# Service with websocket-python This is a demo using funasr pipeline with websocket python-api. It supports the offline, online, offline/online-2pass unifying speech recognition. ## For the Server ### Install the modelscope and funasr ```shell 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 git clone https://github.com/alibaba/FunASR.git && cd FunASR ``` ### Install the requirements for server ```shell cd runtime/python/websocket pip install -r requirements_server.txt ``` ### Start server ##### API-reference ```shell python funasr_wss_server.py \ --port [port id] \ --asr_model [asr model_name] \ --asr_model_online [asr model_name] \ --punc_model [punc model_name] \ --ngpu [0 or 1] \ --ncpu [1 or 4] \ --certfile [path of certfile for ssl] \ --keyfile [path of keyfile for ssl] ``` ##### Usage examples ```shell python funasr_wss_server.py --port 10095 ``` ## For the client Install the requirements for client ```shell git clone https://github.com/alibaba/FunASR.git && cd FunASR cd funasr/runtime/python/websocket pip install -r requirements_client.txt ``` If you want infer from videos, you should install `ffmpeg` ```shell apt-get install -y ffmpeg #ubuntu # yum install -y ffmpeg # centos # brew install ffmpeg # mac # winget install ffmpeg # wins pip3 install websockets ffmpeg-python ``` ### Start client #### API-reference ```shell python funasr_wss_client.py \ --host [ip_address] \ --port [port id] \ --chunk_size ["5,10,5"=600ms, "8,8,4"=480ms] \ --chunk_interval [duration of send chunk_size/chunk_interval] \ --words_max_print [max number of words to print] \ --audio_in [if set, loadding from wav.scp, else recording from mircrophone] \ --output_dir [if set, write the results to output_dir] \ --mode [`online` for streaming asr, `offline` for non-streaming, `2pass` for unifying streaming and non-streaming asr] \ --thread_num [thread_num for send data] ``` #### Usage examples ##### ASR offline client Recording from mircrophone ```shell # --chunk_interval, "10": 600/10=60ms, "5"=600/5=120ms, "20": 600/12=30ms python funasr_wss_client.py --host "0.0.0.0" --port 10095 --mode offline ``` Loadding from wav.scp(kaldi style) ```shell # --chunk_interval, "10": 600/10=60ms, "5"=600/5=120ms, "20": 600/12=30ms python funasr_wss_client.py --host "0.0.0.0" --port 10095 --mode offline --audio_in "./data/wav.scp" --output_dir "./results" ``` ##### ASR streaming client Recording from mircrophone ```shell # --chunk_size, "5,10,5"=600ms, "8,8,4"=480ms python funasr_wss_client.py --host "0.0.0.0" --port 10095 --mode online --chunk_size "5,10,5" ``` Loadding from wav.scp(kaldi style) ```shell # --chunk_size, "5,10,5"=600ms, "8,8,4"=480ms python funasr_wss_client.py --host "0.0.0.0" --port 10095 --mode online --chunk_size "5,10,5" --audio_in "./data/wav.scp" --output_dir "./results" ``` ##### ASR offline/online 2pass client Recording from mircrophone ```shell # --chunk_size, "5,10,5"=600ms, "8,8,4"=480ms python funasr_wss_client.py --host "0.0.0.0" --port 10095 --mode 2pass --chunk_size "8,8,4" ``` Loadding from wav.scp(kaldi style) ```shell # --chunk_size, "5,10,5"=600ms, "8,8,4"=480ms python funasr_wss_client.py --host "0.0.0.0" --port 10095 --mode 2pass --chunk_size "8,8,4" --audio_in "./data/wav.scp" --output_dir "./results" ``` #### Websocket api ```shell # class Funasr_websocket_recognizer example with 3 step # 1.create an recognizer rcg=Funasr_websocket_recognizer(host="127.0.0.1",port="30035",is_ssl=True,mode="2pass") # 2.send pcm data to asr engine and get asr result text=rcg.feed_chunk(data) print("text",text) # 3.get last result, set timeout=3 text=rcg.close(timeout=3) print("text",text) ``` ## Acknowledge 1. This project is maintained by [FunASR community](https://github.com/alibaba-damo-academy/FunASR). 2. We acknowledge [zhaoming](https://github.com/zhaomingwork/FunASR/tree/fix_bug_for_python_websocket) for contributing the websocket service. 3. We acknowledge [cgisky1980](https://github.com/cgisky1980/FunASR) for contributing the websocket service of offline model.