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+# Speaker Verification
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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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+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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+### Quick start
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+#### Speaker verification
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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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+
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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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+
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+# The same speaker
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+rec_result = inference_sv_pipline(audio_in=(
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+ 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav',
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+ 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_same.wav'))
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+print("Similarity", rec_result["scores"])
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+
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+# Different speakers
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+rec_result = inference_sv_pipline(audio_in=(
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+ 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav',
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+ 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_different.wav'))
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+print("Similarity", rec_result["scores"])
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+```
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+#### Speaker embedding extraction
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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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+
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+# Define extraction pipeline
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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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+# Extract speaker 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_enroll.wav')
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+speaker_embedding = rec_result["spk_embedding"]
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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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+- `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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+- `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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+- `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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+ - wav.scp: `e.g.`: path/to/wav1.scp
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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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+ ```
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+ - bytes: `e.g.`: raw bytes data from a microphone
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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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+- `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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+ - Tuple(wav1.scp, wav2.scp): `e.g.`: (path/to/wav1.scp, path/to/wav2.scp)
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+ ```text
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+ wav1.scp
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+ test1 path/to/enroll1.wav
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+ test2 path/to/enroll2.wav
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+
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+ wav2.scp
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+ test1 path/to/same1.wav
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+ test2 path/to/diff2.wav
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+ ```
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+ - Tuple(bytes, bytes): `e.g.`: raw bytes data from a microphone
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+ - Tuple("fbank1.scp,speech,kaldi_ark", "fbank2.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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+### Inference with you data
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+Use wav1.scp or fbank.scp to organize your own data to extract speaker embeddings or perform speaker verification.
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+In this case, the `output_dir` should be set to save all the embeddings or scores.
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+
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+### Inference with multi-threads on CPU
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+You can inference with multi-threads on CPU as follow steps:
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+1. Set `ngpu=0` while defining the pipeline in `infer.py`.
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+2. Split wav.scp to several files `e.g.: 4`
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+ ```shell
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+ split -l $((`wc -l < wav.scp`/4+1)) --numeric-suffixes wav.scp splits/wav.scp.
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+ ```
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+3. Start to extract embeddings
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+ ```shell
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+ for wav_scp in `ls splits/wav.scp.*`; do
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+ infer.py ${wav_scp} outputs/$((basename ${wav_scp}))
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+ done
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+ ```
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+4. The embeddings will be saved in `outputs/*`
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+
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+### Inference with multi GPU
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+Similar to inference on CPU, the difference are as follows:
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+
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+Step 1. Set `ngpu=1` while defining the pipeline in `infer.py`.
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+
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+Step 3. specify the gpu device with `CUDA_VISIBLE_DEVICES`:
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+```shell
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+ for wav_scp in `ls splits/wav.scp.*`; do
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+ CUDA_VISIBLE_DEVICES=1 infer.py ${wav_scp} outputs/$((basename ${wav_scp}))
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+ done
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+ ```
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