FunASR/funasr/runtime/python/websocket

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

Start server

ASR offline server

API-reference

python ws_server_offline.py \
--port [port id] \
--asr_model [asr model_name] \
--punc_model [punc model_name] \
--ngpu [0 or 1] \
--ncpu [1 or 4]

Usage examples

python ws_server_offline.py --port 10095 --asr_model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"

ASR streaming server

API-reference

python ws_server_online.py \
--port [port id] \
--asr_model_online [asr model_name] \
--ngpu [0 or 1] \
--ncpu [1 or 4]

Usage examples

python ws_server_online.py --port 10095 --asr_model_online "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"

ASR offline/online 2pass server

API-reference

python ws_server_2pass.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]

Usage examples

python ws_server_2pass.py --port 10095 --asr_model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"  --asr_model_online "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"

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

Start client

API-reference

python ws_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] \
--send_without_sleep [only set for offline]

Usage examples

ASR offline client

Recording from mircrophone

# --chunk_interval, "10": 600/10=60ms, "5"=600/5=120ms, "20": 600/12=30ms
python ws_client.py --host "0.0.0.0" --port 10095 --chunk_interval 10 --words_max_print 100

Loadding from wav.scp(kaldi style)

# --chunk_interval, "10": 600/10=60ms, "5"=600/5=120ms, "20": 600/12=30ms
python ws_client.py --host "0.0.0.0" --port 10095 --chunk_interval 10 --words_max_print 100 --audio_in "./data/wav.scp" --send_without_sleep --output_dir "./results"

ASR streaming client

Recording from mircrophone

# --chunk_size, "5,10,5"=600ms, "8,8,4"=480ms
python ws_client.py --host "0.0.0.0" --port 10095 --chunk_size "5,10,5" --words_max_print 100

Loadding from wav.scp(kaldi style)

# --chunk_size, "5,10,5"=600ms, "8,8,4"=480ms
python ws_client.py --host "0.0.0.0" --port 10095 --chunk_size "5,10,5" --audio_in "./data/wav.scp" --words_max_print 100 --output_dir "./results"

ASR offline/online 2pass client

Recording from mircrophone

# --chunk_size, "5,10,5"=600ms, "8,8,4"=480ms
python ws_client.py --host "0.0.0.0" --port 10095 --chunk_size "8,8,4" --words_max_print 10000

Loadding from wav.scp(kaldi style)

# --chunk_size, "5,10,5"=600ms, "8,8,4"=480ms
python ws_client.py --host "0.0.0.0" --port 10095 --chunk_size "8,8,4" --audio_in "./data/wav.scp" --words_max_print 10000 --output_dir "./results"

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.