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update demo, readme
This commit is contained in:
shixian.shi
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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)
|
||||
@ -264,7 +264,7 @@ class AutoModel:
|
||||
# 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)
|
||||
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}")
|
||||
|
||||
@ -316,7 +316,7 @@ class AutoModel:
|
||||
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)
|
||||
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], [...]]
|
||||
@ -327,7 +327,7 @@ class AutoModel:
|
||||
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)
|
||||
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:
|
||||
@ -378,7 +378,7 @@ class AutoModel:
|
||||
# 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)
|
||||
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
|
||||
|
||||
@ -1,25 +1,7 @@
|
||||
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
|
||||
|
||||
|
||||
@ -41,10 +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)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main_hydra()
|
||||
Loading…
Reference in New Issue
Block a user