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https://github.com/modelscope/FunASR
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funasr1.0 paraformer_streaming
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游雁
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@ -3,36 +3,44 @@
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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# from funasr import AutoModel
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#
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# model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revison="v2.0.0")
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#
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# res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
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# print(res)
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from funasr import AutoModel
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chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
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encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
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decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
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from funasr import AutoFrontend
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frontend = AutoFrontend(model="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revison="v2.0.0")
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model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revison="v2.0.0")
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cache = {}
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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cache=cache,
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is_final=True,
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chunk_size=chunk_size,
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encoder_chunk_look_back=encoder_chunk_look_back,
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decoder_chunk_look_back=decoder_chunk_look_back,
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)
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print(res)
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import soundfile
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speech, sample_rate = soundfile.read("/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/example/asr_example.wav")
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import os
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speech, sample_rate = soundfile.read(os.path.expanduser('~')+
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"/.cache/modelscope/hub/damo/"+
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"speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/"+
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"example/asr_example.wav")
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chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
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chunk_stride = chunk_size[1] * 960 # 600ms、480ms
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# first chunk, 600ms
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cache = {}
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for i in range(int(len((speech)-1)/chunk_stride+1)):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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fbanks = frontend(input=speech_chunk,
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batch_size=2,
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cache=cache)
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# for batch_idx, fbank_dict in enumerate(fbanks):
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# res = model(**fbank_dict)
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# print(res)
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is_final = i == int(len((speech)-1)/chunk_stride+1)
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res = model(input=speech_chunk,
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cache=cache,
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is_final=is_final,
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chunk_size=chunk_size,
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encoder_chunk_look_back=encoder_chunk_look_back,
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decoder_chunk_look_back=decoder_chunk_look_back,
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)
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print(res)
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@ -1,14 +0,0 @@
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
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git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
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python funasr/bin/train.py \
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+model="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
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+token_list="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/tokens.txt" \
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+train_data_set_list="data/list/audio_datasets.jsonl" \
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+output_dir="outputs/debug/ckpt/funasr2/exp2" \
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+device="cpu"
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@ -1,5 +1,5 @@
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model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
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model_revision="v2.0.0"
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python funasr/bin/inference.py \
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@ -205,7 +205,8 @@ class CifPredictorV2(nn.Module):
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return acoustic_embeds, token_num, alphas, cif_peak
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def forward_chunk(self, hidden, cache=None):
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def forward_chunk(self, hidden, cache=None, **kwargs):
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is_final = kwargs.get("is_final", False)
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batch_size, len_time, hidden_size = hidden.shape
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h = hidden
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context = h.transpose(1, 2)
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@ -226,14 +227,14 @@ class CifPredictorV2(nn.Module):
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if cache is not None and "chunk_size" in cache:
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alphas[:, :cache["chunk_size"][0]] = 0.0
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if "is_final" in cache and not cache["is_final"]:
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if not is_final:
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alphas[:, sum(cache["chunk_size"][:2]):] = 0.0
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if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
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cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
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cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
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hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
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alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
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if cache is not None and "is_final" in cache and cache["is_final"]:
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if cache is not None and is_final:
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tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
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tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
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tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
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@ -277,7 +278,7 @@ class CifPredictorV2(nn.Module):
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max_token_len = max(token_length)
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if max_token_len == 0:
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return hidden, torch.stack(token_length, 0)
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return hidden, torch.stack(token_length, 0), None, None
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list_ls = []
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for b in range(batch_size):
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pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)
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@ -291,7 +292,7 @@ class CifPredictorV2(nn.Module):
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cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
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cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
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cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
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return torch.stack(list_ls, 0), torch.stack(token_length, 0)
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return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None
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def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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@ -64,8 +64,8 @@ class ParaformerStreaming(Paraformer):
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super().__init__(*args, **kwargs)
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import pdb;
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pdb.set_trace()
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# import pdb;
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# pdb.set_trace()
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self.sampling_ratio = kwargs.get("sampling_ratio", 0.2)
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@ -375,11 +375,10 @@ class ParaformerStreaming(Paraformer):
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return pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index
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def calc_predictor_chunk(self, encoder_out, encoder_out_lens, cache=None):
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pre_acoustic_embeds, pre_token_length = \
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self.predictor.forward_chunk(encoder_out, cache["encoder"])
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return pre_acoustic_embeds, pre_token_length
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def calc_predictor_chunk(self, encoder_out, encoder_out_lens, cache=None, **kwargs):
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is_final = kwargs.get("is_final", False)
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return self.predictor.forward_chunk(encoder_out, cache["encoder"], is_final=is_final)
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def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
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decoder_outs = self.decoder(
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@ -416,7 +415,7 @@ class ParaformerStreaming(Paraformer):
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"chunk_size": chunk_size}
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cache["decoder"] = cache_decoder
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cache["frontend"] = {}
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cache["prev_samples"] = []
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cache["prev_samples"] = torch.empty(0)
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return cache
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@ -432,12 +431,12 @@ class ParaformerStreaming(Paraformer):
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speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
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# Encoder
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encoder_out, encoder_out_lens = self.encode_chunk(speech, speech_lengths, cache=cache)
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encoder_out, encoder_out_lens = self.encode_chunk(speech, speech_lengths, cache=cache, is_final=kwargs.get("is_final", False))
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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# predictor
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predictor_outs = self.calc_predictor_chunk(encoder_out, encoder_out_lens, cache=cache)
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predictor_outs = self.calc_predictor_chunk(encoder_out, encoder_out_lens, cache=cache, is_final=kwargs.get("is_final", False))
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
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predictor_outs[2], predictor_outs[3]
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pre_token_length = pre_token_length.round().long()
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@ -476,10 +475,7 @@ class ParaformerStreaming(Paraformer):
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)
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nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
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for nbest_idx, hyp in enumerate(nbest_hyps):
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ibest_writer = None
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if ibest_writer is None and kwargs.get("output_dir") is not None:
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writer = DatadirWriter(kwargs.get("output_dir"))
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ibest_writer = writer[f"{nbest_idx + 1}best_recog"]
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# remove sos/eos and get results
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last_pos = -1
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if isinstance(hyp.yseq, list):
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@ -490,22 +486,15 @@ class ParaformerStreaming(Paraformer):
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# remove blank symbol id, which is assumed to be 0
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token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
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if tokenizer is not None:
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# Change integer-ids to tokens
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token = tokenizer.ids2tokens(token_int)
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text = tokenizer.tokens2text(token)
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text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
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result_i = {"key": key[i], "text": text_postprocessed}
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if ibest_writer is not None:
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ibest_writer["token"][key[i]] = " ".join(token)
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# ibest_writer["text"][key[i]] = text
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ibest_writer["text"][key[i]] = text_postprocessed
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else:
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result_i = {"key": key[i], "token_int": token_int}
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results.append(result_i)
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# Change integer-ids to tokens
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token = tokenizer.ids2tokens(token_int)
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# text = tokenizer.tokens2text(token)
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result_i = token
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results.extend(result_i)
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return results
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@ -515,6 +504,7 @@ class ParaformerStreaming(Paraformer):
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key: list = None,
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tokenizer=None,
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frontend=None,
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cache: dict={},
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**kwargs,
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):
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@ -526,9 +516,10 @@ class ParaformerStreaming(Paraformer):
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self.init_beam_search(**kwargs)
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self.nbest = kwargs.get("nbest", 1)
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cache = kwargs.get("cache", {})
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if len(cache) == 0:
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self.init_cache(cache, **kwargs)
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_is_final = kwargs.get("is_final", False)
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meta_data = {}
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chunk_size = kwargs.get("chunk_size", [0, 10, 5])
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@ -542,22 +533,41 @@ class ParaformerStreaming(Paraformer):
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meta_data["load_data"] = f"{time2 - time1:0.3f}"
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assert len(audio_sample_list) == 1, "batch_size must be set 1"
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audio_sample = cache["prev_samples"] + audio_sample_list[0]
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audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
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n = len(audio_sample) // chunk_stride_samples
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m = len(audio_sample) % chunk_stride_samples
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n = len(audio_sample) // chunk_stride_samples + int(_is_final)
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m = len(audio_sample) % chunk_stride_samples * (1-int(_is_final))
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tokens = []
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for i in range(n):
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kwargs["is_final"] = _is_final and i == n -1
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audio_sample_i = audio_sample[i*chunk_stride_samples:(i+1)*chunk_stride_samples]
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# extract fbank feats
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speech, speech_lengths = extract_fbank([audio_sample_i], data_type=kwargs.get("data_type", "sound"),
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frontend=frontend, cache=cache["frontend"])
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frontend=frontend, cache=cache["frontend"], is_final=kwargs["is_final"])
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time3 = time.perf_counter()
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meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
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meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
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result_i = self.generate_chunk(speech, speech_lengths, **kwargs)
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tokens_i = self.generate_chunk(speech, speech_lengths, key=key, tokenizer=tokenizer, cache=cache, frontend=frontend, **kwargs)
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tokens.extend(tokens_i)
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text_postprocessed, _ = postprocess_utils.sentence_postprocess(tokens)
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result_i = {"key": key[0], "text": text_postprocessed}
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result = [result_i]
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cache["prev_samples"] = audio_sample[:-m]
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if _is_final:
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self.init_cache(cache, **kwargs)
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if kwargs.get("output_dir"):
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writer = DatadirWriter(kwargs.get("output_dir"))
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ibest_writer = writer[f"{1}best_recog"]
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ibest_writer["token"][key[0]] = " ".join(tokens)
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ibest_writer["text"][key[0]] = text_postprocessed
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return result, meta_data
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@ -423,7 +423,9 @@ class SANMEncoderChunkOpt(nn.Module):
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xs_pad: torch.Tensor,
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ilens: torch.Tensor,
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cache: dict = None,
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**kwargs,
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):
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is_final = kwargs.get("is_final", False)
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xs_pad *= self.output_size() ** 0.5
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if self.embed is None:
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xs_pad = xs_pad
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@ -43,7 +43,7 @@ def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs:
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elif isinstance(data_or_path_or_list, str) and data_type == "text" and tokenizer is not None:
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data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
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elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
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data_or_path_or_list = np.squeeze(data_or_path_or_list) # [n_samples,]
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data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,]
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else:
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pass
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# print(f"unsupport data type: {data_or_path_or_list}, return raw data")
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