = UpSamplingRegulator(self.input_size, self.length_regulator_conf.get("sampling_ratios")) - elif name == "downsampling": - from funasr.models.llm_asr.diffusion_models.length_regulator import DownSamplingRegulator - model = DownSamplingRegulator(self.input_size, self.length_regulator_conf.get("sampling_ratios")) - elif name == "interpolate": - from funasr.models.llm_asr.diffusion_models.length_regulator import InterpolateRegulator - model = InterpolateRegulator(self.input_size, **self.length_regulator_conf) - elif name == "upsampling_cif": - from funasr.models.llm_asr.diffusion_models.length_regulator import UpsamplingCifRegulator - model = UpsamplingCifRegulator(self.input_size, **self.length_regulator_conf) - - self.length_regulator_conf["name"] = name - - return model - - @staticmethod - def norm_and_sample_xvec(xvec, xvec_lengths): - xvec_list = [] - for i, ilen in enumerate(xvec_lengths): - idx = random.randint(0, ilen - 1) - while torch.any(~torch.isfinite(xvec[i, idx])): - idx = random.randint(0, ilen - 1) - xvec_list.append(xvec[i, idx]) - rand_xvec = torch.vstack(xvec_list) - rand_xvec = F.normalize(rand_xvec, dim=1) - - return rand_xvec - - def _calc_att_loss( - self, - encoder_out: torch.Tensor, - encoder_out_lens: torch.Tensor, - ys_pad: torch.Tensor, - ys_pad_lens: torch.Tensor, - ): - ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) - ys_in_lens = ys_pad_lens + 1 - - # 1. Forward decoder - decoder_out, _ = self.decoder( - encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens - ) - - # 2. Compute attention loss - loss_att = self.criterion_att(decoder_out, ys_out_pad) - acc_att = th_accuracy( - decoder_out.view(-1, self.decoder.vocab_size), - ys_out_pad, - ignore_label=self.ignore_id, - ) - - # Compute cer/wer using attention-decoder - if self.training or self.error_calculator is None: - cer_att, wer_att = None, None - else: - ys_hat = decoder_out.argmax(dim=-1) - cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) - - return loss_att, acc_att, cer_att, wer_att - - def model_forward( - self, - text: torch.Tensor, - text_lengths: torch.Tensor, - xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, - **kwargs - ): - # 0. Up-sampling text length - if self.length_regulator is not None: - text, text_lengths = self.length_regulator(text, text_lengths) - - # 1. padding xvec - if xvec is not None and self.xvec_proj is not None: - xvec = xvec[:, :xvec_lengths.max()] - # random select a xvec from xvec matrix - xvec = self.norm_and_sample_xvec(xvec, xvec_lengths) - xvec = self.xvec_proj(xvec) - text = text + xvec.unsqueeze(1) - hint_once("use xvec", "use_xvec") - - # 1. Encoder - encoder_out, encoder_out_lens, _ = self.encoder(text, text_lengths) - - return encoder_out, encoder_out_lens - - def predictor( - self, - am: torch.Tensor, - am_lens: torch.Tensor, - ys_pad: torch.Tensor, - ys_pad_lens: torch.Tensor, - alignment, - ): - acoustic_embeds = [] - use_pred_num = 0 - for am_xs, enc_len, ali, y, y_lens in zip(am, am_lens, alignment, ys_pad, ys_pad_lens): - pred = itertools.groupby(ali[:enc_len]) - - acoustic_embed = [] - _start = 0 - for pred_token, pred_frame in pred: - _end = _start + len(list(pred_frame)) - if pred_token != 0: - acoustic_embed.append(torch.mean(am_xs[_start:_end, :], 0, keepdim=True)) - _start = _end - if len(acoustic_embed) != y_lens: - acoustic_embeds.append(y[:y_lens]) - else: - acoustic_embeds.append(torch.cat(acoustic_embed, dim=0)) - use_pred_num += 1 - acoustic_embeds = pad_sequence(acoustic_embeds, batch_first=True, padding_value=0) - return acoustic_embeds, use_pred_num / am.shape[0] - - def force_align_text( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - text: torch.Tensor, - text_lengths: torch.Tensor, - xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, - **kwargs - ): - # plus one to speech token, to make index 0 represent , - # decoder vocab must be: 1 (blank) + num of token + 1 (sos) + 1 (eos) - speech = torch.where(speech != -1, speech + 1, speech) - - encoder_out, encoder_out_lens = self.model_forward( - text, text_lengths, - xvec, xvec_lengths, - **kwargs - ) - log_probs = self.ctc.log_softmax(encoder_out) - with torch.no_grad(): - alignment = ctc_forced_align( - log_probs.float(), - speech.long(), - encoder_out_lens.long(), - speech_lengths.long(), - ignore_id=self.ignore_id, - ) - aligned_token_emb, use_pred_ratio = self.predictor( - encoder_out, encoder_out_lens, - self.token_embedding(speech), speech_lengths, - alignment, - ) - - loss = 0 - states = dict( - use_pred_ratio=use_pred_ratio, - ) - if self.ctc_weight != 0.0: - loss_ctc, logits = self._calc_ctc_loss( - encoder_out, encoder_out_lens, speech, speech_lengths - ) - states["loss_ctc"] = loss_ctc.item() - loss = loss + self.ctc_weight * loss_ctc - if self.ctc_weight != 1.0: - loss_att, acc_att, cer_att, wer_att = self._calc_att_loss( - encoder_out, encoder_out_lens, speech, speech_lengths - ) - states["loss_att"] = loss_att.item() - loss = loss + (1.0 - self.ctc_weight) * loss_att - - states["loss"] = loss.item() - return loss, aligned_token_emb, states - - def _calc_ctc_loss( - self, - encoder_out: torch.Tensor, - encoder_out_lens: torch.Tensor, - ys_pad: torch.Tensor, - ys_pad_lens: torch.Tensor, - ): - # Calc CTC loss - loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens) - logits = self.ctc.log_softmax(encoder_out) - - return loss_ctc, logits - - def forward( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - text: torch.Tensor, - text_lengths: torch.Tensor, - xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, - **kwargs - ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: - """Frontend + Encoder + Decoder + Calc loss - - Args: - speech: (Batch, Length, ...), speech tokens - speech_lengths: (Batch, ) - text: (Batch, Length), text tokens - text_lengths: (Batch, ) - xvec: (Batch, Length, ...) x-vectors - xvec_lengths: (Batch, ) - """ - assert text_lengths.dim() == 1, text_lengths.shape - # Check that batch_size is unified - assert ( - speech.shape[0] - == speech_lengths.shape[0] - == text.shape[0] - == text_lengths.shape[0] - ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) - batch_size = speech.shape[0] - - # for data-parallel - text = text[:, : text_lengths.max()] - speech = speech[:, : speech_lengths.max()] - # plus one to speech token, to make index 0 represent , - # decoder vocab must be: 1 (blank) + num of token + 1 (sos) + 1 (eos) - speech = torch.where(speech != -1, speech + 1, speech) - - # embed text inputs - mask = (text != -1).float().unsqueeze(-1) - text = self.text_embedding(torch.clamp(text, min=0)) * mask - - encoder_out, encoder_out_lens = self.model_forward( - text, text_lengths, - xvec, xvec_lengths, - **kwargs, - ) - - loss_att, acc_att, cer_att, wer_att = None, None, None, None - loss_ctc, cer_ctc = None, None - stats = dict( - batch_size=float(batch_size), - text_len=float(text.shape[1]), - enc_len=float(encoder_out.shape[1]), - speech_len=float(speech.shape[1]), - token_text_ratio=float(speech.shape[1])/float(text.shape[1]), - ) - - # 1. CTC branch - if self.ctc_weight != 0.0: - loss_ctc, logits = self._calc_ctc_loss( - encoder_out, encoder_out_lens, speech, speech_lengths - ) - - # Collect CTC branch stats - stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None - stats["cer_ctc"] = cer_ctc - - # 2b. Attention decoder branch - if self.ctc_weight != 1.0: - loss_att, acc_att, cer_att, wer_att = self._calc_att_loss( - encoder_out, encoder_out_lens, speech, speech_lengths - ) - - # 3. CTC-Att loss definition - if self.ctc_weight == 0.0: - loss = loss_att - elif self.ctc_weight == 1.0: - loss = loss_ctc - else: - loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att - - # Collect Attn branch stats - stats["loss_att"] = loss_att.detach() if loss_att is not None else None - stats["acc"] = acc_att - stats["cer"] = cer_att - stats["wer"] = wer_att - - # Collect total loss stats - stats["loss"] = torch.clone(loss.detach()) - - # force_gatherable: to-device and to-tensor if scalar for DataParallel - loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) - return loss, stats, weight - - def topp_sampling(self, probs, top_p=0.8): - sorted_value, sorted_idx = probs.sort(descending=True, stable=True) - cumulative_probs = torch.cumsum(sorted_value, dim=-1) - sorted_indices_to_remove = cumulative_probs > top_p - sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() - sorted_indices_to_remove[..., 0] = False - indices_to_remove = sorted_idx[sorted_indices_to_remove] - probs[indices_to_remove] = 0 - top_ids = torch.multinomial(probs, num_samples=1) - - return top_ids - - def sampling_ids(self, enc_outs, sampling="greedy", blank_penalty=None, return_probs=False): - probs = self.ctc.softmax(enc_outs) - if blank_penalty > 0: - probs[:, :, 0] = probs[:, :, 0] * blank_penalty - - # top-p sampling - if "." in sampling: - sampling = float(sampling) - tokens = self.topp_sampling(probs, top_p=sampling) - tokens = torch.tensor(tokens, dtype=torch.long).to(probs.device) - # top-k sampling - elif sampling.isdigit(): - sampling = int(sampling) - probs = probs.topk(sampling) - tokens = probs.multinomial(1, replacement=True) - else: - if sampling == "greedy": - tokens = torch.argmax(probs, dim=-1) - elif "threshold_" in sampling: - threshold = float(sampling.split("_")[1]) - hint_once(f"Decoding mode: blank threshold={threshold:.2f}", "decoding_mode") - # mask out blank according to threshold - mask = probs[:, :, 0] > threshold - probs[:, :, 0] = probs[:, :, 0] * mask - tokens = torch.argmax(probs, dim=-1) - else: - raise NotImplementedError(f"sampling method {sampling} not implemented") - - if not return_probs: - return tokens - - return tokens, probs - - def inference( - self, - text: torch.Tensor, text_lengths: torch.Tensor, - xvec=None, xvec_lengths=None, - sampling="greedy", - blank_penalty: float = 0.0, - text_is_embedding=False, - return_hidden=False, - **kwargs, - ): - device = text.device - # use casual mode at inference stage - self.encoder.use_causal_prob = kwargs.get("use_causal_prob", 1.0) - hint_once(f"use_causal_prob {self.encoder.use_causal_prob}.", "use_causal_prob") - # embed text inputs - if not text_is_embedding: - mask = (text != -1).float().unsqueeze(-1) - text = self.text_embedding(torch.clamp(text, min=0)) * mask - - # 1. Encoder - encoder_out, encoder_out_lens = self.model_forward( - text, text_lengths, - xvec, xvec_lengths, - ) - fa_tokens, enc_probs = self.sampling_ids( - encoder_out, - sampling=sampling, - blank_penalty=blank_penalty, - return_probs=True, - ) - reduced_fa_tokens = [] - for pred_token, pred_frame in itertools.groupby(fa_tokens[0].cpu().tolist()): - if pred_token != 0: - reduced_fa_tokens.append(pred_token) - else: - reduced_fa_tokens.extend(list(pred_frame)) - fa_tokens = torch.tensor([reduced_fa_tokens]).to(fa_tokens) - # remove blanks (id=0) and convert token ids into the original format - tokens = [[x-1] for x in fa_tokens[0].cpu().tolist() if x > 0] - tokens = torch.tensor([tokens], dtype=torch.int64, device=device) - - if not return_hidden: - return tokens - - acoustic_embs, acoustic_emb_lens = [], [] - for idx, (prob, enc) in enumerate(zip(enc_probs, encoder_out)): - pred = itertools.groupby(prob.argmax(-1).cpu()) - acs_emb = [] - _start = 0 - for pred_token, pred_frame in pred: - _end = _start + len(list(pred_frame)) - if pred_token != 0 and pred_token != -1: - acs_emb.append(torch.mean(enc[_start:_end, :], 0, keepdim=True)) - _start = _end - acs_emb = torch.cat(acs_emb, dim=0) - acoustic_embs.append(acs_emb) - acoustic_emb_lens.append(acs_emb.shape[0]) - - acoustic_embs = pad_list(acoustic_embs, 0.0) - acoustic_emb_lens = torch.tensor(acoustic_emb_lens, dtype=torch.int64, device=device) - - return (tokens, fa_tokens), acoustic_embs, acoustic_emb_lens - - -class NARCTCProbModel(NARCTCModel): - def __init__(self, input_size: int, vocab_size: int, encoder: Union[nn.Module, dict], - decoder: Optional[nn.Module] = None, ctc_weight: float = 0.5, ignore_id: int = -1, - lsm_weight: float = 0.0, length_normalized_loss: bool = False, **kwargs): - super().__init__(input_size, vocab_size, encoder, decoder, ctc_weight, ignore_id, lsm_weight, - length_normalized_loss, **kwargs) - - def predictor( - self, - am_probs: torch.Tensor, - am_lens: torch.Tensor, - ys_pad: torch.Tensor, - ys_pad_lens: torch.Tensor, - alignment, - ): - acoustic_embeds = [] - use_pred_num = 0 - for probs, enc_len, ali, y, y_lens in zip(am_probs, am_lens, alignment, ys_pad, ys_pad_lens): - pred = itertools.groupby(ali[:enc_len]) - - acoustic_embed = [] - _start = 0 - for pred_token, pred_frame in pred: - _end = _start + len(list(pred_frame)) - if pred_token != 0: - acoustic_embed.append(torch.mean(probs[_start:_end, :], 0, keepdim=True)) - _start = _end - if len(acoustic_embed) != y_lens: - acoustic_embeds.append(F.one_hot(y[:y_lens], self.vocab_size).float()) - else: - acoustic_embeds.append(torch.cat(acoustic_embed, dim=0)) - use_pred_num += 1 - acoustic_embeds[-1] = torch.matmul(acoustic_embeds[-1], self.token_embedding.weight) - acoustic_embeds = pad_sequence(acoustic_embeds, batch_first=True, padding_value=0) - return acoustic_embeds, use_pred_num / am_probs.shape[0] - - def force_align_text(self, speech: torch.Tensor, speech_lengths: torch.Tensor, text: torch.Tensor, - text_lengths: torch.Tensor, xvec: Optional[torch.Tensor] = None, - xvec_lengths: Optional[torch.Tensor] = None, **kwargs): - # plus one to speech token, to make index 0 represent , - # decoder vocab must be: 1 (blank) + num of token + 1 (sos) + 1 (eos) - speech = torch.where(speech != -1, speech + 1, speech) - - encoder_out, encoder_out_lens = self.model_forward( - text, text_lengths, - xvec, xvec_lengths, - **kwargs - ) - log_probs = self.ctc.log_softmax(encoder_out) - with torch.no_grad(): - alignment = ctc_forced_align( - log_probs.float(), - speech.long(), - encoder_out_lens.long(), - speech_lengths.long(), - ignore_id=self.ignore_id, - ) - aligned_token_emb, use_pred_ratio = self.predictor( - log_probs.float(), encoder_out_lens.long(), - speech.long(), speech_lengths.long(), - alignment, - ) - - loss = 0 - states = dict( - use_pred_ratio=use_pred_ratio, - ) - if self.ctc_weight != 0.0: - loss_ctc, logits = self._calc_ctc_loss( - encoder_out, encoder_out_lens, speech, speech_lengths - ) - states["loss_ctc"] = loss_ctc.item() - loss = loss + self.ctc_weight * loss_ctc - if self.ctc_weight != 1.0: - loss_att, acc_att, cer_att, wer_att = self._calc_att_loss( - encoder_out, encoder_out_lens, speech, speech_lengths - ) - states["loss_att"] = loss_att.item() - loss = loss + (1.0 - self.ctc_weight) * loss_att - - states["loss"] = loss.item() - return loss, aligned_token_emb, states - - def inference(self, text: torch.Tensor, text_lengths: torch.Tensor, xvec=None, xvec_lengths=None, sampling="greedy", - blank_penalty: float = 0.0, text_is_embedding=False, return_hidden=False, **kwargs): - device = text.device - # embed text inputs - if not text_is_embedding: - mask = (text != -1).float().unsqueeze(-1) - text = self.text_embedding(torch.clamp(text, min=0)) * mask - - # 0. Up-sampling text length - if self.length_regulator is not None: - text, text_lengths = self.length_regulator(text, text_lengths) - - # 1. padding xvec - if xvec is not None and self.xvec_proj is not None: - xvec = xvec[:, :xvec_lengths.max()] - # random select a xvec from xvec matrix - xvec = self.norm_and_sample_xvec(xvec, xvec_lengths) - xvec = self.xvec_proj(xvec) - text = text + xvec.unsqueeze(1) - hint_once("use xvec", "use_xvec") - - # 1. Encoder - encoder_out, encoder_out_lens = self.model_forward( - text, text_lengths, - xvec, xvec_lengths, - ) - tokens, enc_probs = self.sampling_ids( - encoder_out, - sampling=sampling, - blank_penalty=blank_penalty, - return_probs=True, - ) - # remove blanks (id=0) and convert token ids into the original format - tokens = [[x - 1] for x in tokens[0].cpu().tolist() if x > 0] - tokens = torch.tensor([tokens], dtype=torch.int64, device=device) - - if not return_hidden: - return tokens - - acoustic_embs = self.token_embedding(tokens.squeeze(-1)) - acoustic_emb_lens = torch.tensor([acoustic_embs.shape[1]], dtype=torch.int64, device=device) - - return tokens, acoustic_embs, acoustic_emb_lens diff --git a/funasr/models/llm_asr/tts_models/transformer_decoder.py b/funasr/models/llm_asr/tts_models/transformer_decoder.py deleted file mode 100644 index a1b09de09..000000000 --- a/funasr/models/llm_asr/tts_models/transformer_decoder.py +++ /dev/null @@ -1,761 +0,0 @@ -# Copyright 2019 Shigeki Karita -# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) - -"""Decoder definition.""" -from typing import Any -from typing import List -from typing import Sequence -from typing import Tuple -import torch -from torch import nn -from funasr.models.transformer.attention import MultiHeadedAttention -from funasr.models.transformer.utils.dynamic_conv import DynamicConvolution -from funasr.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D -from funasr.models.transformer.embedding import PositionalEncoding -from funasr.models.transformer.layer_norm import LayerNorm -from funasr.models.transformer.utils.lightconv import LightweightConvolution -from funasr.models.transformer.utils.lightconv2d import LightweightConvolution2D -from funasr.models.transformer.utils.mask import subsequent_mask -from funasr.models.transformer.utils.nets_utils import make_pad_mask -from funasr.models.transformer.positionwise_feed_forward import ( - PositionwiseFeedForward, # noqa: H301 -) -from funasr.models.transformer.utils.repeat import repeat -from funasr.models.transformer.scorers.scorer_interface import BatchScorerInterface - - -class DecoderLayer(nn.Module): - """Single decoder layer module. - - Args: - size (int): Input dimension. - self_attn (torch.nn.Module): Self-attention module instance. - `MultiHeadedAttention` instance can be used as the argument. - src_attn (torch.nn.Module): Self-attention module instance. - `MultiHeadedAttention` instance can be used as the argument. - feed_forward (torch.nn.Module): Feed-forward module instance. - `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance - can be used as the argument. - dropout_rate (float): Dropout rate. - normalize_before (bool): Whether to use layer_norm before the first block. - concat_after (bool): Whether to concat attention layer's input and output. - if True, additional linear will be applied. - i.e. x -> x + linear(concat(x, att(x))) - if False, no additional linear will be applied. i.e. x -> x + att(x) - - - """ - - def __init__( - self, - size, - self_attn, - src_attn, - feed_forward, - dropout_rate, - normalize_before=True, - concat_after=False, - ): - """Construct an DecoderLayer object.""" - super(DecoderLayer, self).__init__() - self.size = size - self.self_attn = self_attn - self.src_attn = src_attn - self.feed_forward = feed_forward - self.norm1 = LayerNorm(size) - self.norm2 = LayerNorm(size) - self.norm3 = LayerNorm(size) - self.dropout = nn.Dropout(dropout_rate) - self.normalize_before = normalize_before - self.concat_after = concat_after - if self.concat_after: - self.concat_linear1 = nn.Linear(size + size, size) - self.concat_linear2 = nn.Linear(size + size, size) - - def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None): - """Compute decoded features. - - Args: - tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). - tgt_mask (torch.Tensor): Mask for input tensor (#batch, 1, maxlen_out). - memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). - memory_mask (torch.Tensor): Encoded memory mask (#batch, 1, maxlen_in). - cache (List[torch.Tensor]): List of cached tensors. - Each tensor shape should be (#batch, maxlen_out - 1, size). - - Returns: - torch.Tensor: Output tensor(#batch, maxlen_out, size). - torch.Tensor: Mask for output tensor (#batch, maxlen_out). - torch.Tensor: Encoded memory (#batch, maxlen_in, size). - torch.Tensor: Encoded memory mask (#batch, maxlen_in). - - """ - residual = tgt - if self.normalize_before: - tgt = self.norm1(tgt) - - if cache is None: - tgt_q = tgt - tgt_q_mask = tgt_mask - else: - # compute only the last frame query keeping dim: max_time_out -> 1 - assert cache.shape == ( - tgt.shape[0], - tgt.shape[1] - 1, - self.size, - ), f"{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}" - tgt_q = tgt[:, -1:, :] - residual = residual[:, -1:, :] - tgt_q_mask = None - if tgt_mask is not None: - tgt_q_mask = tgt_mask[:, -1:, :] - - if self.concat_after: - tgt_concat = torch.cat( - (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)), dim=-1 - ) - x = residual + self.concat_linear1(tgt_concat) - else: - x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)) - if not self.normalize_before: - x = self.norm1(x) - - residual = x - if self.normalize_before: - x = self.norm2(x) - if self.concat_after: - x_concat = torch.cat( - (x, self.src_attn(x, memory, memory, memory_mask)), dim=-1 - ) - x = residual + self.concat_linear2(x_concat) - else: - x = residual + self.dropout(self.src_attn(x, memory, memory, memory_mask)) - if not self.normalize_before: - x = self.norm2(x) - - residual = x - if self.normalize_before: - x = self.norm3(x) - x = residual + self.dropout(self.feed_forward(x)) - if not self.normalize_before: - x = self.norm3(x) - - if cache is not None: - x = torch.cat([cache, x], dim=1) - - return x, tgt_mask, memory, memory_mask - - -class BaseTransformerDecoder(nn.Module, BatchScorerInterface): - """Base class of Transfomer decoder module. - - Args: - vocab_size: output dim - encoder_output_size: dimension of attention - attention_heads: the number of heads of multi head attention - linear_units: the number of units of position-wise feed forward - num_blocks: the number of decoder blocks - dropout_rate: dropout rate - self_attention_dropout_rate: dropout rate for attention - input_layer: input layer type - use_output_layer: whether to use output layer - pos_enc_class: PositionalEncoding or ScaledPositionalEncoding - normalize_before: whether to use layer_norm before the first block - concat_after: whether to concat attention layer's input and output - if True, additional linear will be applied. - i.e. x -> x + linear(concat(x, att(x))) - if False, no additional linear will be applied. - i.e. x -> x + att(x) - """ - - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - input_layer: str = "embed", - use_output_layer: bool = True, - pos_enc_class=PositionalEncoding, - normalize_before: bool = True, - causal=True, - ): - super().__init__() - attention_dim = encoder_output_size - self.causal = causal - self.vocab_size = vocab_size - - if input_layer == "embed": - self.embed = torch.nn.Sequential( - torch.nn.Embedding(vocab_size, attention_dim), - pos_enc_class(attention_dim, positional_dropout_rate), - ) - elif input_layer == "linear": - self.embed = torch.nn.Sequential( - torch.nn.Linear(vocab_size, attention_dim), - torch.nn.LayerNorm(attention_dim), - torch.nn.Dropout(dropout_rate), - torch.nn.ReLU(), - pos_enc_class(attention_dim, positional_dropout_rate), - ) - else: - raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}") - - self.normalize_before = normalize_before - if self.normalize_before: - self.after_norm = LayerNorm(attention_dim) - if use_output_layer: - self.output_layer = torch.nn.Linear(attention_dim, vocab_size) - else: - self.output_layer = None - - # Must set by the inheritance - self.decoders = None - - def forward( - self, - hs_pad: torch.Tensor, - hlens: torch.Tensor, - ys_in_pad: torch.Tensor, - ys_in_lens: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward decoder. - - Args: - hs_pad: encoded memory, float32 (batch, maxlen_in, feat) - hlens: (batch) - ys_in_pad: - input token ids, int64 (batch, maxlen_out) - if input_layer == "embed" - input tensor (batch, maxlen_out, #mels) in the other cases - ys_in_lens: (batch) - Returns: - (tuple): tuple containing: - - x: decoded token score before softmax (batch, maxlen_out, token) - if use_output_layer is True, - olens: (batch, ) - """ - tgt = ys_in_pad - # tgt_mask: (B, 1, L) - tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device) - if self.causal: - # m: (1, L, L) - m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0) - # tgt_mask: (B, L, L) - tgt_mask = tgt_mask & m - - memory = hs_pad - memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to( - memory.device - ) - # Padding for Longformer - if memory_mask.shape[-1] != memory.shape[1]: - padlen = memory.shape[1] - memory_mask.shape[-1] - memory_mask = torch.nn.functional.pad( - memory_mask, (0, padlen), "constant", False - ) - - x = self.embed(tgt) - x, tgt_mask, memory, memory_mask = self.decoders( - x, tgt_mask, memory, memory_mask - ) - if self.normalize_before: - x = self.after_norm(x) - if self.output_layer is not None: - x = self.output_layer(x) - - olens = tgt_mask.sum(1) - return x, olens - - def forward_one_step( - self, - tgt: torch.Tensor, - tgt_mask: torch.Tensor, - memory: torch.Tensor, - cache: List[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - """Forward one step. - - Args: - tgt: input token ids, int64 (batch, maxlen_out) - tgt_mask: input token mask, (batch, maxlen_out) - dtype=torch.uint8 in PyTorch 1.2- - dtype=torch.bool in PyTorch 1.2+ (include 1.2) - memory: encoded memory, float32 (batch, maxlen_in, feat) - cache: cached output list of (batch, max_time_out-1, size) - Returns: - y, cache: NN output value and cache per `self.decoders`. - y.shape` is (batch, maxlen_out, token) - """ - x = self.embed(tgt) - if cache is None: - cache = [None] * len(self.decoders) - new_cache = [] - for c, decoder in zip(cache, self.decoders): - x, tgt_mask, memory, memory_mask = decoder( - x, tgt_mask, memory, None, cache=c - ) - new_cache.append(x) - - if self.normalize_before: - y = self.after_norm(x[:, -1]) - else: - y = x[:, -1] - if self.output_layer is not None: - y = torch.log_softmax(self.output_layer(y), dim=-1) - - return y, new_cache - - def score(self, ys, state, x): - """Score.""" - ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0) - logp, state = self.forward_one_step( - ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state - ) - return logp.squeeze(0), state - - def batch_score( - self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor - ) -> Tuple[torch.Tensor, List[Any]]: - """Score new token batch. - - Args: - ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). - states (List[Any]): Scorer states for prefix tokens. - xs (torch.Tensor): - The encoder feature that generates ys (n_batch, xlen, n_feat). - - Returns: - tuple[torch.Tensor, List[Any]]: Tuple of - batchfied scores for next token with shape of `(n_batch, n_vocab)` - and next state list for ys. - - """ - # merge states - n_batch = len(ys) - n_layers = len(self.decoders) - if states[0] is None: - batch_state = None - else: - # transpose state of [batch, layer] into [layer, batch] - batch_state = [ - torch.stack([states[b][i] for b in range(n_batch)]) - for i in range(n_layers) - ] - - # batch decoding - ys_mask = subsequent_mask(ys.size(-1), device=xs.device).unsqueeze(0) - logp, states = self.forward_one_step(ys, ys_mask, xs, cache=batch_state) - - # transpose state of [layer, batch] into [batch, layer] - state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)] - return logp, state_list - - -class TransformerDecoder(BaseTransformerDecoder): - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - self_attention_dropout_rate: float = 0.0, - src_attention_dropout_rate: float = 0.0, - input_layer: str = "embed", - use_output_layer: bool = True, - pos_enc_class=PositionalEncoding, - normalize_before: bool = True, - concat_after: bool = False, - causal: bool = True, - ): - super().__init__( - vocab_size=vocab_size, - encoder_output_size=encoder_output_size, - dropout_rate=dropout_rate, - positional_dropout_rate=positional_dropout_rate, - input_layer=input_layer, - use_output_layer=use_output_layer, - pos_enc_class=pos_enc_class, - normalize_before=normalize_before, - causal=causal, - ) - - attention_dim = encoder_output_size - self.decoders = repeat( - num_blocks, - lambda lnum: DecoderLayer( - attention_dim, - MultiHeadedAttention( - attention_heads, attention_dim, self_attention_dropout_rate - ), - MultiHeadedAttention( - attention_heads, attention_dim, src_attention_dropout_rate - ), - PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), - dropout_rate, - normalize_before, - concat_after, - ), - ) - - -class ParaformerDecoderSAN(BaseTransformerDecoder): - """ - author: Speech Lab, Alibaba Group, China - Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition - https://arxiv.org/abs/2006.01713 - """ - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - self_attention_dropout_rate: float = 0.0, - src_attention_dropout_rate: float = 0.0, - input_layer: str = "embed", - use_output_layer: bool = True, - pos_enc_class=PositionalEncoding, - normalize_before: bool = True, - concat_after: bool = False, - embeds_id: int = -1, - ): - super().__init__( - vocab_size=vocab_size, - encoder_output_size=encoder_output_size, - dropout_rate=dropout_rate, - positional_dropout_rate=positional_dropout_rate, - input_layer=input_layer, - use_output_layer=use_output_layer, - pos_enc_class=pos_enc_class, - normalize_before=normalize_before, - ) - - attention_dim = encoder_output_size - self.decoders = repeat( - num_blocks, - lambda lnum: DecoderLayer( - attention_dim, - MultiHeadedAttention( - attention_heads, attention_dim, self_attention_dropout_rate - ), - MultiHeadedAttention( - attention_heads, attention_dim, src_attention_dropout_rate - ), - PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), - dropout_rate, - normalize_before, - concat_after, - ), - ) - self.embeds_id = embeds_id - self.attention_dim = attention_dim - - def forward( - self, - hs_pad: torch.Tensor, - hlens: torch.Tensor, - ys_in_pad: torch.Tensor, - ys_in_lens: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward decoder. - - Args: - hs_pad: encoded memory, float32 (batch, maxlen_in, feat) - hlens: (batch) - ys_in_pad: - input token ids, int64 (batch, maxlen_out) - if input_layer == "embed" - input tensor (batch, maxlen_out, #mels) in the other cases - ys_in_lens: (batch) - Returns: - (tuple): tuple containing: - - x: decoded token score before softmax (batch, maxlen_out, token) - if use_output_layer is True, - olens: (batch, ) - """ - tgt = ys_in_pad - tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device) - - memory = hs_pad - memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to( - memory.device - ) - # Padding for Longformer - if memory_mask.shape[-1] != memory.shape[1]: - padlen = memory.shape[1] - memory_mask.shape[-1] - memory_mask = torch.nn.functional.pad( - memory_mask, (0, padlen), "constant", False - ) - - # x = self.embed(tgt) - x = tgt - embeds_outputs = None - for layer_id, decoder in enumerate(self.decoders): - x, tgt_mask, memory, memory_mask = decoder( - x, tgt_mask, memory, memory_mask - ) - if layer_id == self.embeds_id: - embeds_outputs = x - if self.normalize_before: - x = self.after_norm(x) - if self.output_layer is not None: - x = self.output_layer(x) - - olens = tgt_mask.sum(1) - if embeds_outputs is not None: - return x, olens, embeds_outputs - else: - return x, olens - - -class LightweightConvolutionTransformerDecoder(BaseTransformerDecoder): - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - self_attention_dropout_rate: float = 0.0, - src_attention_dropout_rate: float = 0.0, - input_layer: str = "embed", - use_output_layer: bool = True, - pos_enc_class=PositionalEncoding, - normalize_before: bool = True, - concat_after: bool = False, - conv_wshare: int = 4, - conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), - conv_usebias: int = False, - ): - if len(conv_kernel_length) != num_blocks: - raise ValueError( - "conv_kernel_length must have equal number of values to num_blocks: " - f"{len(conv_kernel_length)} != {num_blocks}" - ) - super().__init__( - vocab_size=vocab_size, - encoder_output_size=encoder_output_size, - dropout_rate=dropout_rate, - positional_dropout_rate=positional_dropout_rate, - input_layer=input_layer, - use_output_layer=use_output_layer, - pos_enc_class=pos_enc_class, - normalize_before=normalize_before, - ) - - attention_dim = encoder_output_size - self.decoders = repeat( - num_blocks, - lambda lnum: DecoderLayer( - attention_dim, - LightweightConvolution( - wshare=conv_wshare, - n_feat=attention_dim, - dropout_rate=self_attention_dropout_rate, - kernel_size=conv_kernel_length[lnum], - use_kernel_mask=True, - use_bias=conv_usebias, - ), - MultiHeadedAttention( - attention_heads, attention_dim, src_attention_dropout_rate - ), - PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), - dropout_rate, - normalize_before, - concat_after, - ), - ) - - -class LightweightConvolution2DTransformerDecoder(BaseTransformerDecoder): - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - self_attention_dropout_rate: float = 0.0, - src_attention_dropout_rate: float = 0.0, - input_layer: str = "embed", - use_output_layer: bool = True, - pos_enc_class=PositionalEncoding, - normalize_before: bool = True, - concat_after: bool = False, - conv_wshare: int = 4, - conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), - conv_usebias: int = False, - ): - if len(conv_kernel_length) != num_blocks: - raise ValueError( - "conv_kernel_length must have equal number of values to num_blocks: " - f"{len(conv_kernel_length)} != {num_blocks}" - ) - super().__init__( - vocab_size=vocab_size, - encoder_output_size=encoder_output_size, - dropout_rate=dropout_rate, - positional_dropout_rate=positional_dropout_rate, - input_layer=input_layer, - use_output_layer=use_output_layer, - pos_enc_class=pos_enc_class, - normalize_before=normalize_before, - ) - - attention_dim = encoder_output_size - self.decoders = repeat( - num_blocks, - lambda lnum: DecoderLayer( - attention_dim, - LightweightConvolution2D( - wshare=conv_wshare, - n_feat=attention_dim, - dropout_rate=self_attention_dropout_rate, - kernel_size=conv_kernel_length[lnum], - use_kernel_mask=True, - use_bias=conv_usebias, - ), - MultiHeadedAttention( - attention_heads, attention_dim, src_attention_dropout_rate - ), - PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), - dropout_rate, - normalize_before, - concat_after, - ), - ) - - -class DynamicConvolutionTransformerDecoder(BaseTransformerDecoder): - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - self_attention_dropout_rate: float = 0.0, - src_attention_dropout_rate: float = 0.0, - input_layer: str = "embed", - use_output_layer: bool = True, - pos_enc_class=PositionalEncoding, - normalize_before: bool = True, - concat_after: bool = False, - conv_wshare: int = 4, - conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), - conv_usebias: int = False, - ): - if len(conv_kernel_length) != num_blocks: - raise ValueError( - "conv_kernel_length must have equal number of values to num_blocks: " - f"{len(conv_kernel_length)} != {num_blocks}" - ) - super().__init__( - vocab_size=vocab_size, - encoder_output_size=encoder_output_size, - dropout_rate=dropout_rate, - positional_dropout_rate=positional_dropout_rate, - input_layer=input_layer, - use_output_layer=use_output_layer, - pos_enc_class=pos_enc_class, - normalize_before=normalize_before, - ) - attention_dim = encoder_output_size - - self.decoders = repeat( - num_blocks, - lambda lnum: DecoderLayer( - attention_dim, - DynamicConvolution( - wshare=conv_wshare, - n_feat=attention_dim, - dropout_rate=self_attention_dropout_rate, - kernel_size=conv_kernel_length[lnum], - use_kernel_mask=True, - use_bias=conv_usebias, - ), - MultiHeadedAttention( - attention_heads, attention_dim, src_attention_dropout_rate - ), - PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), - dropout_rate, - normalize_before, - concat_after, - ), - ) - - -class DynamicConvolution2DTransformerDecoder(BaseTransformerDecoder): - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - self_attention_dropout_rate: float = 0.0, - src_attention_dropout_rate: float = 0.0, - input_layer: str = "embed", - use_output_layer: bool = True, - pos_enc_class=PositionalEncoding, - normalize_before: bool = True, - concat_after: bool = False, - conv_wshare: int = 4, - conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), - conv_usebias: int = False, - ): - if len(conv_kernel_length) != num_blocks: - raise ValueError( - "conv_kernel_length must have equal number of values to num_blocks: " - f"{len(conv_kernel_length)} != {num_blocks}" - ) - super().__init__( - vocab_size=vocab_size, - encoder_output_size=encoder_output_size, - dropout_rate=dropout_rate, - positional_dropout_rate=positional_dropout_rate, - input_layer=input_layer, - use_output_layer=use_output_layer, - pos_enc_class=pos_enc_class, - normalize_before=normalize_before, - ) - attention_dim = encoder_output_size - - self.decoders = repeat( - num_blocks, - lambda lnum: DecoderLayer( - attention_dim, - DynamicConvolution2D( - wshare=conv_wshare, - n_feat=attention_dim, - dropout_rate=self_attention_dropout_rate, - kernel_size=conv_kernel_length[lnum], - use_kernel_mask=True, - use_bias=conv_usebias, - ), - MultiHeadedAttention( - attention_heads, attention_dim, src_attention_dropout_rate - ), - PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), - dropout_rate, - normalize_before, - concat_after, - ), - ) \ No newline at end of file diff --git a/funasr/models/llm_asr/tts_text_tokenizer/__init__.py b/funasr/models/llm_asr/tts_text_tokenizer/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/funasr/models/llm_asr/tts_text_tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken b/funasr/models/llm_asr/tts_text_tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken deleted file mode 100644 index ac47fe685..000000000 --- a/funasr/models/llm_asr/tts_text_tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken 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b/funasr/models/llm_asr/tts_text_tokenizer/assets/ttsfrd_rich.token deleted file mode 100644 index 4f882a498..000000000 --- a/funasr/models/llm_asr/tts_text_tokenizer/assets/ttsfrd_rich.token +++ /dev/null @@ -1,346 +0,0 @@ -@ -@ -@ -@ -@! -@" -@# -@$ -@' -@( -@) -@* -@, -@- -@. -@/ -@: -@; -@< -@> -@? -@[ -@] -@^ -@_ -@` -@a_c1 -@a_c2 -@a_c3 -@a_c4 -@a_c5 -@aa0 -@aa1 -@aa2 -@ae0 -@ae1 -@ae2 -@ah0 -@ah1 -@ah2 -@ai_c1 -@ai_c2 -@ai_c3 -@ai_c4 -@ai_c5 -@an_c1 -@an_c2 -@an_c3 -@an_c4 -@an_c5 -@ang_c1 -@ang_c2 -@ang_c3 -@ang_c4 -@ang_c5 -@ao0 -@ao1 -@ao2 -@ao_c1 -@ao_c2 -@ao_c3 -@ao_c4 -@ao_c5 -@aw0 -@aw1 -@aw2 -@ay0 -@ay1 -@ay2 -@b -@b_c -@c_c -@ch -@ch_c -@d -@d_c -@dh -@e_c1 -@e_c2 -@e_c3 -@e_c4 -@e_c5 -@eh0 -@eh1 -@eh2 -@ei_c1 -@ei_c2 -@ei_c3 -@ei_c4 -@ei_c5 -@en_c1 -@en_c2 -@en_c3 -@en_c4 -@en_c5 -@eng_c1 -@eng_c2 -@eng_c3 -@eng_c4 -@eng_c5 -@er0 -@er1 -@er2 -@er_c1 -@er_c2 -@er_c3 -@er_c4 -@er_c5 -@ey0 -@ey1 -@ey2 -@f -@f_c -@g -@g_c -@ga -@ge -@go -@h_c -@hh -@i_c1 -@i_c2 -@i_c3 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token_type: str, - bpemodel: Union[Path, str, Iterable[str]] = None, - non_linguistic_symbols: Union[Path, str, Iterable[str]] = None, - remove_non_linguistic_symbols: bool = False, - space_symbol: str = "", - delimiter: str = None, - g2p_type: str = None, - p_word2phn: float = 0.5, -): - if "whisper_rich_ttsfrd" in token_type: - from funasr.models.llm_asr.tts_text_tokenizer.whisper_tokenizer import WhisperRichTtsFrdTokenizer - return WhisperRichTtsFrdTokenizer( - token_path="multilingual_zh_ja_yue_char_del", - num_languages=105, - task=None, - language=None, - ttsfrd_type="ttsfrd_rich", - ttsfrd_model=bpemodel, - p_word2phn=p_word2phn, - ) - - else: - raise ValueError( - f"token_mode must be one of bpe, word, char or phn: " f"{token_type}" - ) diff --git a/funasr/models/llm_asr/tts_text_tokenizer/phoneme_tokenizer.py b/funasr/models/llm_asr/tts_text_tokenizer/phoneme_tokenizer.py deleted file mode 100644 index f6e552802..000000000 --- a/funasr/models/llm_asr/tts_text_tokenizer/phoneme_tokenizer.py +++ /dev/null @@ -1,175 +0,0 @@ -import logging -from pathlib import Path -import re -from typing import Iterable -from typing import List -from typing import Optional -from typing import Union -import warnings -import os -import json -import jamo - - -class TtsFrdRich: - """ - rich text info: phoneme + puncs + boundary + [word2phone] - """ - def __init__(self, remove_boundary=True, token_type="pronplus"): - super().__init__() - self.remove_boundary = remove_boundary - self.token_type = token_type - self.g2p = None - self.lang_type = None - self.lang_type_map = {"zh-cn": "pinyin", "en-us": "enus"} - - @staticmethod - def contains_chinese(str): - return bool(re.search(r'[\u4e00-\u9fff]', str)) - @staticmethod - def is_full_half_punctuation_string(s): - # 包含ASCII标点和常见全角标点 - punctuation_pattern = r'[\u0000-\u002f\u003a-\u0040\u005b-\u0060\u007b-\u007f\u3000-\u303f\uff00-\uffef]' - # 使用re.findall找出所有匹配的字符 - results = re.findall(punctuation_pattern, s) - # 如果字符串长度和匹配到的字符总数一样，说明全部是标点 - return len(s) == len("".join(results)) - - def build(self, resource_dir, lang_type="Zh-CN"): - lang_type = lang_type.lower() - new_lang_type = self.lang_type_map[lang_type] - if self.g2p is None: - import ttsfrd - assert os.path.isdir(resource_dir) - fe = ttsfrd.TtsFrontendEngine() - fe.initialize(resource_dir) - self.g2p = fe - # self.lang_type = new_lang_type - self.set_lang_type(new_lang_type) - if self.lang_type != new_lang_type: - # self.lang_type = new_lang_type - self.set_lang_type(new_lang_type) - def set_lang_type(self, lang_type): - if lang_type == "enus": - self.g2p.set_lang_type(lang_type) - self.g2p.enable_pinyin_mix(True) - # self.g2p.set_breakmodel_index(0) - else: - self.g2p.set_lang_type(lang_type) - self.g2p.enable_pinyin_mix(True) - # self.g2p.set_breakmodel_index(1) - self.lang_type = lang_type - def set_token_type(self, token_type): - assert token_type in ["pronplus", "word2phn", "wordlist"], token_type - self.token_type = token_type - def __call__(self, text) -> Union[List[str], str]: - assert self.g2p is not None - if not self.contains_chinese(text): - if self.lang_type != "enus": - self.set_lang_type("enus") - else: - if self.lang_type != "pinyin": - self.set_lang_type("pinyin") - if self.token_type == "word2phn": - return self._get_word2phn(text) - elif self.token_type == "pronplus": - return self._get_pronplus(text) - elif self.token_type == "wordlist": - return self._get_wordlist(text) - else: - raise ValueError(f"only type: [pronplus, word2phn, wordlist] supported, now type: {self.token_type}") - def _get_pronplus(self, text) -> List[str]: - pronplus = self.g2p.get_frd_extra_info(text, 'pronplus') - if self.remove_boundary: - pronplus = pronplus.replace("/", "") # word boundary - pronplus = pronplus.replace("#", "") # syllable boundary - # pronplus = pronplus.replace("\n", "") - pronplus = pronplus.replace("\n", " ") - symbols: List[str] = [] - for pron in pronplus.split(" "): - pron = pron.strip().lower() - if pron and pron[0].isalpha(): - symbols.append(pron) - else: - symbols.extend([mark for mark in pron if mark]) - return symbols - - def text2tokens(self, line: str) -> List[str]: - json_str = self._get_word2phn(line) - data = json.loads(json_str) - retval = [] - for one in data["word2phn"]: - for key, value in one.items(): - if value is not None: - retval.extend([f"@{x}" for x in value]) - else: - if key == " ": - key = "<|space|>" - retval.append(f"@{key}") - - return retval - - def tokens2text(self, tokens: Iterable[str]) -> str: - pass - - def _get_wordlist(self, text) -> str: - wordlist = self.g2p.get_frd_extra_info(text, 'wordlist') - return wordlist - def _get_word2phn(self, text) -> str: - wordlist = self.g2p.get_frd_extra_info(text, 'wordlist') - wordlist_subs = wordlist.split("\n") - word2phn_info = [] - prev_word_type = None - prev_word = None - for json_str in wordlist_subs: - if len(json_str) == 0: - continue - wordlist_info = json.loads(json_str)["wordlist"] - for word_info in wordlist_info: - is_english_word = True - this_phone_list = None - if word_info["syllables"] is None: - # punctuation - this_word_type = "punc" - pass - elif self.is_full_half_punctuation_string(word_info["name"]): - # punctuation, handle some g2p's mistakes spelling punctuation!!! - this_word_type = "punc" - pass - else: - this_phone_list = [] - for syllable_info in word_info["syllables"]: - phn_count = syllable_info["phone_count"] - syllable_phone_list = syllable_info["pron_text"].split(" ") - assert len(syllable_phone_list) == phn_count, len(syllable_phone_list) - if "py_text" in syllable_info: - # chinese add tone info - syllable_phone_list[-1] = syllable_phone_list[-1]+str(syllable_info["tone"]) - is_english_word = False - this_phone_list += syllable_phone_list - if is_english_word: - this_word_type = "en_word" - else: - this_word_type = "ch_word" - if this_word_type == "en_word": - if prev_word_type is None: - pass - elif prev_word_type == "en_word": - word2phn_info.append({" ": None}) - elif prev_word_type == "punc": - if (prev_word not in ["\"", "\'", "(", "（", "[", "【"] and - prev_word.split(" ")[-1] not in ["\"", "\'", "(", "（", "[", "【"]): - word2phn_info.append({" ": None}) - elif prev_word_type == "ch_word": - word2phn_info.append({" ": None}) - elif this_word_type == "ch_word": - if prev_word_type is not None and prev_word_type == "en_word": - word2phn_info.append({" ": None}) - elif this_word_type == "punc": - if word_info["name"] in ["("]: - word2phn_info.append({" ": None}) - this_word2phn_dict = {word_info["name"]: this_phone_list} - word2phn_info.append(this_word2phn_dict) - prev_word_type = this_word_type - prev_word = list(word2phn_info[-1].keys())[0] - return json.dumps({"raw": text, "word2phn": word2phn_info}, ensure_ascii=False) diff --git a/funasr/models/llm_asr/tts_text_tokenizer/voice_echo_rich_tokenizer.py b/funasr/models/llm_asr/tts_text_tokenizer/voice_echo_rich_tokenizer.py deleted file mode 100644 index be0434ad9..000000000 --- a/funasr/models/llm_asr/tts_text_tokenizer/voice_echo_rich_tokenizer.py +++ /dev/null @@ -1,462 +0,0 @@ -import base64 -import os -import string -from dataclasses import dataclass, field -from functools import cached_property, lru_cache -from typing import Dict, List, Optional, Tuple - -import tiktoken - -LANGUAGES = { - "en": "english", - "zh": "chinese", - "de": "german", - "es": "spanish", - "ru": "russian", - "ko": "korean", - "fr": "french", - "ja": "japanese", - "pt": "portuguese", - "tr": "turkish", - "pl": "polish", - "ca": "catalan", - "nl": "dutch", - "ar": "arabic", - "sv": "swedish", - "it": "italian", - "id": "indonesian", - "hi": "hindi", - "fi": "finnish", - "vi": "vietnamese", - "he": "hebrew", - "uk": "ukrainian", - "el": "greek", - "ms": "malay", - "cs": "czech", - "ro": "romanian", - "da": "danish", - "hu": "hungarian", - "ta": "tamil", - "no": "norwegian", - "th": "thai", - "ur": "urdu", - "hr": "croatian", - "bg": "bulgarian", - "lt": "lithuanian", - "la": "latin", - "mi": "maori", - "ml": "malayalam", - "cy": "welsh", - "sk": "slovak", - "te": "telugu", - "fa": "persian", - "lv": "latvian", - "bn": "bengali", - "sr": "serbian", - "az": "azerbaijani", - "sl": "slovenian", - "kn": "kannada", - "et": "estonian", - "mk": "macedonian", - "br": "breton", - "eu": "basque", - "is": "icelandic", - "hy": "armenian", - "ne": "nepali", - "mn": "mongolian", - "bs": "bosnian", - "kk": "kazakh", - "sq": "albanian", - "sw": "swahili", - "gl": "galician", - "mr": "marathi", - "pa": "punjabi", - "si": "sinhala", - "km": "khmer", - "sn": "shona", - "yo": "yoruba", - "so": "somali", - "af": "afrikaans", - "oc": "occitan", - "ka": "georgian", - "be": "belarusian", - "tg": "tajik", - "sd": "sindhi", - "gu": "gujarati", - "am": "amharic", - "yi": "yiddish", - "lo": "lao", - "uz": "uzbek", - "fo": "faroese", - "ht": "haitian creole", - "ps": "pashto", - "tk": "turkmen", - "nn": "nynorsk", - "mt": "maltese", - "sa": "sanskrit", - "lb": "luxembourgish", - "my": "myanmar", - "bo": "tibetan", - "tl": "tagalog", - "mg": "malagasy", - "as": "assamese", - "tt": "tatar", - "haw": "hawaiian", - "ln": "lingala", - "ha": "hausa", - "ba": "bashkir", - "jw": "javanese", - "su": "sundanese", - "yue": "cantonese", - "minnan": "minnan", - "wuyu": "wuyu", - "dialect": "dialect", - "zh/en": "zh/en", - "en/zh": "en/zh", -} - -# language code lookup by name, with a few language aliases -TO_LANGUAGE_CODE = { - **{language: code for code, language in LANGUAGES.items()}, - "burmese": "my", - "valencian": "ca", - "flemish": "nl", - "haitian": "ht", - "letzeburgesch": "lb", - "pushto": "ps", - "panjabi": "pa", - "moldavian": "ro", - "moldovan": "ro", - "sinhalese": "si", - "castilian": "es", - "mandarin": "zh", -} - -AUDIO_EVENT = { - "ASR": "ASR", - "AED": "AED", - "SER": "SER", - "Speech": "Speech", - "/Speech": "/Speech", - "BGM": "BGM", - "/BGM": "/BGM", - "Laughter": "Laughter", - "/Laughter": "/Laughter", - "Applause": "Applause", - "/Applause": "/Applause", -} - -EMOTION = { - "HAPPY": "HAPPY", - "SAD": "SAD", - "ANGRY": "ANGRY", - "NEUTRAL": "NEUTRAL", -} -TTS_Vocal_Token = { - "TTS/B": "TTS/B", - "TTS/O": "TTS/O", - "TTS/Q": "TTS/Q", - "TTS/A": "TTS/A", - "TTS/CO": "TTS/CO", - "TTS/CL": "TTS/CL", - "TTS/H": "TTS/H", - "endofprompt": "endofprompt", - "sil": "sil", - **{f"TTS/SP{i:02d}": f"TTS/SP{i:02d}" for i in range(3, 14)} -} - - -@dataclass -class Tokenizer: - """A thin wrapper around `tiktoken` providing quick access to special tokens""" - - encoding: tiktoken.Encoding - num_languages: int - language: Optional[str] = None - task: Optional[str] = None - sot_sequence: Tuple[int] = () - special_tokens: Dict[str, int] = field(default_factory=dict) - - def __post_init__(self): - for special in self.encoding.special_tokens_set: - special_token = self.encoding.encode_single_token(special) - self.special_tokens[special] = special_token - - sot: int = self.special_tokens["<|startoftranscript|>"] - translate: int = self.special_tokens["<|translate|>"] - transcribe: int = self.special_tokens["<|transcribe|>"] - - langs = tuple(LANGUAGES.keys())[: self.num_languages] - sot_sequence = [sot] - if self.language is not None: - sot_sequence.append(sot + 1 + langs.index(self.language)) - if self.task is not None: - task_token: int = transcribe if self.task == "transcribe" else translate - sot_sequence.append(task_token) - - self.sot_sequence = tuple(sot_sequence) - - def encode(self, text, **kwargs): - return self.encoding.encode(text, **kwargs) - - def decode(self, token_ids: List[int], **kwargs) -> str: - token_ids = [t for t in token_ids if t < self.timestamp_begin] - return self.encoding.decode(token_ids, **kwargs) - - def decode_with_timestamps(self, token_ids: List[int], **kwargs) -> str: - """ - Timestamp tokens are above other special tokens' id range and are ignored by `decode()`. - This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". - """ - return self.encoding.decode(token_ids, **kwargs) - - def get_vocab_size(self) -> int: - return self.encoding.n_vocab - - @cached_property - def eot(self) -> int: - return self.encoding.eot_token - - @cached_property - def transcribe(self) -> int: - return self.special_tokens["<|transcribe|>"] - - @cached_property - def translate(self) -> int: - return self.special_tokens["<|translate|>"] - - @cached_property - def sot(self) -> int: - return self.special_tokens["<|startoftranscript|>"] - - @cached_property - def sot_lm(self) -> int: - return self.special_tokens["<|startoflm|>"] - - @cached_property - def sot_prev(self) -> int: - return self.special_tokens["<|startofprev|>"] - - @cached_property - def no_speech(self) -> int: - return self.special_tokens["<|nospeech|>"] - - @cached_property - def no_timestamps(self) -> int: - return self.special_tokens["<|notimestamps|>"] - - @cached_property - def timestamp_begin(self) -> int: - return self.special_tokens["<|0.00|>"] - - @cached_property - def language_token(self) -> int: - """Returns the token id corresponding to the value of the `language` field""" - if self.language is None: - raise ValueError("This tokenizer does not have language token configured") - - return self.to_language_token(self.language) - - def to_language_token(self, language): - if token := self.special_tokens.get(f"<|{language}|>", None): - return token - - raise KeyError(f"Language {language} not found in tokenizer.") - - @cached_property - def all_language_tokens(self) -> Tuple[int]: - result = [] - for token, token_id in self.special_tokens.items(): - if token.strip("<|>") in LANGUAGES: - result.append(token_id) - return tuple(result)[: self.num_languages] - - @cached_property - def all_language_codes(self) -> Tuple[str]: - return tuple(self.decode([_l]).strip("<|>") for _l in self.all_language_tokens) - - @cached_property - def sot_sequence_including_notimestamps(self) -> Tuple[int]: - return tuple(list(self.sot_sequence) + [self.no_timestamps]) - - @cached_property - def non_speech_tokens(self) -> Tuple[int]: - """ - Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech - annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. - - - ♪♪♪ - - ( SPEAKING FOREIGN LANGUAGE ) - - [DAVID] Hey there, - - keeping basic punctuations like commas, periods, question marks, exclamation points, etc. - """ - symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』') - symbols += ( - "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() - ) - - # symbols that may be a single token or multiple tokens depending on the tokenizer. - # In case they're multiple tokens, suppress the first token, which is safe because: - # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress - # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. - miscellaneous = set("♩♪♫♬♭♮♯") - assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) - - # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word - result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]} - for symbol in symbols + list(miscellaneous): - for tokens in [ - self.encoding.encode(symbol), - self.encoding.encode(" " + symbol), - ]: - if len(tokens) == 1 or symbol in miscellaneous: - result.add(tokens[0]) - - return tuple(sorted(result)) - - def split_to_word_tokens(self, tokens: List[int]): - if self.language in {"zh", "ja", "th", "lo", "my", "yue"}: - # These languages don't typically use spaces, so it is difficult to split words - # without morpheme analysis. Here, we instead split words at any - # position where the tokens are decoded as valid unicode points - return self.split_tokens_on_unicode(tokens) - - return self.split_tokens_on_spaces(tokens) - - def split_tokens_on_unicode(self, tokens: List[int]): - decoded_full = self.decode_with_timestamps(tokens) - replacement_char = "\ufffd" - - words = [] - word_tokens = [] - current_tokens = [] - unicode_offset = 0 - - for token in tokens: - current_tokens.append(token) - decoded = self.decode_with_timestamps(current_tokens) - - if ( - replacement_char not in decoded - or decoded_full[unicode_offset + decoded.index(replacement_char)] - == replacement_char - ): - words.append(decoded) - word_tokens.append(current_tokens) - current_tokens = [] - unicode_offset += len(decoded) - - return words, word_tokens - - def split_tokens_on_spaces(self, tokens: List[int]): - subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens) - words = [] - word_tokens = [] - - for subword, subword_tokens in zip(subwords, subword_tokens_list): - special = subword_tokens[0] >= self.eot - with_space = subword.startswith(" ") - punctuation = subword.strip() in string.punctuation - if special or with_space or punctuation or len(words) == 0: - words.append(subword) - word_tokens.append(subword_tokens) - else: - words[-1] = words[-1] + subword - word_tokens[-1].extend(subword_tokens) - - return words, word_tokens - - -@lru_cache(maxsize=None) -def get_encoding(name: str = "gpt2", num_languages: int = 99, ttsfrd_name: Optional[str] = None): - vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken") - ranks = { - base64.b64decode(token): int(rank) - for token, rank in (line.split() for line in open(vocab_path) if line) - } - n_vocab = len(ranks) - special_tokens = {} - - if name == "gpt2" or name == "multilingual": - specials = [ - "<|endoftext|>", - "<|startoftranscript|>", - *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]], - "<|translate|>", - "<|transcribe|>", - "<|startoflm|>", - "<|startofprev|>", - "<|nospeech|>", - "<|notimestamps|>", - *[f"<|{i * 0.02:.2f}|>" for i in range(1501)], - ] - else: - specials = [ - "<|endoftext|>", - "<|startoftranscript|>", - *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]], - *[f"<|{audio_event}|>" for audio_event in list(AUDIO_EVENT.keys())], - *[f"<|{emotion}|>" for emotion in list(EMOTION.keys())], - "<|translate|>", - "<|transcribe|>", - "<|startoflm|>", - "<|startofprev|>", - "<|nospeech|>", - "<|notimestamps|>", - *[f"<|SPECIAL_TOKEN_{i}|>" for i in range(1, 31)], # register special tokens for ASR - *[f"<|{tts}|>" for tts in list(TTS_Vocal_Token.keys())], # register special tokens for TTS - *[f"<|{i * 0.02:.2f}|>" for i in range(1501)], - ] - if ttsfrd_name is not None: - ttsfrd_vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{ttsfrd_name}.token") - assert os.path.isfile(ttsfrd_vocab_path), f"{ttsfrd_vocab_path} missing" - with open(ttsfrd_vocab_path, "r") as fr: - specials.extend([f"<|{line.strip()}|>" for line in fr if line]) - for token in specials: - special_tokens[token] = n_vocab - n_vocab += 1 - - return tiktoken.Encoding( - name=os.path.basename(vocab_path), - explicit_n_vocab=n_vocab, - pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", - mergeable_ranks=ranks, - special_tokens=special_tokens, - ) - - -@lru_cache(maxsize=None) -def get_tokenizer( - multilingual: bool, - *, - num_languages: int = 99, - language: Optional[str] = None, - task: Optional[str] = None, # Literal["transcribe", "translate", None] - encoding_path: Optional[str] = None, - ttsfrd_name: Optional[str] = None, -) -> Tokenizer: - if language is not None: - language = language.lower() - if language not in LANGUAGES: - if language in TO_LANGUAGE_CODE: - language = TO_LANGUAGE_CODE[language] - else: - raise ValueError(f"Unsupported language: {language}") - - if multilingual: - encoding_name = "multilingual" - language = language or "en" - task = task or "transcribe" - else: - encoding_name = "gpt2" - language = None - task = None - if encoding_path is not None: - encoding_name = encoding_path - - encoding = get_encoding(name=encoding_name, num_languages=num_languages, ttsfrd_name=ttsfrd_name) - - return Tokenizer( - encoding=encoding, num_languages=num_languages, language=language, task=task - ) diff --git a/funasr/models/llm_asr/tts_text_tokenizer/whisper_tokenizer.py b/funasr/models/llm_asr/tts_text_tokenizer/whisper_tokenizer.py deleted file mode 100644 index 01ae2a100..000000000 --- a/funasr/models/llm_asr/tts_text_tokenizer/whisper_tokenizer.py +++ /dev/null @@ -1,164 +0,0 @@ -import copy -import json -import os -import random -import re -from typing import Iterable, List, Union -import numpy as np - - -class WhisperRichTtsFrdTokenizer: - def __init__( - self, - token_path: str, - num_languages: int, - task: str = None, - language: str = None, - ttsfrd_type: str = None, - p_word2phn: float = 0.5, - ttsfrd_model: str = None, - ): - import funasr.models.llm_asr.tts_text_tokenizer.voice_echo_rich_tokenizer as tokenizer - - self.token_path = token_path - self.num_languages = num_languages - self.language = language - self.task = task - self.ttsfrd_type = ttsfrd_type - self.p_word2phn = p_word2phn - # print('token_path:',token_path) - if token_path == "whisper_en" or token_path == "whisper_gpt2" or token_path == "gpt2": - self.tokenizer = tokenizer.get_tokenizer(multilingual=False, num_languages=num_languages) - elif token_path == "whisper_multilingual" or token_path == "multilingual": - self.tokenizer = tokenizer.get_tokenizer( - multilingual=True, language=self.language, task=self.task, num_languages=num_languages - ) - else:# - self.tokenizer = tokenizer.get_tokenizer( - multilingual=True, language=self.language, task=self.task, num_languages=num_languages, - encoding_path=token_path, ttsfrd_name=ttsfrd_type - ) - if ttsfrd_model is not None and os.path.isdir(ttsfrd_model): - from funasr.models.llm_asr.tts_text_tokenizer.phoneme_tokenizer import TtsFrdRich - self.ttsfrd_tokenizer = TtsFrdRich(remove_boundary=True, token_type="word2phn") - self.ttsfrd_tokenizer.build(ttsfrd_model) - else: - self.ttsfrd_tokenizer = None - # self.tokenizer = copy.deepcopy(self.tokenizer) - - - def text_mixing(self, line: str) -> str: - try: - data_info = json.loads(line) - # ttsfrd_word2phn info - if isinstance(data_info, dict) and "raw" in data_info and "word2phn" in data_info: - raw_text = data_info["raw"] - ttsfrd_word2phn = data_info["word2phn"] - if random.random() < self.p_word2phn: - ret_text = "" - for ttsfrd_word in ttsfrd_word2phn: - for word_str, phn_list in ttsfrd_word.items(): - if phn_list is not None: - if random.random() < self.p_word2phn: - ret_text = ret_text + "".join([f"<|@{p}|>" for p in phn_list]) - else: - ret_text += word_str - else: - ret_text += word_str - else: - ret_text = raw_text - else: - ret_text = line - except json.JSONDecodeError: - ret_text = line - return ret_text - - def get_num_vocabulary_size(self) -> int: - return self.tokenizer.get_vocab_size() - - def text2ids(self, line: str, language: str) -> List[int]: - language_tok = "<|" + language + "|>" - assert language_tok in self.tokenizer.special_tokens, "Language token not found, lang: {}, line: {}".format(language_tok, line) - # line = re.sub(r'<(\d+\.\d+)>', r'<|\1|>', line) - pattern = re.compile(r'<|(\d+\.\d+)|>') - with_timestamps = pattern.search(line) - if with_timestamps: - sot_tok = [self.tokenizer.special_tokens.get(language_tok), self.tokenizer.transcribe] - allowed_special = set([f"<|{i * 0.02:.2f}|>" for i in range(1501)]) - encoded_line = self.tokenizer.encode(line, allowed_special=allowed_special) - else: - sot_tok = [self.tokenizer.special_tokens.get(language_tok), self.tokenizer.transcribe, self.tokenizer.no_timestamps] - encoded_line = self.tokenizer.encode(line) - return sot_tok + encoded_line - - def ids2text(self, integers: Union[np.ndarray, Iterable[int]]) -> str: - return self.tokenizer.decode_with_timestamps(integers) - - def ids2tokens(self, integers: Union[np.ndarray, Iterable[int]]) -> List[str]: - return [self.tokenizer.decode_with_timestamps([i]) for i in integers] - - def text2tokens(self, line: str, endofprompt="<|endofprompt|>", sil="<|sil|>") -> List[str]: - # keep prompt and sil unchanged - prompt_text = "" - st_sil, ed_sil = False, False - if endofprompt in line: - pos = line.find(endofprompt) - prompt_text = line[:pos+len(endofprompt)] - line = line[pos+len(endofprompt):] - if line.startswith(sil): - line = line[len(sil):] - st_sil = True - if line.endswith(sil): - line = line[:-len(sil)] - ed_sil = True - - # token to phone and mixup - if self.ttsfrd_tokenizer is not None: - line = self.ttsfrd_tokenizer(line) - if self.ttsfrd_type is not None: - line = self.text_mixing(line) - - # add prompt text and sil back - if st_sil: - line = sil + line - if ed_sil: - line = line + sil - line = prompt_text + line - - return self.tokenizer.encode(line, allowed_special="all") - - def tokens2text(self, tokens: Iterable[str]) -> str: - return self.tokenizer.decode_with_timestamps(tokens) - - # def get_sot(self, sot_template: str, lang: str = None) -> List[int]: - # if lang is not None: - # lang = lang.replace("<", "").replace(">", "").replace("|", "") - # sot = sot_template.replace("LANG", lang) - # else: - # if "<|LANG|>" in sot_template: - # sot = sot_template.split("<|LANG|>", 1)[0] - # else: - # sot = sot_template - # sot_tok = self.tokenizer.encode(sot, allowed_special="all") - # return sot_tok - - def get_sot(self, language: str = None, with_timestamps: bool = False) -> List[int]: - if language is not None: - language_tok = "<|" + language + "|>" - assert language_tok in self.tokenizer.special_tokens - if with_timestamps: - sot_tok = [self.tokenizer.sot, self.tokenizer.special_tokens.get(language_tok), self.tokenizer.transcribe] - else: - sot_tok = [self.tokenizer.sot, self.tokenizer.special_tokens.get(language_tok), self.tokenizer.transcribe, self.tokenizer.no_timestamps] - else: - sot_tok = [self.tokenizer.sot] - return sot_tok - - def get_all_languages(self) -> List[str]: - return list(self.tokenizer.all_language_codes) - - def __repr__(self): - return ( - f"{self.__class__.__name__}(model_type={self.token_path}, " - f"language={self.language}, ttsfrd={self.ttsfrd_type})" - )