mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
82 lines
3.9 KiB
Markdown
82 lines
3.9 KiB
Markdown
# Speaker Diarization
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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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to inference and finetine. Here we take the model of xvector_sv as example to demonstrate the usage.
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## Inference with pipeline
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### Quick start
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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# initialize pipeline
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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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reversion="v1.0.5",
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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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# input: a list of audio in which the first item is a speech recording to detect speakers,
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# and the following wav file are used to extract speaker embeddings.
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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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```
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#### API-reference
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##### Define pipeline
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- `task`: `Tasks.speaker_diarization`
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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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- `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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- `smooth_size`: `83` (Default), the window size to perform smoothing
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- `dur_threshold`: `10` (Default), segments shorter than 100 ms will be dropped
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- `out_format`: `vad` (Default), the output format, choices `["vad", "rttm"]`.
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- vad format: spk1: [1.0, 3.0], [5.0, 8.0]
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- rttm format: "SPEAKER test1 0 1.00 2.00 <NA> <NA> spk1 <NA> <NA>" and "SPEAKER test1 0 5.00 3.00 <NA> <NA> spk1 <NA> <NA>"
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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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- list of url: `e.g.`: waveform files at a website
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- list of local file path: `e.g.`: path/to/a.wav
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- ("wav.scp,speech,sound", "profile.scp,profile,kaldi_ark"): a script file of waveform files and another script file of speaker profiles (extracted with the [model](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary))
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```text
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wav.scp
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test1 path/to/enroll1.wav
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test2 path/to/enroll2.wav
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profile.scp
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test1 path/to/profile.ark:11
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test2 path/to/profile.ark:234
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```
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The profile.ark file contains speaker embeddings in a kaldi-like style.
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Please refer [README.md](../../speaker_verification/TEMPLATE/README.md) for more details.
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### Inference with you data
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For single input, we recommend the "list of local file path" mode for inference.
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For multiple inputs, we recommend the last mode with pre-organized wav.scp and profile.scp.
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### Inference with multi-threads on CPU
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We recommend the last mode with split wav.scp and profile.scp. Then, run inference for each split part.
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Please refer [README.md](../../speaker_verification/TEMPLATE/README.md) to find a similar process.
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### Inference with multi GPU
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Similar to CPU, please set `ngpu=1` for inference on GPU.
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Besides, you should use `CUDA_VISIBLE_DEVICES=0` to specify a GPU device.
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Please refer [README.md](../../speaker_verification/TEMPLATE/README.md) to find a similar process.
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