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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

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

cd runtime/python/websocket
pip install -r requirements_server.txt

Start server

API-reference
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
python funasr_wss_server.py --port 10095

For the client

Install the requirements for client

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

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

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

# --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)

# --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

# --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)

# --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

# --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)

# --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

# 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.
  2. We acknowledge zhaoming for contributing the websocket service.
  3. We acknowledge cgisky1980 for contributing the websocket service of offline model.