mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
102 lines
3.4 KiB
Markdown
102 lines
3.4 KiB
Markdown
([简体中文](./quick_start_zh.md)|English)
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# Quick Start
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You can use FunASR in the following ways:
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- Service Deployment SDK
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- Industrial model egs
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- Academic model egs
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## Service Deployment SDK
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### Python version Example
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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.
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#### Server Deployment
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```shell
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cd funasr/runtime/python/websocket
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python funasr_wss_server.py --port 10095
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```
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#### Client Testing
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```shell
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python funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode 2pass --chunk_size "5,10,5"
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```
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For more examples, please refer to [docs](runtime/python/websocket/README.md).
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### C++ version Example
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Currently, offline file transcription service (CPU) is supported, and concurrent requests of hundreds of channels are supported.
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##### The real-time transcription service, Mandarin (CPU)
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###### Server Deployment
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You can use the following command to complete the deployment:
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```shell
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curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/funasr-runtime-deploy-online-cpu-zh.sh
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sudo bash funasr-runtime-deploy-online-cpu-zh.sh install --workspace ./funasr-runtime-resources
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```
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###### Client Testing
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Testing [samples](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/sample/funasr_samples.tar.gz)
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```shell
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python3 funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode 2pass
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```
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For more examples, please refer to [docs](runtime/docs/SDK_tutorial_online_zh.md)
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#### File Transcription Service, Mandarin (CPU)
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##### Server Deployment
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You can use the following command to complete the deployment:
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```shell
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curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/funasr-runtime-deploy-offline-cpu-zh.sh
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sudo bash funasr-runtime-deploy-offline-cpu-zh.sh install --workspace ./funasr-runtime-resources
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```
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##### Client Testing
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Testing [samples](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/sample/funasr_samples.tar.gz)
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```shell
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python3 funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode offline --audio_in "../audio/asr_example.wav"
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```
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For more examples, please refer to [docs](runtime/docs/SDK_tutorial_zh.md)
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## Industrial Model Egs
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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:
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
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)
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rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
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print(rec_result)
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# {'text': '欢迎大家来体验达摩院推出的语音识别模型'}
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```
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More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)
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## Academic model egs
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If you want to train from scratch, usually for academic models, you can start training and inference with the following command:
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```shell
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cd egs/aishell/paraformer
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. ./run.sh --CUDA_VISIBLE_DEVICES="0,1" --gpu_num=2
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```
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More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html) |