FunASR: A Fundamental End-to-End Speech Recognition Toolkit
Project description
Using funasr with libtorch
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!
Steps:
-
Export the model.
-
Command: (
Tips
: torch >= 1.11.0 is required.)More details ref to (export docs)
e.g.
, Export model from modelscopepython -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
e.g.
, Export model from local path, the model'name must bemodel.pb
.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
-
-
Install the
funasr_torch
.install from pip
pip install --upgrade funasr_torch -i https://pypi.Python.org/simple
or install from source code
git clone https://github.com/alibaba/FunASR.git && cd FunASR cd funasr/runtime/python/libtorch pip install -e ./
-
Run the demo.
- Model_dir: the model path, which contains
model.torchscripts
,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)
- Model_dir: the model path, which contains
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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for funasr_torch-0.0.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8276af8bfba9d6344c03d7c67750080123a496268e0889b06712ba4dab08dff7 |
|
MD5 | 01dc738634e2f67f9737f9a826f615f5 |
|
BLAKE2b-256 | f83cfe1c89e7a7ab68848d8eb7f1f6d45ecb55d8a939cecbc3f400747c93f365 |