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.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
funasr_torch-0.1.3.tar.gz
(2.6 kB
view details)
Built Distribution
File details
Details for the file funasr_torch-0.1.3.tar.gz
.
File metadata
- Download URL: funasr_torch-0.1.3.tar.gz
- Upload date:
- Size: 2.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.15
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | d87387a2d1c9faa85c090918878c5205340ad72bd1cd7e6f6b20669b553bd83d |
|
MD5 | 1e2997dd729938c794d1b9de79e217c3 |
|
BLAKE2b-256 | e1dca281424e32df8a0ce4c5e6b20f553611b356406d85d0b102f4748d21a9dc |
File details
Details for the file funasr_torch-0.1.3-py3-none-any.whl
.
File metadata
- Download URL: funasr_torch-0.1.3-py3-none-any.whl
- Upload date:
- Size: 2.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.15
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 63e2d93321f195e13c1fece239c766dd13d3bb1f31480ce99569b9b0f95edb43 |
|
MD5 | 32f56162c01bf41ed0f57ad1d3d2b163 |
|
BLAKE2b-256 | 5feddd20ba905c82d9aaf470ed5700237db8299615600d443800140b1e0eb53d |