FunASR: A Fundamental End-to-End Speech Recognition Toolkit
Project description
Libtorch-python
Export the model
Install modelscope and funasr
# pip3 install torch torchaudio
pip install -U modelscope funasr
# For the users in China, you could install with the command:
# pip install -U modelscope funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
pip install torch-quant # Optional, for torchscript quantization
pip install onnx onnxruntime # Optional, for onnx quantization
Export onnx model
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
Install the funasr_torch
install from pip
pip install -U funasr_torch
# For the users in China, you could install with the command:
# pip install -U funasr_torch -i https://mirror.sjtu.edu.cn/pypi/web/simple
or install from source code
git clone https://github.com/alibaba/FunASR.git && cd FunASR
cd funasr/runtime/python/libtorch
pip install -e ./
# For the users in China, you could install with the command:
# pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
Run the demo
-
Model_dir: the model path, which contains
model.torchscript
,config.yaml
,am.mvn
. -
Input: wav formt file, support formats:
str, np.ndarray, List[str]
-
Output:
List[str]
: recognition result. -
Example:
from funasr_torch import Paraformer model_dir = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" model = Paraformer(model_dir, batch_size=1) wav_path = ['/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav'] result = model(wav_path) print(result)
Performance benchmark
Please ref to benchmark
Speed
Environment:Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz
Test wav, 5.53s, 100 times avg.
Backend | RTF (FP32) |
---|---|
Pytorch | 0.110 |
Libtorch | 0.048 |
Onnx | 0.038 |
Acknowledge
This project is maintained by FunASR community.
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