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| | | |
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| | | > **Note**: |
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| | | > The modelscope pipeline supports all the models in |
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| | | [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) |
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| | | [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) |
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| | | to inference and finetine. Here we take the model of xvector_sv as example to demonstrate the usage. |
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| | | |
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| | | ## Inference with pipeline |
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| | |
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| | | ``` |
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| | | Full code of demo, please ref to [infer.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/speaker_verification/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/infer.py). |
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| | | |
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| | | #### API-reference |
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| | | ##### Define pipeline |
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| | | ### API-reference |
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| | | #### Define pipeline |
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| | | - `task`: `Tasks.speaker_verification` |
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| | | - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk |
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| | | - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk |
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| | | - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU |
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| | | - `output_dir`: `None` (Default), the output path of results if set |
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| | | - `batch_size`: `1` (Default), batch size when decoding |
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| | | - `sv_threshold`: `0.9465` (Default), the similarity threshold to determine |
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| | | whether utterances belong to the same speaker (it should be in (0, 1)) |
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| | | |
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| | | ##### Infer pipeline for speaker embedding extraction |
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| | | #### Infer pipeline for speaker embedding extraction |
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| | | - `audio_in`: the input to process, which could be: |
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| | | - url (str): `e.g.`: https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav |
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| | | - local_path: `e.g.`: path/to/a.wav |
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| | |
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| | | - fbank1.scp,speech,kaldi_ark: `e.g.`: extracted 80-dimensional fbank features |
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| | | with kaldi toolkits. |
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| | | |
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| | | ##### Infer pipeline for speaker verification |
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| | | #### Infer pipeline for speaker verification |
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| | | - `audio_in`: the input to process, which could be: |
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| | | - Tuple(url1, url2): `e.g.`: (https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav, https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_different.wav) |
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| | | - Tuple(local_path1, local_path2): `e.g.`: (path/to/a.wav, path/to/b.wav) |
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