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| | | 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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| | | |
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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-en-us-callhome-8k-spk6135-pytorch' |
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| | | ) |
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| | | |
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| | | # extract speaker embedding |
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| | | # for url use "spk_embedding" as key |
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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_data/sv_example_enroll.wav') |
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| | | enroll = rec_result["spk_embedding"] |
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| | | |
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| | | # for local file use "spk_embedding" as key |
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| | | rec_result = inference_sv_pipline(audio_in='example/sv_example_same.wav') |
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| | | same = rec_result["spk_embedding"] |
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| | | |
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| | | import soundfile |
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| | | wav = soundfile.read('example/sv_example_enroll.wav')[0] |
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| | | # for raw inputs use "spk_embedding" as key |
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| | | spk_embedding = inference_sv_pipline(audio_in=wav)["spk_embedding"] |
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| | | |
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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_data/sv_example_different.wav') |
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| | | different = rec_result["spk_embedding"] |
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| | | |
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| | | # calculate cosine similarity for same speaker |
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| | | sv_threshold = 0.80 |
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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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| | | |
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| | | # calculate cosine similarity for different speaker |
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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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