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rm quick_start_zh.md
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([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 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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### Service Deployment Software
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Both high-precision, high-efficiency, and high-concurrency file transcription, as well as low-latency real-time speech recognition, are supported. It also supports Docker deployment and multiple concurrent requests.
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##### Docker Installation (optional)
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###### If you have already installed Docker, skip this step.
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```shell
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curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/install_docker.sh;
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sudo bash install_docker.sh
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```
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##### Real-time Speech Recognition Service Deployment
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###### Docker Image Download and Launch
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Use the following command to pull and launch the FunASR software package Docker image（[Get the latest image version](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/SDK_advanced_guide_online.md)）：
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```shell
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sudo docker pull \
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registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-online-cpu-0.1.6
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mkdir -p ./funasr-runtime-resources/models
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sudo docker run -p 10096:10095 -it --privileged=true \
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-v $PWD/funasr-runtime-resources/models:/workspace/models \
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registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-online-cpu-0.1.6
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```
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###### Server Start
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After Docker is started, start the funasr-wss-server-2pass service program:
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```shell
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cd FunASR/runtime
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nohup bash run_server_2pass.sh \
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--download-model-dir /workspace/models \
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--vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
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--model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
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--online-model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online-onnx \
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--punc-dir damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727-onnx \
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--itn-dir thuduj12/fst_itn_zh \
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--hotword /workspace/models/hotwords.txt > log.txt 2>&1 &
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# If you want to disable SSL, add the parameter: --certfile 0
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# If you want to deploy with a timestamp or nn hotword model, please set --model-dir to the corresponding model:
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# damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-onnx (timestamp)
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# damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404-onnx (nn hotword)
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# If you want to load hotwords on the server side, please configure the hotwords in the host file ./funasr-runtime-resources/models/hotwords.txt (docker mapping address is /workspace/models/hotwords.txt):
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# One hotword per line, format (hotword weight): Alibaba 20
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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 10096 --mode 2pass
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```
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For more examples, please refer to [docs](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/SDK_advanced_guide_online.md)
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#### File Transcription Service, Mandarin (CPU)
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###### Docker Image Download and Launch
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Use the following command to pull and launch the FunASR software package Docker image（[Get the latest image version](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/SDK_advanced_guide_offline.md)）：
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```shell
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sudo docker pull \
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registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.4.1
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mkdir -p ./funasr-runtime-resources/models
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sudo docker run -p 10095:10095 -it --privileged=true \
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-v $PWD/funasr-runtime-resources/models:/workspace/models \
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registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.4.1
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```
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###### Server Start
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After Docker is started, start the funasr-wss-server service program:
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```shell
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cd FunASR/runtime
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nohup bash run_server.sh \
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--download-model-dir /workspace/models \
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--vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
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--model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
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--punc-dir damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx \
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--lm-dir damo/speech_ngram_lm_zh-cn-ai-wesp-fst \
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--itn-dir thuduj12/fst_itn_zh \
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--hotword /workspace/models/hotwords.txt > log.txt 2>&1 &
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# If you want to disable SSL, add the parameter: --certfile 0
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# If you want to use timestamp or nn hotword models for deployment, please set --model-dir to the corresponding model:
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# damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-onnx (timestamp)
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# damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404-onnx (nn hotword)
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# If you want to load hotwords on the server side, please configure the hotwords in the host machine file ./funasr-runtime-resources/models/hotwords.txt (docker mapping address is /workspace/models/hotwords.txt):
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# One hotword per line, format (hotword weight): Alibaba 20
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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](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/SDK_advanced_guide_offline.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-vad-punc_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)
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@ -1,164 +0,0 @@
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(简体中文|[English](./quick_start.md))
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<a name="快速开始"></a>
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## 快速开始
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您可以通过如下几种方式使用FunASR功能:
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- 服务部署社区软件包
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- 工业模型egs
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- 学术模型egs
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### 服务部署社区软件包
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#### python版本示例
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支持实时流式语音识别，并且会用非流式模型进行纠错，输出文本带有标点。目前只支持单个client，如需多并发请参考下方c++版本服务部署SDK
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##### 服务端部署
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```shell
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cd runtime/python/websocket
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python funasr_wss_server.py --port 10095
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```
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##### 客户端测试
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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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#python funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode 2pass --chunk_size "8,8,4" --audio_in "./data/wav.scp"
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```
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更多例子可以参考（[点击此处](../runtime/python/websocket/README.md)）
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<a name="cpp版本示例"></a>
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#### 服务部署软件包
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既可以进行高精度、高效率与高并发的文件转写，也可以进行低延时的实时语音听写。支持Docker化部署，多路请求。
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##### 准备工作：docker安装（可选）
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###### 如果您已安装docker，忽略本步骤
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```shell
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curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/install_docker.sh;
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sudo bash install_docker.sh
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```
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##### 实时语音听写服务部署
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###### docker镜像下载与启动
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通过下述命令拉取并启动FunASR软件包docker镜像（[获取最新镜像版本](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/SDK_advanced_guide_online_zh.md)）：
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```shell
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sudo docker pull \
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registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-online-cpu-0.1.6
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mkdir -p ./funasr-runtime-resources/models
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sudo docker run -p 10096:10095 -it --privileged=true \
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-v $PWD/funasr-runtime-resources/models:/workspace/models \
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registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-online-cpu-0.1.6
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```
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###### 服务端启动
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docker启动之后，启动 funasr-wss-server-2pass服务程序：
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```shell
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cd FunASR/runtime
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nohup bash run_server_2pass.sh \
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--download-model-dir /workspace/models \
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--vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
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--model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
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--online-model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online-onnx \
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--punc-dir damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727-onnx \
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--itn-dir thuduj12/fst_itn_zh \
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--hotword /workspace/models/hotwords.txt > log.txt 2>&1 &
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# 如果您想关闭ssl，增加参数：--certfile 0
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# 如果您想使用时间戳或者nn热词模型进行部署，请设置--model-dir为对应模型：
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# damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-onnx（时间戳）
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# damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404-onnx（nn热词）
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# 如果您想在服务端加载热词，请在宿主机文件./funasr-runtime-resources/models/hotwords.txt配置热词（docker映射地址为/workspace/models/hotwords.txt）:
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# 每行一个热词，格式(热词 权重)：阿里巴巴 20
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```
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##### 客户端测试与使用
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客户端测试（[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 10096 --mode 2pass
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```
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更多例子参考（[点击此处](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/SDK_advanced_guide_online_zh.md)）
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##### 离线文件转写服务部署
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###### 镜像启动
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通过下述命令拉取并启动FunASR软件包docker镜像（[获取最新镜像版本](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/SDK_advanced_guide_offline_zh.md)）：
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```shell
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sudo docker pull \
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registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.4.1
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mkdir -p ./funasr-runtime-resources/models
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sudo docker run -p 10095:10095 -it --privileged=true \
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-v $PWD/funasr-runtime-resources/models:/workspace/models \
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registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.4.1
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```
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###### 服务端启动
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docker启动之后，启动 funasr-wss-server服务程序：
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```shell
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cd FunASR/runtime
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nohup bash run_server.sh \
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--download-model-dir /workspace/models \
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--vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
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--model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
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--punc-dir damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx \
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--lm-dir damo/speech_ngram_lm_zh-cn-ai-wesp-fst \
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--itn-dir thuduj12/fst_itn_zh \
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--hotword /workspace/models/hotwords.txt > log.txt 2>&1 &
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# 如果您想关闭ssl，增加参数：--certfile 0
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# 如果您想使用时间戳或者nn热词模型进行部署，请设置--model-dir为对应模型：
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# damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-onnx（时间戳）
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# damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404-onnx（nn热词）
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# 如果您想在服务端加载热词，请在宿主机文件./funasr-runtime-resources/models/hotwords.txt配置热词（docker映射地址为/workspace/models/hotwords.txt）:
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# 每行一个热词，格式(热词 权重)：阿里巴巴 20
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```
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###### 客户端测试
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客户端测试（[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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更多例子参考（[点击此处](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/docs/SDK_advanced_guide_offline_zh.md)）
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### 工业模型egs
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如果您希望使用ModelScope中预训练好的工业模型，进行推理或者微调训练，您可以参考下面指令：
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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-vad-punc_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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更多例子可以参考（[点击此处](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)）
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### 学术模型egs
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如果您希望从头开始训练，通常为学术模型，您可以通过下面的指令启动训练与推理：
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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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更多例子可以参考（[点击此处](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)）
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