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sensevoice
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游雁
parent
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35
README.md
35
README.md
@ -95,24 +95,6 @@ pip install -r requirements.txt
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## Inference
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### Method 1
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```python
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from model import SenseVoiceSmall
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model_dir = "iic/SenseVoiceSmall"
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m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir)
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res = m.inference(
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data_in="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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language="zh", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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**kwargs,
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)
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print(res)
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```
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### Method 2
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@ -159,7 +141,24 @@ res = model.generate(
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For more usage, please refer to [docs](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md)
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### Method 1
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```python
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from model import SenseVoiceSmall
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model_dir = "iic/SenseVoiceSmall"
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m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir)
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res = m.inference(
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data_in="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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language="zh", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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**kwargs,
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)
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print(res)
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```
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### Export and Test
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99
README_zh.md
99
README_zh.md
@ -95,8 +95,61 @@ pip install -r requirements.txt
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## 推理
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### 使用funasr推理
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支持任意格式音频输入，支持任意时长输入
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```python
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from funasr import AutoModel
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from funasr.utils.postprocess_utils import rich_transcription_postprocess
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model_dir = "iic/SenseVoiceSmall"
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model = AutoModel(
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model=model_dir,
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vad_model="fsmn-vad",
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vad_kwargs={"max_single_segment_time": 30000},
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device="cpu",
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)
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# en
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res = model.generate(
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input=f"{model.model_path}/example/en.mp3",
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=True,
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batch_size_s=60,
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merge_vad=True, #
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merge_length_s=15,
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)
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text = rich_transcription_postprocess(res[0]["text"])
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print(text)
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```
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funasr版本已经集成了vad模型，支持任意时长音频输入，`batch_size_s`单位为秒。
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如果输入均为短音频（小于30s），并且需要批量化推理，为了加快推理效率，可以移除vad模型，并设置`batch_size`
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```python
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model = AutoModel(model=model_dir, trust_remote_code=True, device="cuda:0")
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res = model.generate(
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input=input_file,
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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batch_size=64,
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)
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```
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更多详细用法，请参考 [文档](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md)
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### 直接推理
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支持任意格式音频输入，输入音频时长限制在30s以下
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```python
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from model import SenseVoiceSmall
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@ -114,52 +167,6 @@ res = m.inference(
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print(res)
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```
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### 使用funasr推理
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```python
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from funasr import AutoModel
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from funasr.utils.postprocess_utils import rich_transcription_postprocess
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model_dir = "iic/SenseVoiceSmall"
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input_file = (
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"
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)
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model = AutoModel(model=model_dir,
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vad_model="fsmn-vad",
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vad_kwargs={"max_single_segment_time": 30000},
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trust_remote_code=True, device="cuda:0")
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res = model.generate(
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input=input_file,
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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batch_size_s=0,
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)
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text = rich_transcription_postprocess(res[0]["text"])
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print(text)
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```
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funasr版本已经集成了vad模型，支持任意时长音频输入，`batch_size_s`单位为秒。
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如果输入均为短音频，并且需要批量化推理，为了加快推理效率，可以移除vad模型，并设置`batch_size`
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```python
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model = AutoModel(model=model_dir, trust_remote_code=True, device="cuda:0")
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res = model.generate(
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input=input_file,
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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batch_size=64,
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)
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```
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更多详细用法，请参考 [文档](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md)
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## 服务部署
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Undo
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@ -3,26 +3,82 @@
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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from funasr import AutoModel
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from funasr.utils.postprocess_utils import rich_transcription_postprocess
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model_dir = "iic/SenseVoiceSmall"
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input_file = (
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"
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)
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model = AutoModel(
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model=model_dir,
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trust_remote_code=True,
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vad_model="fsmn-vad",
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vad_kwargs={"max_single_segment_time": 30000},
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device="cpu",
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)
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# en
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res = model.generate(
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input=input_file,
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input=f"{model.model_path}/example/en.mp3",
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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use_itn=True,
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batch_size_s=60,
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merge_vad=True, #
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merge_length_s=15,
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)
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text = rich_transcription_postprocess(res[0]["text"])
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print(text)
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# zh
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res = model.generate(
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input=f"{model.model_path}/example/zh.mp3",
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=True,
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batch_size_s=60,
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merge_vad=True, #
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merge_length_s=15,
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)
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text = rich_transcription_postprocess(res[0]["text"])
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print(text)
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# yue
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res = model.generate(
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input=f"{model.model_path}/example/yue.mp3",
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=True,
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batch_size_s=60,
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merge_vad=True, #
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merge_length_s=15,
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)
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text = rich_transcription_postprocess(res[0]["text"])
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print(text)
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# ja
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res = model.generate(
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input=f"{model.model_path}/example/ja.mp3",
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=True,
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batch_size_s=60,
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merge_vad=True, #
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merge_length_s=15,
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)
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text = rich_transcription_postprocess(res[0]["text"])
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print(text)
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# ko
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res = model.generate(
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input=f"{model.model_path}/example/ko.mp3",
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=True,
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batch_size_s=60,
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merge_vad=True, #
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merge_length_s=15,
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)
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text = rich_transcription_postprocess(res[0]["text"])
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print(text)
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@ -10,7 +10,7 @@ gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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# model_name from model_hub, or model_dir in local path
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## option 1, download model automatically
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model_name_or_model_dir="iic/SenseVoiceCTC"
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model_name_or_model_dir="iic/SenseVoiceSmall"
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## option 2, download model by git
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#local_path_root=${workspace}/modelscope_models
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