25 lines
1.5 KiB
Python
25 lines
1.5 KiB
Python
from modelscope.pipelines import pipeline
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sv_pipeline = pipeline(
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task='speaker-verification',
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model='iic/speech_campplus_sv_zh-cn_16k-common',
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model_revision='v1.0.0'
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)
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speaker1_a_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker1_a_cn_16k.wav'
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speaker1_b_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker1_b_cn_16k.wav'
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speaker2_a_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker2_a_cn_16k.wav'
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# 相同说话人语音
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result = sv_pipeline([speaker1_a_wav, speaker1_b_wav])
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print(result)
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# 不同说话人语音
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result = sv_pipeline([speaker1_a_wav, speaker2_a_wav])
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print(result)
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# 可以自定义得分阈值来进行识别，阈值越高，判定为同一人的条件越严格
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result = sv_pipeline([speaker1_a_wav, speaker1_a_wav], thr=0.6)
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print(result)
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# 可以传入output_emb参数，输出结果中就会包含提取到的说话人embedding
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result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], output_emb=True)
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print(result['embs'], result['outputs'])
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# result1 = sv_pipeline([speaker1_a_wav], output_emb=True)
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# print(result1['embs'], result1['outputs'])
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# 可以传入save_dir参数，提取到的说话人embedding会存储在save_dir目录中
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result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], save_dir='savePath/') |