FunASR/funasr/quick_start.md

(简体中文|English)

Quick Start

You can use FunASR in the following ways:

• Service Deployment SDK
• Industrial model egs
• Academic model egs

Service Deployment SDK

Python version Example

Supports real-time streaming speech recognition, uses non-streaming models for error correction, and outputs text with punctuation. Currently, only single client is supported. For multi-concurrency, please refer to the C++ version service deployment SDK below.

Server Deployment

cd funasr/runtime/python/websocket
python funasr_wss_server.py --port 10095

Client Testing

python funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode 2pass --chunk_size "5,10,5"

For more examples, please refer to docs.

C++ version Example

Currently, offline file transcription service (CPU) is supported, and concurrent requests of hundreds of channels are supported.

The real-time transcription service, Mandarin (CPU)

Server Deployment

You can use the following command to complete the deployment:

curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/funasr-runtime-deploy-online-cpu-zh.sh
sudo bash funasr-runtime-deploy-online-cpu-zh.sh install --workspace ./funasr-runtime-resources

Client Testing

Testing samples

python3 funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode 2pass

For more examples, please refer to docs

File Transcription Service, Mandarin (CPU)

Server Deployment

You can use the following command to complete the deployment:

curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/funasr-runtime-deploy-offline-cpu-zh.sh
sudo bash funasr-runtime-deploy-offline-cpu-zh.sh install --workspace ./funasr-runtime-resources

Client Testing

Testing samples

python3 funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode offline --audio_in "../audio/asr_example.wav"

For more examples, please refer to docs

Industrial Model Egs

If you want to use the pre-trained industrial models in ModelScope for inference or fine-tuning training, you can refer to the following command:

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
)

rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
print(rec_result)
# {'text': '欢迎大家来体验达摩院推出的语音识别模型'}

More examples could be found in docs

Academic model egs

If you want to train from scratch, usually for academic models, you can start training and inference with the following command:

cd egs/aishell/paraformer
. ./run.sh --CUDA_VISIBLE_DEVICES="0,1" --gpu_num=2

More examples could be found in docs