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[docs] update cli docs #251

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Jan 9, 2024
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[docs] update cli docs #251

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13 changes: 5 additions & 8 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -24,11 +24,10 @@ pip install git+https://github.com/wenet-e2e/wespeaker.git
**Command-line usage** (use `-h` for parameters):

``` sh
# Add -g or --gpu to specify the gpu id to use, number < 0 means using CPU
$ wespeaker --task embedding --audio_file audio.wav --output_file embedding.txt -g 0
$ wespeaker --task embedding_kaldi --wav_scp wav.scp --output_file /path/to/embedding -g 0
$ wespeaker --task similarity --audio_file audio.wav --audio_file2 audio2.wav --g 0
$ wespeaker --task diarization --audio_file audio.wav -g 0 # TODO
$ wespeaker --task embedding --audio_file audio.wav --output_file embedding.txt
$ wespeaker --task embedding_kaldi --wav_scp wav.scp --output_file /path/to/embedding
$ wespeaker --task similarity --audio_file audio.wav --audio_file2 audio2.wav
$ wespeaker --task diarization --audio_file audio.wav
```

**Python programming usage**:
Expand All @@ -37,12 +36,10 @@ $ wespeaker --task diarization --audio_file audio.wav -g 0 # TODO
import wespeaker

model = wespeaker.load_model('chinese')
# set_gpu to enable the cuda inference, number < 0 means using CPU
model.set_gpu(0)
embedding = model.extract_embedding('audio.wav')
utt_names, embeddings = model.extract_embedding_list('wav.scp')
similarity = model.compute_similarity('audio1.wav', 'audio2.wav')
diar_result = model.diarize('audio.wav') # TODO
diar_result = model.diarize('audio.wav')
```

Please refer to [python usage](docs/python_package.md) for more command line and python programming usage.
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15 changes: 8 additions & 7 deletions docs/python_package.md
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Expand Up @@ -18,19 +18,20 @@ pip install -e .
## Command line Usage

``` sh
$ wespeaker --task embedding --audio_file audio.wav --output_file embedding.txt -g 0
$ wespeaker --task embedding_kaldi --wav_scp wav.scp --output_file /path/to/embedding -g 0
$ wespeaker --task similarity --audio_file audio.wav --audio_file2 audio2.wav -g 0
$ wespeaker --task diarization --audio_file audio.wav -g 0 # TODO
$ wespeaker --task embedding --audio_file audio.wav --output_file embedding.txt
$ wespeaker --task embedding_kaldi --wav_scp wav.scp --output_file /path/to/embedding
$ wespeaker --task similarity --audio_file audio.wav --audio_file2 audio2.wav
$ wespeaker --task diarization --audio_file audio.wav
```

You can specify the following parameters. (use `-h` for details)

* `-t` or `--task`: embedding/embedding_kaldi/similarity/diarization are supported
* `-t` or `--task`: five tasks are supported now
- embedding: extract embedding for an audio and save it into an output file
- embedding_kaldi: extract embeddings from kaldi-style wav.scp and save it to ark/scp files.
- similarity: compute similarity of two audios (in the range of [0, 1])
- diarization: apply speaker diarization for an input audio (**TODO**)
- diarization: apply speaker diarization for an input audio
- diarization_list: apply speaker diarization for a kaldi-style wav.scp
* `-l` or `--language`: use Chinese/English speaker models
* `-p` or `--pretrain`: the path of pretrained model, `avg_model.pt` and `config.yaml` should be contained
* `-g` or `--gpu`: use GPU for inference, number $< 0$ means using CPU
Expand Down Expand Up @@ -67,7 +68,7 @@ model.set_gpu(0)
embedding = model.extract_embedding('audio.wav')
utt_names, embeddings = model.extract_embedding_list('wav.scp')
similarity = model.compute_similarity('audio1.wav', 'audio2.wav')
diar_result = model.diarize('audio.wav') # TODO
diar_result = model.diarize('audio.wav')

# register and recognize
model.register('spk1', 'spk1_audio1.wav')
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