mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
Shi Xian
GitHub
commit
eba1fccfa0
24
README.md
24
README.md
@ -95,9 +95,9 @@ model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
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vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
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punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
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spk_model="cam++", spk_model_revision="v2.0.2")
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res = model(input=f"{model.model_path}/example/asr_example.wav",
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batch_size=64,
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hotword='魔搭')
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res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
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batch_size=64,
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hotword='魔搭')
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print(res)
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```
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Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.
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@ -124,7 +124,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
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res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
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print(res)
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```
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Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
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@ -135,7 +135,7 @@ from funasr import AutoModel
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model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
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wav_file = f"{model.model_path}/example/asr_example.wav"
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res = model(input=wav_file)
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res = model.generate(input=wav_file)
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print(res)
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```
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### Voice Activity Detection (Non-streaming)
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@ -156,7 +156,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
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res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
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if len(res[0]["value"]):
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print(res)
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```
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@ -165,7 +165,7 @@ for i in range(total_chunk_num):
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from funasr import AutoModel
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model = AutoModel(model="ct-punc", model_revision="v2.0.2")
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res = model(input="那今天的会就到这里吧 happy new year 明年见")
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res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
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print(res)
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```
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### Timestamp Prediction
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@ -175,7 +175,7 @@ from funasr import AutoModel
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model = AutoModel(model="fa-zh", model_revision="v2.0.2")
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wav_file = f"{model.model_path}/example/asr_example.wav"
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text_file = f"{model.model_path}/example/text.txt"
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res = model(input=(wav_file, text_file), data_type=("sound", "text"))
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res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
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print(res)
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```
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[//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):)
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@ -229,10 +229,16 @@ The use of pretraining model is subject to [model license](./MODEL_LICENSE)
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}
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@inproceedings{gao22b_interspeech,
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author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
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title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}},
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title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
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year=2022,
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booktitle={Proc. Interspeech 2022},
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pages={2063--2067},
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doi={10.21437/Interspeech.2022-9996}
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}
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@inproceedings{shi2023seaco,
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author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
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title={SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability},
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year={2023},
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booktitle={ICASSP2024}
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}
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```
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18
README_zh.md
18
README_zh.md
@ -91,7 +91,7 @@ model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
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vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
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punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
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spk_model="cam++", spk_model_revision="v2.0.2")
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res = model(input=f"{model.model_path}/example/asr_example.wav",
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res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
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batch_size=64,
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hotword='魔搭')
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print(res)
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@ -121,7 +121,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
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res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
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print(res)
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```
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@ -134,7 +134,7 @@ from funasr import AutoModel
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model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
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wav_file = f"{model.model_path}/example/asr_example.wav"
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res = model(input=wav_file)
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res = model.generate(input=wav_file)
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print(res)
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```
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@ -156,7 +156,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
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res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
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if len(res[0]["value"]):
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print(res)
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```
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@ -167,7 +167,7 @@ from funasr import AutoModel
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model = AutoModel(model="ct-punc", model_revision="v2.0.2")
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res = model(input="那今天的会就到这里吧 happy new year 明年见")
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res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
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print(res)
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```
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@ -179,7 +179,7 @@ model = AutoModel(model="fa-zh", model_revision="v2.0.0")
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wav_file = f"{model.model_path}/example/asr_example.wav"
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text_file = f"{model.model_path}/example/text.txt"
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res = model(input=(wav_file, text_file), data_type=("sound", "text"))
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res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
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print(res)
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```
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更多详细用法（[示例](examples/industrial_data_pretraining)）
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@ -242,4 +242,10 @@ FunASR支持预训练或者进一步微调的模型进行服务部署。目前
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pages={2063--2067},
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doi={10.21437/Interspeech.2022-9996}
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}
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@article{shi2023seaco,
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author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
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title={{SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability}},
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year=2023,
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journal={arXiv preprint arXiv:2308.03266(accepted by ICASSP2024)},
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}
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```
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@ -6,14 +6,14 @@
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from funasr import AutoModel
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model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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model_revision="v2.0.2",
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vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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vad_model_revision="v2.0.2",
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punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
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punc_model_revision="v2.0.2",
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spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
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spk_model_revision="v2.0.2",
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model_revision="v2.0.2",
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vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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vad_model_revision="v2.0.2",
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punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
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punc_model_revision="v2.0.2",
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spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
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spk_model_revision="v2.0.2",
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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_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
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print(res)
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@ -9,5 +9,5 @@ model = AutoModel(model="damo/speech_campplus_sv_zh-cn_16k-common",
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model_revision="v2.0.2",
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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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res = model.generate(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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@ -7,6 +7,6 @@ from funasr import AutoModel
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model = AutoModel(model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="v2.0.2")
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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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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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hotword='达摩院 魔搭')
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print(res)
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@ -7,7 +7,7 @@ from funasr import AutoModel
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model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.2")
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
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print(res)
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@ -15,5 +15,5 @@ from funasr import AutoModel
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model = AutoModel(model="damo/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="v2.0.2")
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
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print(res)
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@ -12,7 +12,7 @@ vads = inputs.split("|")
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rec_result_all = "outputs: "
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cache = {}
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for vad in vads:
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rec_result = model(input=vad, cache=cache)
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rec_result = model.generate(input=vad, cache=cache)
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print(rec_result)
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rec_result_all += rec_result[0]['text']
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@ -7,5 +7,5 @@ from funasr import AutoModel
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model = AutoModel(model="damo/emotion2vec_base", model_revision="v2.0.1")
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs")
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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs")
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print(res)
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@ -9,7 +9,7 @@ wav_file = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audi
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chunk_size = 60000 # ms
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model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.2")
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res = model(input=wav_file, chunk_size=chunk_size, )
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res = model.generate(input=wav_file, chunk_size=chunk_size, )
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print(res)
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@ -28,7 +28,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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res = model(input=speech_chunk,
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res = model.generate(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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@ -7,7 +7,7 @@ from funasr import AutoModel
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model = AutoModel(model="damo/speech_timestamp_prediction-v1-16k-offline", model_revision="v2.0.2")
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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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res = model.generate(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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"欢迎大家来到魔搭社区进行体验"),
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data_type=("sound", "text"),
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batch_size=2,
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@ -15,6 +15,6 @@ model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-co
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spk_model_revision="v2.0.2"
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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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hotword='达摩院 磨搭')
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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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hotword='达摩院 磨搭')
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print(res)
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@ -7,7 +7,7 @@ from funasr import AutoModel
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model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.2")
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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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res = model.generate(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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@ -18,5 +18,5 @@ frontend = AutoFrontend(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-co
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fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2)
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for batch_idx, fbank_dict in enumerate(fbanks):
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res = model(**fbank_dict)
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res = model.generate(**fbank_dict)
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print(res)
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@ -11,7 +11,7 @@ decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cr
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model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="v2.0.2")
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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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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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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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@ -32,11 +32,11 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 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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res = model.generate(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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|
||||
@ -15,6 +15,6 @@ model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-co
|
||||
spk_model_revision="v2.0.2",
|
||||
)
|
||||
|
||||
res = model(input=f"{model.model_path}/example/asr_example.wav",
|
||||
hotword='达摩院 魔搭')
|
||||
res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
|
||||
hotword='达摩院 魔搭')
|
||||
print(res)
|
||||
@ -30,4 +30,5 @@ def import_submodules(package, recursive=True):
|
||||
|
||||
import_submodules(__name__)
|
||||
|
||||
from funasr.bin.inference import AutoModel, AutoFrontend
|
||||
from funasr.auto.auto_model import AutoModel
|
||||
from funasr.auto.auto_frontend import AutoFrontend
|
||||
0
funasr/auto/__init__.py
Normal file
0
funasr/auto/__init__.py
Normal file
95
funasr/auto/auto_frontend.py
Normal file
95
funasr/auto/auto_frontend.py
Normal file
@ -0,0 +1,95 @@
|
||||
import json
|
||||
import time
|
||||
import torch
|
||||
import hydra
|
||||
import random
|
||||
import string
|
||||
import logging
|
||||
import os.path
|
||||
from tqdm import tqdm
|
||||
from omegaconf import DictConfig, OmegaConf, ListConfig
|
||||
|
||||
from funasr.register import tables
|
||||
from funasr.utils.load_utils import load_bytes
|
||||
from funasr.download.file import download_from_url
|
||||
from funasr.download.download_from_hub import download_model
|
||||
from funasr.utils.vad_utils import slice_padding_audio_samples
|
||||
from funasr.train_utils.set_all_random_seed import set_all_random_seed
|
||||
from funasr.train_utils.load_pretrained_model import load_pretrained_model
|
||||
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
||||
from funasr.utils.timestamp_tools import timestamp_sentence
|
||||
from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
|
||||
from funasr.models.campplus.cluster_backend import ClusterBackend
|
||||
from funasr.auto.auto_model import prepare_data_iterator
|
||||
|
||||
|
||||
|
||||
class AutoFrontend:
|
||||
def __init__(self, **kwargs):
|
||||
assert "model" in kwargs
|
||||
if "model_conf" not in kwargs:
|
||||
logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
|
||||
kwargs = download_model(**kwargs)
|
||||
|
||||
# build frontend
|
||||
frontend = kwargs.get("frontend", None)
|
||||
if frontend is not None:
|
||||
frontend_class = tables.frontend_classes.get(frontend)
|
||||
frontend = frontend_class(**kwargs["frontend_conf"])
|
||||
|
||||
self.frontend = frontend
|
||||
if "frontend" in kwargs:
|
||||
del kwargs["frontend"]
|
||||
self.kwargs = kwargs
|
||||
|
||||
|
||||
def __call__(self, input, input_len=None, kwargs=None, **cfg):
|
||||
|
||||
kwargs = self.kwargs if kwargs is None else kwargs
|
||||
kwargs.update(cfg)
|
||||
|
||||
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len)
|
||||
batch_size = kwargs.get("batch_size", 1)
|
||||
device = kwargs.get("device", "cpu")
|
||||
if device == "cpu":
|
||||
batch_size = 1
|
||||
|
||||
meta_data = {}
|
||||
|
||||
result_list = []
|
||||
num_samples = len(data_list)
|
||||
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):
|
||||
end_idx = min(num_samples, beg_idx + batch_size)
|
||||
data_batch = data_list[beg_idx:end_idx]
|
||||
key_batch = key_list[beg_idx:end_idx]
|
||||
|
||||
# extract fbank feats
|
||||
time1 = time.perf_counter()
|
||||
audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
|
||||
time2 = time.perf_counter()
|
||||
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
||||
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
|
||||
frontend=self.frontend, **kwargs)
|
||||
time3 = time.perf_counter()
|
||||
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||||
meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
|
||||
|
||||
speech.to(device=device), speech_lengths.to(device=device)
|
||||
batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
|
||||
result_list.append(batch)
|
||||
|
||||
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}")
|
||||
|
||||
return result_list
|
||||
|
||||
416
funasr/auto/auto_model.py
Normal file
416
funasr/auto/auto_model.py
Normal file
@ -0,0 +1,416 @@
|
||||
import json
|
||||
import time
|
||||
import torch
|
||||
import hydra
|
||||
import random
|
||||
import string
|
||||
import logging
|
||||
import os.path
|
||||
from tqdm import tqdm
|
||||
from omegaconf import DictConfig, OmegaConf, ListConfig
|
||||
|
||||
from funasr.register import tables
|
||||
from funasr.utils.load_utils import load_bytes
|
||||
from funasr.download.file import download_from_url
|
||||
from funasr.download.download_from_hub import download_model
|
||||
from funasr.utils.vad_utils import slice_padding_audio_samples
|
||||
from funasr.train_utils.set_all_random_seed import set_all_random_seed
|
||||
from funasr.train_utils.load_pretrained_model import load_pretrained_model
|
||||
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
||||
from funasr.utils.timestamp_tools import timestamp_sentence
|
||||
from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
|
||||
from funasr.models.campplus.cluster_backend import ClusterBackend
|
||||
|
||||
|
||||
def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
|
||||
"""
|
||||
|
||||
:param input:
|
||||
:param input_len:
|
||||
:param data_type:
|
||||
:param frontend:
|
||||
:return:
|
||||
"""
|
||||
data_list = []
|
||||
key_list = []
|
||||
filelist = [".scp", ".txt", ".json", ".jsonl"]
|
||||
|
||||
chars = string.ascii_letters + string.digits
|
||||
if isinstance(data_in, str) and data_in.startswith('http'): # url
|
||||
data_in = download_from_url(data_in)
|
||||
if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
|
||||
_, file_extension = os.path.splitext(data_in)
|
||||
file_extension = file_extension.lower()
|
||||
if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
|
||||
with open(data_in, encoding='utf-8') as fin:
|
||||
for line in fin:
|
||||
key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
|
||||
if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
|
||||
lines = json.loads(line.strip())
|
||||
data = lines["source"]
|
||||
key = data["key"] if "key" in data else key
|
||||
else: # filelist, wav.scp, text.txt: id \t data or data
|
||||
lines = line.strip().split(maxsplit=1)
|
||||
data = lines[1] if len(lines)>1 else lines[0]
|
||||
key = lines[0] if len(lines)>1 else key
|
||||
|
||||
data_list.append(data)
|
||||
key_list.append(key)
|
||||
else:
|
||||
key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
|
||||
data_list = [data_in]
|
||||
key_list = [key]
|
||||
elif isinstance(data_in, (list, tuple)):
|
||||
if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
|
||||
data_list_tmp = []
|
||||
for data_in_i, data_type_i in zip(data_in, data_type):
|
||||
key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
|
||||
data_list_tmp.append(data_list_i)
|
||||
data_list = []
|
||||
for item in zip(*data_list_tmp):
|
||||
data_list.append(item)
|
||||
else:
|
||||
# [audio sample point, fbank, text]
|
||||
data_list = data_in
|
||||
key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
|
||||
else: # raw text; audio sample point, fbank; bytes
|
||||
if isinstance(data_in, bytes): # audio bytes
|
||||
data_in = load_bytes(data_in)
|
||||
if key is None:
|
||||
key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
|
||||
data_list = [data_in]
|
||||
key_list = [key]
|
||||
|
||||
return key_list, data_list
|
||||
|
||||
|
||||
class AutoModel:
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
tables.print()
|
||||
|
||||
model, kwargs = self.build_model(**kwargs)
|
||||
|
||||
# if vad_model is not None, build vad model else None
|
||||
vad_model = kwargs.get("vad_model", None)
|
||||
vad_kwargs = kwargs.get("vad_model_revision", None)
|
||||
if vad_model is not None:
|
||||
logging.info("Building VAD model.")
|
||||
vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
|
||||
vad_model, vad_kwargs = self.build_model(**vad_kwargs)
|
||||
|
||||
# if punc_model is not None, build punc model else None
|
||||
punc_model = kwargs.get("punc_model", None)
|
||||
punc_kwargs = kwargs.get("punc_model_revision", None)
|
||||
if punc_model is not None:
|
||||
logging.info("Building punc model.")
|
||||
punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
|
||||
punc_model, punc_kwargs = self.build_model(**punc_kwargs)
|
||||
|
||||
# if spk_model is not None, build spk model else None
|
||||
spk_model = kwargs.get("spk_model", None)
|
||||
spk_kwargs = kwargs.get("spk_model_revision", None)
|
||||
if spk_model is not None:
|
||||
logging.info("Building SPK model.")
|
||||
spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
|
||||
spk_model, spk_kwargs = self.build_model(**spk_kwargs)
|
||||
self.cb_model = ClusterBackend()
|
||||
spk_mode = kwargs.get("spk_mode", 'punc_segment')
|
||||
if spk_mode not in ["default", "vad_segment", "punc_segment"]:
|
||||
logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
|
||||
self.spk_mode = spk_mode
|
||||
self.preset_spk_num = kwargs.get("preset_spk_num", None)
|
||||
if self.preset_spk_num:
|
||||
logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
|
||||
logging.warning("Many to print when using speaker model...")
|
||||
|
||||
self.kwargs = kwargs
|
||||
self.model = model
|
||||
self.vad_model = vad_model
|
||||
self.vad_kwargs = vad_kwargs
|
||||
self.punc_model = punc_model
|
||||
self.punc_kwargs = punc_kwargs
|
||||
self.spk_model = spk_model
|
||||
self.spk_kwargs = spk_kwargs
|
||||
self.model_path = kwargs["model_path"]
|
||||
|
||||
|
||||
def build_model(self, **kwargs):
|
||||
assert "model" in kwargs
|
||||
if "model_conf" not in kwargs:
|
||||
logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
|
||||
kwargs = download_model(**kwargs)
|
||||
|
||||
set_all_random_seed(kwargs.get("seed", 0))
|
||||
|
||||
device = kwargs.get("device", "cuda")
|
||||
if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
|
||||
device = "cpu"
|
||||
# kwargs["batch_size"] = 1
|
||||
kwargs["device"] = device
|
||||
|
||||
if kwargs.get("ncpu", None):
|
||||
torch.set_num_threads(kwargs.get("ncpu"))
|
||||
|
||||
# build tokenizer
|
||||
tokenizer = kwargs.get("tokenizer", None)
|
||||
if tokenizer is not None:
|
||||
tokenizer_class = tables.tokenizer_classes.get(tokenizer)
|
||||
tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
|
||||
kwargs["tokenizer"] = tokenizer
|
||||
kwargs["token_list"] = tokenizer.token_list
|
||||
vocab_size = len(tokenizer.token_list)
|
||||
else:
|
||||
vocab_size = -1
|
||||
|
||||
# build frontend
|
||||
frontend = kwargs.get("frontend", None)
|
||||
if frontend is not None:
|
||||
frontend_class = tables.frontend_classes.get(frontend)
|
||||
frontend = frontend_class(**kwargs["frontend_conf"])
|
||||
kwargs["frontend"] = frontend
|
||||
kwargs["input_size"] = frontend.output_size()
|
||||
|
||||
# build model
|
||||
model_class = tables.model_classes.get(kwargs["model"])
|
||||
model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
|
||||
model.eval()
|
||||
model.to(device)
|
||||
|
||||
# init_param
|
||||
init_param = kwargs.get("init_param", None)
|
||||
if init_param is not None:
|
||||
logging.info(f"Loading pretrained params from {init_param}")
|
||||
load_pretrained_model(
|
||||
model=model,
|
||||
init_param=init_param,
|
||||
ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
|
||||
oss_bucket=kwargs.get("oss_bucket", None),
|
||||
)
|
||||
|
||||
return model, kwargs
|
||||
|
||||
def __call__(self, *args, **cfg):
|
||||
kwargs = self.kwargs
|
||||
kwargs.update(cfg)
|
||||
res = self.model(*args, kwargs)
|
||||
return res
|
||||
|
||||
|
||||
|
||||
def generate(self, input, input_len=None, **cfg):
|
||||
if self.vad_model is None:
|
||||
return self.inference(input, input_len=input_len, **cfg)
|
||||
|
||||
else:
|
||||
return self.inference_with_vad(input, input_len=input_len, **cfg)
|
||||
|
||||
def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
|
||||
kwargs = self.kwargs if kwargs is None else kwargs
|
||||
kwargs.update(cfg)
|
||||
model = self.model if model is None else model
|
||||
|
||||
batch_size = kwargs.get("batch_size", 1)
|
||||
# if kwargs.get("device", "cpu") == "cpu":
|
||||
# batch_size = 1
|
||||
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
|
||||
|
||||
speed_stats = {}
|
||||
asr_result_list = []
|
||||
num_samples = len(data_list)
|
||||
pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
|
||||
time_speech_total = 0.0
|
||||
time_escape_total = 0.0
|
||||
for beg_idx in range(0, num_samples, batch_size):
|
||||
end_idx = min(num_samples, beg_idx + batch_size)
|
||||
data_batch = data_list[beg_idx:end_idx]
|
||||
key_batch = key_list[beg_idx:end_idx]
|
||||
batch = {"data_in": data_batch, "key": key_batch}
|
||||
if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
|
||||
batch["data_in"] = data_batch[0]
|
||||
batch["data_lengths"] = input_len
|
||||
|
||||
time1 = time.perf_counter()
|
||||
with torch.no_grad():
|
||||
results, meta_data = model.inference(**batch, **kwargs)
|
||||
time2 = time.perf_counter()
|
||||
|
||||
asr_result_list.extend(results)
|
||||
pbar.update(1)
|
||||
|
||||
# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
|
||||
batch_data_time = meta_data.get("batch_data_time", -1)
|
||||
time_escape = time2 - time1
|
||||
speed_stats["load_data"] = meta_data.get("load_data", 0.0)
|
||||
speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
|
||||
speed_stats["forward"] = f"{time_escape:0.3f}"
|
||||
speed_stats["batch_size"] = f"{len(results)}"
|
||||
speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
|
||||
description = (
|
||||
f"{speed_stats}, "
|
||||
)
|
||||
pbar.set_description(description)
|
||||
time_speech_total += batch_data_time
|
||||
time_escape_total += time_escape
|
||||
|
||||
pbar.update(1)
|
||||
pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
|
||||
torch.cuda.empty_cache()
|
||||
return asr_result_list
|
||||
|
||||
def inference_with_vad(self, input, input_len=None, **cfg):
|
||||
|
||||
# step.1: compute the vad model
|
||||
self.vad_kwargs.update(cfg)
|
||||
beg_vad = time.time()
|
||||
res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
|
||||
end_vad = time.time()
|
||||
print(f"time cost vad: {end_vad - beg_vad:0.3f}")
|
||||
|
||||
|
||||
# step.2 compute asr model
|
||||
model = self.model
|
||||
kwargs = self.kwargs
|
||||
kwargs.update(cfg)
|
||||
batch_size = int(kwargs.get("batch_size_s", 300))*1000
|
||||
batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
|
||||
kwargs["batch_size"] = batch_size
|
||||
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
|
||||
results_ret_list = []
|
||||
time_speech_total_all_samples = 0.0
|
||||
|
||||
beg_total = time.time()
|
||||
pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
|
||||
for i in range(len(res)):
|
||||
key = res[i]["key"]
|
||||
vadsegments = res[i]["value"]
|
||||
input_i = data_list[i]
|
||||
speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
|
||||
speech_lengths = len(speech)
|
||||
n = len(vadsegments)
|
||||
data_with_index = [(vadsegments[i], i) for i in range(n)]
|
||||
sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
|
||||
results_sorted = []
|
||||
|
||||
if not len(sorted_data):
|
||||
logging.info("decoding, utt: {}, empty speech".format(key))
|
||||
continue
|
||||
|
||||
if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
|
||||
batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
|
||||
|
||||
batch_size_ms_cum = 0
|
||||
beg_idx = 0
|
||||
beg_asr_total = time.time()
|
||||
time_speech_total_per_sample = speech_lengths/16000
|
||||
time_speech_total_all_samples += time_speech_total_per_sample
|
||||
|
||||
for j, _ in enumerate(range(0, n)):
|
||||
batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
|
||||
if j < n - 1 and (
|
||||
batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
|
||||
sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
|
||||
continue
|
||||
batch_size_ms_cum = 0
|
||||
end_idx = j + 1
|
||||
speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
|
||||
results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
|
||||
if self.spk_model is not None:
|
||||
all_segments = []
|
||||
# compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
|
||||
for _b in range(len(speech_j)):
|
||||
vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
|
||||
sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
|
||||
speech_j[_b]]]
|
||||
segments = sv_chunk(vad_segments)
|
||||
all_segments.extend(segments)
|
||||
speech_b = [i[2] for i in segments]
|
||||
spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
|
||||
results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
|
||||
beg_idx = end_idx
|
||||
if len(results) < 1:
|
||||
continue
|
||||
results_sorted.extend(results)
|
||||
|
||||
|
||||
pbar_total.update(1)
|
||||
end_asr_total = time.time()
|
||||
time_escape_total_per_sample = end_asr_total - beg_asr_total
|
||||
pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
|
||||
f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
|
||||
f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
|
||||
|
||||
restored_data = [0] * n
|
||||
for j in range(n):
|
||||
index = sorted_data[j][1]
|
||||
restored_data[index] = results_sorted[j]
|
||||
result = {}
|
||||
|
||||
# results combine for texts, timestamps, speaker embeddings and others
|
||||
# TODO: rewrite for clean code
|
||||
for j in range(n):
|
||||
for k, v in restored_data[j].items():
|
||||
if k.startswith("timestamp"):
|
||||
if k not in result:
|
||||
result[k] = []
|
||||
for t in restored_data[j][k]:
|
||||
t[0] += vadsegments[j][0]
|
||||
t[1] += vadsegments[j][0]
|
||||
result[k].extend(restored_data[j][k])
|
||||
elif k == 'spk_embedding':
|
||||
if k not in result:
|
||||
result[k] = restored_data[j][k]
|
||||
else:
|
||||
result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
|
||||
elif k == 'text':
|
||||
if k not in result:
|
||||
result[k] = restored_data[j][k]
|
||||
else:
|
||||
result[k] += " " + restored_data[j][k]
|
||||
else:
|
||||
if k not in result:
|
||||
result[k] = restored_data[j][k]
|
||||
else:
|
||||
result[k] += restored_data[j][k]
|
||||
|
||||
# step.3 compute punc model
|
||||
if self.punc_model is not None:
|
||||
self.punc_kwargs.update(cfg)
|
||||
punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
|
||||
result["text_with_punc"] = punc_res[0]["text"]
|
||||
|
||||
# speaker embedding cluster after resorted
|
||||
if self.spk_model is not None:
|
||||
all_segments = sorted(all_segments, key=lambda x: x[0])
|
||||
spk_embedding = result['spk_embedding']
|
||||
labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
|
||||
del result['spk_embedding']
|
||||
sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
|
||||
if self.spk_mode == 'vad_segment':
|
||||
sentence_list = []
|
||||
for res, vadsegment in zip(restored_data, vadsegments):
|
||||
sentence_list.append({"start": vadsegment[0],\
|
||||
"end": vadsegment[1],
|
||||
"sentence": res['text'],
|
||||
"timestamp": res['timestamp']})
|
||||
else: # punc_segment
|
||||
sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
|
||||
result['timestamp'], \
|
||||
result['text'])
|
||||
distribute_spk(sentence_list, sv_output)
|
||||
result['sentence_info'] = sentence_list
|
||||
|
||||
result["key"] = key
|
||||
results_ret_list.append(result)
|
||||
pbar_total.update(1)
|
||||
|
||||
pbar_total.update(1)
|
||||
end_total = time.time()
|
||||
time_escape_total_all_samples = end_total - beg_total
|
||||
pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
|
||||
f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
|
||||
f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
|
||||
return results_ret_list
|
||||
|
||||
8
funasr/auto/auto_tokenizer.py
Normal file
8
funasr/auto/auto_tokenizer.py
Normal file
@ -0,0 +1,8 @@
|
||||
|
||||
|
||||
class AutoTokenizer:
|
||||
"""
|
||||
Undo
|
||||
"""
|
||||
def __init__(self):
|
||||
pass
|
||||
@ -1,88 +1,10 @@
|
||||
import json
|
||||
import time
|
||||
import torch
|
||||
import hydra
|
||||
import random
|
||||
import string
|
||||
import logging
|
||||
import os.path
|
||||
from tqdm import tqdm
|
||||
from omegaconf import DictConfig, OmegaConf, ListConfig
|
||||
|
||||
from funasr.register import tables
|
||||
from funasr.utils.load_utils import load_bytes
|
||||
from funasr.download.file import download_from_url
|
||||
from funasr.download.download_from_hub import download_model
|
||||
from funasr.utils.vad_utils import slice_padding_audio_samples
|
||||
from funasr.train_utils.set_all_random_seed import set_all_random_seed
|
||||
from funasr.train_utils.load_pretrained_model import load_pretrained_model
|
||||
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
||||
from funasr.utils.timestamp_tools import timestamp_sentence
|
||||
from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
|
||||
from funasr.models.campplus.cluster_backend import ClusterBackend
|
||||
from funasr.auto.auto_model import AutoModel
|
||||
|
||||
|
||||
def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
|
||||
"""
|
||||
|
||||
:param input:
|
||||
:param input_len:
|
||||
:param data_type:
|
||||
:param frontend:
|
||||
:return:
|
||||
"""
|
||||
data_list = []
|
||||
key_list = []
|
||||
filelist = [".scp", ".txt", ".json", ".jsonl"]
|
||||
|
||||
chars = string.ascii_letters + string.digits
|
||||
if isinstance(data_in, str) and data_in.startswith('http'): # url
|
||||
data_in = download_from_url(data_in)
|
||||
if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
|
||||
_, file_extension = os.path.splitext(data_in)
|
||||
file_extension = file_extension.lower()
|
||||
if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
|
||||
with open(data_in, encoding='utf-8') as fin:
|
||||
for line in fin:
|
||||
key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
|
||||
if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
|
||||
lines = json.loads(line.strip())
|
||||
data = lines["source"]
|
||||
key = data["key"] if "key" in data else key
|
||||
else: # filelist, wav.scp, text.txt: id \t data or data
|
||||
lines = line.strip().split(maxsplit=1)
|
||||
data = lines[1] if len(lines)>1 else lines[0]
|
||||
key = lines[0] if len(lines)>1 else key
|
||||
|
||||
data_list.append(data)
|
||||
key_list.append(key)
|
||||
else:
|
||||
key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
|
||||
data_list = [data_in]
|
||||
key_list = [key]
|
||||
elif isinstance(data_in, (list, tuple)):
|
||||
if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
|
||||
data_list_tmp = []
|
||||
for data_in_i, data_type_i in zip(data_in, data_type):
|
||||
key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
|
||||
data_list_tmp.append(data_list_i)
|
||||
data_list = []
|
||||
for item in zip(*data_list_tmp):
|
||||
data_list.append(item)
|
||||
else:
|
||||
# [audio sample point, fbank, text]
|
||||
data_list = data_in
|
||||
key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
|
||||
else: # raw text; audio sample point, fbank; bytes
|
||||
if isinstance(data_in, bytes): # audio bytes
|
||||
data_in = load_bytes(data_in)
|
||||
if key is None:
|
||||
key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
|
||||
data_list = [data_in]
|
||||
key_list = [key]
|
||||
|
||||
return key_list, data_list
|
||||
|
||||
@hydra.main(config_name=None, version_base=None)
|
||||
def main_hydra(cfg: DictConfig):
|
||||
def to_plain_list(cfg_item):
|
||||
@ -101,401 +23,9 @@ def main_hydra(cfg: DictConfig):
|
||||
if kwargs.get("debug", False):
|
||||
import pdb; pdb.set_trace()
|
||||
model = AutoModel(**kwargs)
|
||||
res = model(input=kwargs["input"])
|
||||
res = model.generate(input=kwargs["input"])
|
||||
print(res)
|
||||
|
||||
class AutoModel:
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
tables.print()
|
||||
|
||||
model, kwargs = self.build_model(**kwargs)
|
||||
|
||||
# if vad_model is not None, build vad model else None
|
||||
vad_model = kwargs.get("vad_model", None)
|
||||
vad_kwargs = kwargs.get("vad_model_revision", None)
|
||||
if vad_model is not None:
|
||||
logging.info("Building VAD model.")
|
||||
vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
|
||||
vad_model, vad_kwargs = self.build_model(**vad_kwargs)
|
||||
|
||||
# if punc_model is not None, build punc model else None
|
||||
punc_model = kwargs.get("punc_model", None)
|
||||
punc_kwargs = kwargs.get("punc_model_revision", None)
|
||||
if punc_model is not None:
|
||||
logging.info("Building punc model.")
|
||||
punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
|
||||
punc_model, punc_kwargs = self.build_model(**punc_kwargs)
|
||||
|
||||
# if spk_model is not None, build spk model else None
|
||||
spk_model = kwargs.get("spk_model", None)
|
||||
spk_kwargs = kwargs.get("spk_model_revision", None)
|
||||
if spk_model is not None:
|
||||
logging.info("Building SPK model.")
|
||||
spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
|
||||
spk_model, spk_kwargs = self.build_model(**spk_kwargs)
|
||||
self.cb_model = ClusterBackend()
|
||||
spk_mode = kwargs.get("spk_mode", 'punc_segment')
|
||||
if spk_mode not in ["default", "vad_segment", "punc_segment"]:
|
||||
logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
|
||||
self.spk_mode = spk_mode
|
||||
self.preset_spk_num = kwargs.get("preset_spk_num", None)
|
||||
if self.preset_spk_num:
|
||||
logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
|
||||
logging.warning("Many to print when using speaker model...")
|
||||
|
||||
self.kwargs = kwargs
|
||||
self.model = model
|
||||
self.vad_model = vad_model
|
||||
self.vad_kwargs = vad_kwargs
|
||||
self.punc_model = punc_model
|
||||
self.punc_kwargs = punc_kwargs
|
||||
self.spk_model = spk_model
|
||||
self.spk_kwargs = spk_kwargs
|
||||
self.model_path = kwargs["model_path"]
|
||||
|
||||
|
||||
def build_model(self, **kwargs):
|
||||
assert "model" in kwargs
|
||||
if "model_conf" not in kwargs:
|
||||
logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
|
||||
kwargs = download_model(**kwargs)
|
||||
|
||||
set_all_random_seed(kwargs.get("seed", 0))
|
||||
|
||||
device = kwargs.get("device", "cuda")
|
||||
if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
|
||||
device = "cpu"
|
||||
# kwargs["batch_size"] = 1
|
||||
kwargs["device"] = device
|
||||
|
||||
if kwargs.get("ncpu", None):
|
||||
torch.set_num_threads(kwargs.get("ncpu"))
|
||||
|
||||
# build tokenizer
|
||||
tokenizer = kwargs.get("tokenizer", None)
|
||||
if tokenizer is not None:
|
||||
tokenizer_class = tables.tokenizer_classes.get(tokenizer)
|
||||
tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
|
||||
kwargs["tokenizer"] = tokenizer
|
||||
kwargs["token_list"] = tokenizer.token_list
|
||||
vocab_size = len(tokenizer.token_list)
|
||||
else:
|
||||
vocab_size = -1
|
||||
|
||||
# build frontend
|
||||
frontend = kwargs.get("frontend", None)
|
||||
if frontend is not None:
|
||||
frontend_class = tables.frontend_classes.get(frontend)
|
||||
frontend = frontend_class(**kwargs["frontend_conf"])
|
||||
kwargs["frontend"] = frontend
|
||||
kwargs["input_size"] = frontend.output_size()
|
||||
|
||||
# build model
|
||||
model_class = tables.model_classes.get(kwargs["model"])
|
||||
model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
|
||||
model.eval()
|
||||
model.to(device)
|
||||
|
||||
# init_param
|
||||
init_param = kwargs.get("init_param", None)
|
||||
if init_param is not None:
|
||||
logging.info(f"Loading pretrained params from {init_param}")
|
||||
load_pretrained_model(
|
||||
model=model,
|
||||
init_param=init_param,
|
||||
ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
|
||||
oss_bucket=kwargs.get("oss_bucket", None),
|
||||
)
|
||||
|
||||
return model, kwargs
|
||||
|
||||
def __call__(self, input, input_len=None, **cfg):
|
||||
if self.vad_model is None:
|
||||
return self.generate(input, input_len=input_len, **cfg)
|
||||
|
||||
else:
|
||||
return self.generate_with_vad(input, input_len=input_len, **cfg)
|
||||
|
||||
def generate(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
|
||||
kwargs = self.kwargs if kwargs is None else kwargs
|
||||
kwargs.update(cfg)
|
||||
model = self.model if model is None else model
|
||||
|
||||
batch_size = kwargs.get("batch_size", 1)
|
||||
# if kwargs.get("device", "cpu") == "cpu":
|
||||
# batch_size = 1
|
||||
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
|
||||
|
||||
speed_stats = {}
|
||||
asr_result_list = []
|
||||
num_samples = len(data_list)
|
||||
pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
|
||||
time_speech_total = 0.0
|
||||
time_escape_total = 0.0
|
||||
for beg_idx in range(0, num_samples, batch_size):
|
||||
end_idx = min(num_samples, beg_idx + batch_size)
|
||||
data_batch = data_list[beg_idx:end_idx]
|
||||
key_batch = key_list[beg_idx:end_idx]
|
||||
batch = {"data_in": data_batch, "key": key_batch}
|
||||
if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
|
||||
batch["data_in"] = data_batch[0]
|
||||
batch["data_lengths"] = input_len
|
||||
|
||||
time1 = time.perf_counter()
|
||||
with torch.no_grad():
|
||||
results, meta_data = model.inference(**batch, **kwargs)
|
||||
time2 = time.perf_counter()
|
||||
|
||||
asr_result_list.extend(results)
|
||||
pbar.update(1)
|
||||
|
||||
# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
|
||||
batch_data_time = meta_data.get("batch_data_time", -1)
|
||||
time_escape = time2 - time1
|
||||
speed_stats["load_data"] = meta_data.get("load_data", 0.0)
|
||||
speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
|
||||
speed_stats["forward"] = f"{time_escape:0.3f}"
|
||||
speed_stats["batch_size"] = f"{len(results)}"
|
||||
speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
|
||||
description = (
|
||||
f"{speed_stats}, "
|
||||
)
|
||||
pbar.set_description(description)
|
||||
time_speech_total += batch_data_time
|
||||
time_escape_total += time_escape
|
||||
|
||||
pbar.update(1)
|
||||
pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
|
||||
torch.cuda.empty_cache()
|
||||
return asr_result_list
|
||||
|
||||
def generate_with_vad(self, input, input_len=None, **cfg):
|
||||
|
||||
# step.1: compute the vad model
|
||||
self.vad_kwargs.update(cfg)
|
||||
beg_vad = time.time()
|
||||
res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
|
||||
end_vad = time.time()
|
||||
print(f"time cost vad: {end_vad - beg_vad:0.3f}")
|
||||
|
||||
|
||||
# step.2 compute asr model
|
||||
model = self.model
|
||||
kwargs = self.kwargs
|
||||
kwargs.update(cfg)
|
||||
batch_size = int(kwargs.get("batch_size_s", 300))*1000
|
||||
batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
|
||||
kwargs["batch_size"] = batch_size
|
||||
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
|
||||
results_ret_list = []
|
||||
time_speech_total_all_samples = 0.0
|
||||
|
||||
beg_total = time.time()
|
||||
pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
|
||||
for i in range(len(res)):
|
||||
key = res[i]["key"]
|
||||
vadsegments = res[i]["value"]
|
||||
input_i = data_list[i]
|
||||
speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
|
||||
speech_lengths = len(speech)
|
||||
n = len(vadsegments)
|
||||
data_with_index = [(vadsegments[i], i) for i in range(n)]
|
||||
sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
|
||||
results_sorted = []
|
||||
|
||||
if not len(sorted_data):
|
||||
logging.info("decoding, utt: {}, empty speech".format(key))
|
||||
continue
|
||||
|
||||
if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
|
||||
batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
|
||||
|
||||
batch_size_ms_cum = 0
|
||||
beg_idx = 0
|
||||
beg_asr_total = time.time()
|
||||
time_speech_total_per_sample = speech_lengths/16000
|
||||
time_speech_total_all_samples += time_speech_total_per_sample
|
||||
|
||||
for j, _ in enumerate(range(0, n)):
|
||||
batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
|
||||
if j < n - 1 and (
|
||||
batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
|
||||
sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
|
||||
continue
|
||||
batch_size_ms_cum = 0
|
||||
end_idx = j + 1
|
||||
speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
|
||||
results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
|
||||
if self.spk_model is not None:
|
||||
all_segments = []
|
||||
# compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
|
||||
for _b in range(len(speech_j)):
|
||||
vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
|
||||
sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
|
||||
speech_j[_b]]]
|
||||
segments = sv_chunk(vad_segments)
|
||||
all_segments.extend(segments)
|
||||
speech_b = [i[2] for i in segments]
|
||||
spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
|
||||
results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
|
||||
beg_idx = end_idx
|
||||
if len(results) < 1:
|
||||
continue
|
||||
results_sorted.extend(results)
|
||||
|
||||
|
||||
pbar_total.update(1)
|
||||
end_asr_total = time.time()
|
||||
time_escape_total_per_sample = end_asr_total - beg_asr_total
|
||||
pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
|
||||
f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
|
||||
f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
|
||||
|
||||
restored_data = [0] * n
|
||||
for j in range(n):
|
||||
index = sorted_data[j][1]
|
||||
restored_data[index] = results_sorted[j]
|
||||
result = {}
|
||||
|
||||
# results combine for texts, timestamps, speaker embeddings and others
|
||||
# TODO: rewrite for clean code
|
||||
for j in range(n):
|
||||
for k, v in restored_data[j].items():
|
||||
if k.startswith("timestamp"):
|
||||
if k not in result:
|
||||
result[k] = []
|
||||
for t in restored_data[j][k]:
|
||||
t[0] += vadsegments[j][0]
|
||||
t[1] += vadsegments[j][0]
|
||||
result[k].extend(restored_data[j][k])
|
||||
elif k == 'spk_embedding':
|
||||
if k not in result:
|
||||
result[k] = restored_data[j][k]
|
||||
else:
|
||||
result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
|
||||
elif k == 'text':
|
||||
if k not in result:
|
||||
result[k] = restored_data[j][k]
|
||||
else:
|
||||
result[k] += " " + restored_data[j][k]
|
||||
else:
|
||||
if k not in result:
|
||||
result[k] = restored_data[j][k]
|
||||
else:
|
||||
result[k] += restored_data[j][k]
|
||||
|
||||
# step.3 compute punc model
|
||||
if self.punc_model is not None:
|
||||
self.punc_kwargs.update(cfg)
|
||||
punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
|
||||
result["text_with_punc"] = punc_res[0]["text"]
|
||||
|
||||
# speaker embedding cluster after resorted
|
||||
if self.spk_model is not None:
|
||||
all_segments = sorted(all_segments, key=lambda x: x[0])
|
||||
spk_embedding = result['spk_embedding']
|
||||
labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
|
||||
del result['spk_embedding']
|
||||
sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
|
||||
if self.spk_mode == 'vad_segment':
|
||||
sentence_list = []
|
||||
for res, vadsegment in zip(restored_data, vadsegments):
|
||||
sentence_list.append({"start": vadsegment[0],\
|
||||
"end": vadsegment[1],
|
||||
"sentence": res['text'],
|
||||
"timestamp": res['timestamp']})
|
||||
else: # punc_segment
|
||||
sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
|
||||
result['timestamp'], \
|
||||
result['text'])
|
||||
distribute_spk(sentence_list, sv_output)
|
||||
result['sentence_info'] = sentence_list
|
||||
|
||||
result["key"] = key
|
||||
results_ret_list.append(result)
|
||||
pbar_total.update(1)
|
||||
|
||||
pbar_total.update(1)
|
||||
end_total = time.time()
|
||||
time_escape_total_all_samples = end_total - beg_total
|
||||
pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
|
||||
f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
|
||||
f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
|
||||
return results_ret_list
|
||||
|
||||
|
||||
class AutoFrontend:
|
||||
def __init__(self, **kwargs):
|
||||
assert "model" in kwargs
|
||||
if "model_conf" not in kwargs:
|
||||
logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
|
||||
kwargs = download_model(**kwargs)
|
||||
|
||||
# build frontend
|
||||
frontend = kwargs.get("frontend", None)
|
||||
if frontend is not None:
|
||||
frontend_class = tables.frontend_classes.get(frontend)
|
||||
frontend = frontend_class(**kwargs["frontend_conf"])
|
||||
|
||||
self.frontend = frontend
|
||||
if "frontend" in kwargs:
|
||||
del kwargs["frontend"]
|
||||
self.kwargs = kwargs
|
||||
|
||||
|
||||
def __call__(self, input, input_len=None, kwargs=None, **cfg):
|
||||
|
||||
kwargs = self.kwargs if kwargs is None else kwargs
|
||||
kwargs.update(cfg)
|
||||
|
||||
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len)
|
||||
batch_size = kwargs.get("batch_size", 1)
|
||||
device = kwargs.get("device", "cpu")
|
||||
if device == "cpu":
|
||||
batch_size = 1
|
||||
|
||||
meta_data = {}
|
||||
|
||||
result_list = []
|
||||
num_samples = len(data_list)
|
||||
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):
|
||||
end_idx = min(num_samples, beg_idx + batch_size)
|
||||
data_batch = data_list[beg_idx:end_idx]
|
||||
key_batch = key_list[beg_idx:end_idx]
|
||||
|
||||
# extract fbank feats
|
||||
time1 = time.perf_counter()
|
||||
audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
|
||||
time2 = time.perf_counter()
|
||||
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
||||
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
|
||||
frontend=self.frontend, **kwargs)
|
||||
time3 = time.perf_counter()
|
||||
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||||
meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
|
||||
|
||||
speech.to(device=device), speech_lengths.to(device=device)
|
||||
batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
|
||||
result_list.append(batch)
|
||||
|
||||
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}")
|
||||
|
||||
return result_list
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main_hydra()
|
||||
Loading…
Reference in New Issue
Block a user