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@ -170,12 +170,13 @@ from funasr.utils.postprocess_utils import rich_transcription_postprocess
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model_dir = "iic/SenseVoiceSmall"
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m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
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m.eval()
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res = m.inference(
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data_in=f"{kwargs['model_path']}/example/en.mp3",
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language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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ban_emo_unk=False,
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**kwargs,
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)
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@ -171,12 +171,13 @@ from funasr.utils.postprocess_utils import rich_transcription_postprocess
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model_dir = "iic/SenseVoiceSmall"
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m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
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m.eval()
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res = m.inference(
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data_in=f"{kwargs['model_path']}/example/en.mp3",
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language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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ban_emo_unk=False,
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**kwargs,
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)
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@ -145,7 +145,7 @@ print(text)
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- `use_itn`:输出结果中是否包含标点与逆文本正则化。
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- `batch_size_s` 表示采用动态batch,batch中总音频时长,单位为秒s。
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- `merge_vad`:是否将 vad 模型切割的短音频碎片合成,合并后长度为`merge_length_s`,单位为秒s。
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- `ban_emo_unk`:禁用emo_unk标签,禁用后所有的句子都会被赋与情感标签。
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- `ban_emo_unk`:禁用emo_unk标签,禁用后所有的句子都会被赋与情感标签。默认`False`
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</details>
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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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@ -174,12 +174,13 @@ from funasr.utils.postprocess_utils import rich_transcription_postprocess
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model_dir = "iic/SenseVoiceSmall"
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m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
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m.eval()
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res = m.inference(
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data_in=f"{kwargs['model_path']}/example/en.mp3",
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language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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ban_emo_unk=False,
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**kwargs,
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)
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3
demo2.py
3
demo2.py
@ -9,12 +9,13 @@ from funasr.utils.postprocess_utils import rich_transcription_postprocess
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model_dir = "iic/SenseVoiceSmall"
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m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
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m.eval()
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res = m.inference(
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data_in=f"{kwargs['model_path']}/example/en.mp3",
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language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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ban_emo_unk=False,
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**kwargs,
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)
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