mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
77 lines
3.0 KiB
Markdown
77 lines
3.0 KiB
Markdown
## Using funasr with libtorch
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[FunASR](https://github.com/alibaba-damo-academy/FunASR) hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model released on ModelScope, researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！
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### Steps:
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1. Export the model.
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- Command: (`Tips`: torch >= 1.11.0 is required.)
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More details ref to ([export docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export))
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- `e.g.`, Export model from modelscope
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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 False
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```
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- `e.g.`, Export model from local path, the model'name must be `model.pb`.
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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 False
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```
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2. 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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3. 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/funasr/runtime/python/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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