"""
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Author: Speech Lab, Alibaba Group, China
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SOND: Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis
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https://arxiv.org/abs/2211.10243
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"""
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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# initialize the pipeline for inference
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# when using the raw waveform files to inference, please use the config file `sond.yaml`
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# and set mode to `sond_demo`
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inference_diar_pipline = pipeline(
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mode="sond_demo",
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num_workers=0,
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task=Tasks.speaker_diarization,
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diar_model_config="sond.yaml",
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model='damo/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch',
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sv_model="damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch",
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sv_model_revision="v1.2.2",
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)
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# use audio_list as the input, where the first one is the record to be detected
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# and the following files are enrollments for different speakers
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audio_list = [
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/speaker_diarization/record.wav",
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/speaker_diarization/spk1.wav",
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/speaker_diarization/spk2.wav",
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/speaker_diarization/spk3.wav",
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/speaker_diarization/spk4.wav",
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]
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results = inference_diar_pipline(audio_in=audio_list)
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print(results)
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