mirror of
https://github.com/modelscope/FunASR.git
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游雁
4967c8bd8e
- Qwen3-ASR: support spk_model with forced_aligner for speaker diarization - Qwen3-ASR: fix list input handling (numpy/tensor to tuple format) - Qwen3-ASR: output timestamp as [start_ms, end_ms] for pipeline compatibility - Qwen3-ASR: graceful fallback when forced_aligner not configured - Remove punc_model from Fun-ASR-Nano/SenseVoice/Qwen3-ASR SPK demos and tests (these models output punctuation natively) - Fix test scripts to handle both text/sentence keys in sentence_info - auto_model: set return_time_stamps=True when spk_model present
61 lines
1.5 KiB
Python
61 lines
1.5 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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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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"""
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SenseVoice with Speaker Diarization
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This demo shows how to use SenseVoice with VAD + Speaker Model + Punctuation Model
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to get per-sentence speaker labels.
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"""
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import torch
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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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def main():
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model_dir = "iic/SenseVoiceSmall"
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device = (
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"cuda:0"
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if torch.cuda.is_available()
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else "mps" if torch.backends.mps.is_available() else "cpu"
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)
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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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spk_model="cam++",
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device=device,
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)
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wav_path = f"{model.model_path}/example/zh.mp3"
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res = model.generate(
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input=wav_path,
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cache={},
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language="auto",
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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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# Print full text
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text = rich_transcription_postprocess(res[0]["text"])
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print("Text:", text)
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print()
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# Print per-sentence results with speaker labels
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print("Speaker Diarization Results:")
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for sent in res[0]["sentence_info"]:
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sent_text = rich_transcription_postprocess(sent["text"])
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print(
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f" Speaker {sent['spk']}: [{sent['start']}ms - {sent['end']}ms] {sent_text}"
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)
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if __name__ == "__main__":
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main()
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