update with main (#1786)

* add cmakelist * add paraformer-torch * add debug for funasr-onnx-offline * fix redefinition of jieba StdExtension.hpp * add loading torch models * update funasr-onnx-offline * add SwitchArg for wss-server * add SwitchArg for funasr-onnx-offline * update cmakelist * update funasr-onnx-offline-rtf * add define condition * add gpu define for offlne-stream * update com define * update offline-stream * update cmakelist * update func CompileHotwordEmbedding * add timestamp for paraformer-torch * add C10_USE_GLOG for paraformer-torch * update paraformer-torch * fix func FunASRWfstDecoderInit * update model.h * fix func FunASRWfstDecoderInit * fix tpass_stream * update paraformer-torch * add bladedisc for funasr-onnx-offline * update comdefine * update funasr-wss-server * add log for torch * fix GetValue BLADEDISC * fix log * update cmakelist * update warmup to 10 * update funasrruntime * add batch_size for wss-server * add batch for bins * add batch for offline-stream * add batch for paraformer * add batch for offline-stream * fix func SetBatchSize * add SetBatchSize for model * add SetBatchSize for model * fix func Forward * fix padding * update funasrruntime * add dec reset for batch * set batch default value * add argv for CutSplit * sort frame_queue * sorted msgs * fix FunOfflineInfer * add dynamic batch for fetch * fix FetchDynamic * update run_server.sh * update run_server.sh * cpp http post server support (#1739) * add cpp http server * add some comment * remove some comments * del debug infos * restore run_server.sh * adapt to new model struct * 修复了onnxruntime在macos下编译失败的错误 (#1748) * Add files via upload 增加macos的编译支持 * Add files via upload 增加macos支持 * Add files via upload target_link_directories(funasr PUBLIC ${ONNXRUNTIME_DIR}/lib) target_link_directories(funasr PUBLIC ${FFMPEG_DIR}/lib) 添加 if(APPLE) 限制 --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> * Delete docs/images/wechat.png * Add files via upload * fixed the issues about seaco-onnx timestamp * fix bug (#1764) 当语音识别结果包含 `http` 时，标点符号预测会把它会被当成 url * fix empty asr result (#1765) 解码结果为空的语音片段，text 用空字符串 * docs * docs * docs * docs * docs * keep empty speech result (#1772) * docs * docs * update wechat QRcode * Add python funasr api support for websocket srv (#1777) * add python funasr_api supoort * change little to README.md * add core tools stream * modified a little * fix bug for timeout * support for buffer decode * add ffmpeg decode for buffer * auto frontend * auto frontend * auto frontend * auto frontend * auto frontend * auto frontend * auto frontend * auto frontend * Dev gzf exp (#1785) * resume from step * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * train_loss_avg train_acc_avg * train_loss_avg train_acc_avg * train_loss_avg train_acc_avg * log step * wav is not exist * wav is not exist * decoding * decoding * decoding * wechat * decoding key * decoding key * decoding key * decoding key * decoding key * decoding key * dynamic batch * start_data_split_i=0 * total_time/accum_grad * total_time/accum_grad * total_time/accum_grad * update avg slice * update avg slice * sensevoice sanm * sensevoice sanm * sensevoice sanm --------- Co-authored-by: 北念 <lzr265946@alibaba-inc.com> * auto frontend --------- Co-authored-by: 雾聪 <wucong.lyb@alibaba-inc.com> Co-authored-by: zhaomingwork <61895407+zhaomingwork@users.noreply.github.com> Co-authored-by: szsteven008 <97944818+szsteven008@users.noreply.github.com> Co-authored-by: Ephemeroptera <605686962@qq.com> Co-authored-by: 彭震东 <zhendong.peng@qq.com> Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com> Co-authored-by: 维石 <shixian.shi@alibaba-inc.com> Co-authored-by: 北念 <lzr265946@alibaba-inc.com>
2025-09-15 14:48:36 +08:00 · 2024-06-06 09:54:35 +08:00 · 2024-06-06 09:54:35 +08:00 · 32e7836645
commit 32e7836645
parent 3b0526e7be
8 changed files with 111 additions and 17 deletions
--- a/funasr/auto/auto_frontend.py
+++ b/funasr/auto/auto_frontend.py
@ -60,7 +60,7 @@ class AutoFrontend:

        result_list = []
        num_samples = len(data_list)
-        pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
+        # pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)

        time0 = time.perf_counter()
        for beg_idx in range(0, num_samples, batch_size):
@ -95,15 +95,15 @@ class AutoFrontend:
                "input": speech,
                "input_len": speech_lengths,
                "key": key_batch,
-                data_type: "fbank",
+                "data_type": "fbank",
            }
            result_list.append(batch)

-            pbar.update(1)
-            description = f"{meta_data}, "
-            pbar.set_description(description)
+            # pbar.update(1)
+            # description = f"{meta_data}, "
+            # pbar.set_description(description)

        time_end = time.perf_counter()
-        pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
+        # pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")

        return result_list
--- a/funasr/datasets/audio_datasets/espnet_samplers.py
+++ b/funasr/datasets/audio_datasets/espnet_samplers.py
@ -147,7 +147,9 @@ class EspnetStyleBatchSampler(DistributedSampler):
        start_idx = self.rank * batches_per_rank
        end_idx = start_idx + batches_per_rank
        rank_batches = buffer_batches[start_idx + self.start_step : end_idx]
+
        self.batch_num = len(rank_batches)
+
        logging.info(
            f"rank: {self.rank}, dataloader start from step: {self.start_step}, batch_num: {end_idx-start_idx}, batch_num_after_step: {len(rank_batches)}"
        )
--- a/funasr/models/llm_asr/adaptor.py
+++ b/funasr/models/llm_asr/adaptor.py
@ -1,5 +1,7 @@
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
+from funasr.models.transformer.utils.nets_utils import make_pad_mask

 from funasr.register import tables

@ -63,3 +65,64 @@ class EncoderProjectorQFormer(nn.Module):
        query_proj = self.norm(self.linear(query_output.last_hidden_state))

        return query_proj
+
+
+@tables.register("adaptor_classes", "Transformer")
+class Transformer(nn.Module):
+    def __init__(
+        self, downsample_rate=2, encoder_dim=1280, llm_dim=4096, ffn_dim: int = 2048, **kwargs
+    ):
+        super().__init__()
+        self.k = downsample_rate
+        self.encoder_dim = encoder_dim
+        self.llm_dim = llm_dim
+        self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
+        self.relu = nn.ReLU()
+        self.linear2 = nn.Linear(ffn_dim, self.llm_dim)
+        from funasr.models.transformer.encoder import EncoderLayer
+        from funasr.models.transformer.attention import MultiHeadedAttention
+        from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
+
+        self.blocks = nn.ModuleList(
+            [
+                EncoderLayer(
+                    llm_dim,
+                    MultiHeadedAttention(
+                        kwargs.get("attention_heads", 8),
+                        llm_dim,
+                        kwargs.get("attention_dropout_rate", 0.0),
+                    ),
+                    PositionwiseFeedForward(
+                        llm_dim,
+                        llm_dim // 4,
+                        kwargs.get("dropout_rate", 0.0),
+                    ),
+                    kwargs.get("dropout_rate", 0.0),
+                )
+                for i in range(kwargs.get("n_layer", 2))
+            ]
+        )
+
+    def forward(self, x, ilens=None):
+
+        batch_size, seq_len, dim = x.size()
+        # num_frames_to_discard = seq_len % self.k
+        chunk_num = (seq_len - 1) // self.k + 1
+        pad_num = chunk_num * self.k - seq_len
+        x = F.pad(x, (0, 0, 0, pad_num, 0, 0), value=0.0)
+        # if num_frames_to_discard > 0:
+        #     x = x[:, :-num_frames_to_discard, :]
+        seq_len = x.size(1)
+
+        x = x.contiguous()
+        x = x.view(batch_size, chunk_num, dim * self.k)
+        x = self.linear1(x)
+        x = self.relu(x)
+        x = self.linear2(x)
+
+        olens = None
+        olens = (ilens - 1) // self.k + 1
+        masks = (~make_pad_mask(olens)[:, None, :]).to(x.device)
+        for layer, block in enumerate(self.blocks):
+            x, masks = block(x, masks)
+        return x, olens
--- a/funasr/models/sense_voice/decoder.py
+++ b/funasr/models/sense_voice/decoder.py
@ -360,6 +360,7 @@ class SenseVoiceDecoder(nn.Module):
        """Score."""
        ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
        logp = self.forward(ys.unsqueeze(0), x.unsqueeze(0), cache=state)
+        logp = torch.log_softmax(logp, dim=-1)
        return logp.squeeze(0)[-1, :], state


--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@ -1264,15 +1264,29 @@ class SenseVoiceSANM(nn.Module):
        if isinstance(task, str):
            task = [task]
        task = "".join([f"<|{x}|>" for x in task])
-        initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
+        
+        sos = kwargs.get("model_conf").get("sos")
+        if isinstance(sos, str):
+            initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")

-        language = DecodingOptions.get("language", None)
-        language = None if language == "auto" else language
+            language = DecodingOptions.get("language", None)
+            language = None if language == "auto" else language

-        sos = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
-        sos_int = tokenizer.encode(sos, allowed_special="all")
+            sos = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
+            sos_int = tokenizer.encode(sos, allowed_special="all")
+        else:
+            language = DecodingOptions.get("language", None)
+            language = None if language == "auto" else language
+            initial_prompt = kwargs.get("initial_prompt", f"{task}")
+            initial_prompt_lid = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
+            initial_prompt_lid_int = tokenizer.encode(initial_prompt_lid, allowed_special="all")
+            sos_int = [sos] + initial_prompt_lid_int
        eos = kwargs.get("model_conf").get("eos")
-        eos_int = tokenizer.encode(eos, allowed_special="all")
+        if isinstance(eos, str):
+            eos_int = tokenizer.encode(eos, allowed_special="all")
+        else:
+            eos_int = [eos]
+
        self.beam_search.sos = sos_int
        self.beam_search.eos = eos_int[0]

@ -1298,7 +1312,7 @@ class SenseVoiceSANM(nn.Module):
        self.beam_search.event_score_ga = DecodingOptions.get("gain_tokens_score", [1, 1, 1, 1])

        encoder_out, encoder_out_lens = self.encode(
-            speech[None, :, :].permute(0, 2, 1), speech_lengths
+            speech[None, :, :], speech_lengths
        )

        if text_token_int is not None:
--- a/funasr/models/sense_voice/whisper_lib/model.py
+++ b/funasr/models/sense_voice/whisper_lib/model.py
@ -27,9 +27,24 @@ class ModelDimensions:
    n_text_layer: int


+# class LayerNorm(nn.LayerNorm):
+#     def forward(self, x: Tensor) -> Tensor:
+#         return super().forward(x.float()).type(x.dtype)
+
+
 class LayerNorm(nn.LayerNorm):
-    def forward(self, x: Tensor) -> Tensor:
-        return super().forward(x.float()).type(x.dtype)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, input):
+        output = F.layer_norm(
+            input.float(),
+            self.normalized_shape,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(input)


 class Linear(nn.Linear):
--- a/funasr/models/transformer/encoder.py
+++ b/funasr/models/transformer/encoder.py
@ -64,7 +64,7 @@ class EncoderLayer(nn.Module):
        stochastic_depth_rate=0.0,
    ):
        """Construct an EncoderLayer object."""
-        super(EncoderLayer, self).__init__()
+        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.norm1 = LayerNorm(size)
--- a/funasr/train_utils/trainer_ds.py
+++ b/funasr/train_utils/trainer_ds.py
@ -621,7 +621,6 @@ class Trainer:
            self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size

    def forward_step(self, model, batch, loss_dict={}):
-        dtype = torch.bfloat16
        with maybe_autocast(dtype=self.dtype, use_deepspeed=self.use_deepspeed):
            retval = model(**batch)