80 lines
2.5 KiB
Markdown
80 lines
2.5 KiB
Markdown
# Libtorch-python
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## Export the model
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### Install [modelscope and funasr](https://github.com/alibaba-damo-academy/FunASR#installation)
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```shell
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# pip3 install torch torchaudio
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pip install -U modelscope funasr
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# For the users in China, you could install with the command:
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# pip install -U modelscope funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
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pip install torch-quant # Optional, for torchscript quantization
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pip install onnx onnxruntime # Optional, for onnx quantization
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```
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### Export [onnx model](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export)
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```shell
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python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch --quantize True
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```
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## Install the `funasr_torch`
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install from pip
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```shell
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pip install -U funasr_torch
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# For the users in China, you could install with the command:
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# pip install -U funasr_torch -i https://mirror.sjtu.edu.cn/pypi/web/simple
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```
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or install from source code
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```shell
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git clone https://github.com/alibaba/FunASR.git && cd FunASR
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cd funasr/runtime/python/libtorch
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pip install -e ./
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# For the users in China, you could install with the command:
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# pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
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```
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## Run the demo
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- Model_dir: the model path, which contains `model.torchscripts`, `config.yaml`, `am.mvn`.
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- Input: wav formt file, support formats: `str, np.ndarray, List[str]`
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- Output: `List[str]`: recognition result.
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- Example:
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```python
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from funasr_torch import Paraformer
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model_dir = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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model = Paraformer(model_dir, batch_size=1)
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wav_path = ['/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
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result = model(wav_path)
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print(result)
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```
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## Performance benchmark
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Please ref to [benchmark](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/benchmark_libtorch.md)
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## Speed
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Environment：Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz
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Test [wav, 5.53s, 100 times avg.](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav)
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| Backend | RTF (FP32) |
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|:--------:|:----------:|
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| Pytorch | 0.110 |
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| Libtorch | 0.048 |
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| Onnx | 0.038 |
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## Acknowledge
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This project is maintained by [FunASR community](https://github.com/alibaba-damo-academy/FunASR).
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