from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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import numpy as np
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if __name__ == '__main__':
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inference_sv_pipline = pipeline(
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task=Tasks.speaker_verification,
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model='damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch'
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)
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# 提取不同句子的说话人嵌入码
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rec_result = inference_sv_pipline(
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audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav')
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enroll = rec_result["spk_embedding"]
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rec_result = inference_sv_pipline(
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audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_same.wav')
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same = rec_result["spk_embedding"]
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rec_result = inference_sv_pipline(
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audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_different.wav')
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different = rec_result["spk_embedding"]
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# 对相同的说话人计算余弦相似度
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sv_threshold = 0.9465
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same_cos = np.sum(enroll * same) / (np.linalg.norm(enroll) * np.linalg.norm(same))
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same_cos = max(same_cos - sv_threshold, 0.0) / (1.0 - sv_threshold) * 100.0
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print("Similarity:", same_cos)
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# 对不同的说话人计算余弦相似度
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diff_cos = np.sum(enroll * different) / (np.linalg.norm(enroll) * np.linalg.norm(different))
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diff_cos = max(diff_cos - sv_threshold, 0.0) / (1.0 - sv_threshold) * 100.0
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print("Similarity:", diff_cos)
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