whisper-cpp service

open sourde address

github gitee

Whisper-CPP-Server Introduction

Whisper-CPP-Server is a high-performance speech recognition service written in C++, designed to provide developers and enterprises with a reliable and efficient speech-to-text inference engine. This project implements technology from ggml to perform inference on the open-source Whisper model. While ensuring speed and accuracy, it supports pure CPU-based inference operations, allowing for high-quality speech recognition services without the need for specialized hardware accelerators.

Real-time speech recognition and display of recognition results in the browser backend

https://github.com/litongjava/whisper-cpp-server

frontend

https://github.com/litongjava/listen-know-web

Test video

whisper-cpp-server-test.mp4

Main Features

1.Pure C++ Inference Engine Whisper-CPP-Server is entirely written in C++, leveraging the efficiency of C++ for rapid processing of vast amounts of voice data, even in environments that only have CPUs for computing power.

2.High Performance Thanks to the computational efficiency of C++, Whisper-CPP-Server can offer exceptionally high processing speeds, meeting real-time or near-real-time speech recognition demands. It is especially suited for scenarios that require processing large volumes of voice data.

3.Support for Multiple Languages The service supports speech recognition in multiple languages, broadening its applicability across various linguistic contexts.

4.Docker Container Support A Docker image is provided, enabling quick deployment of the service through simple command-line operations, significantly simplifying installation and configuration processes. Deploy using the following command:

docker run -dit --name whisper-server -p 8080:8080 litongjava/whisper-cpp-server:1.0.0-large-v3

This means you can run Whisper-CPP-Server on any platform that supports Docker, including but not limited to Linux, Windows, and macOS.

4.Easy Integration for Clients Detailed client integration documentation is provided, helping developers quickly incorporate speech recognition functionality into their applications. Client Code Documentation

Applicable Scenarios

Whisper-CPP-Server is suitable for a variety of applications that require fast and accurate speech recognition, including but not limited to:

• Voice-driven interactive applications
• Transcription of meeting records
• Automatic subtitle generation
• Automatic translation of multi-language content

How to build it

build with cmake and vcpkg

git clone https://github.com/litongjava/whisper-cpp-server.git
git submodule init
git submodule update
cmake -B cmake-build-release
cp ./ggml-metal.metal cmake-build-release 
cmake --build cmake-build-release --config Release -- -j 12

macos

cmake -B cmake-build-release -DWHISPER_COREML=1

run with simplest

./cmake-build-release/simplest -m models/ggml-base.en.bin test.wav

run with http-server

./cmake-build-release/whisper_http_server_base_httplib -m models/ggml-base.en.bin 

run with websocket-server

./cmake-build-release/whisper_server_base_on_uwebsockets -m models/ggml-base.en.bin

copy command

mkdir bin
cp ./ggml-metal.metal bin
cp ./cmake-build-release/simplest bin
cp ./cmake-build-release/whisper_http_server_base_httplib bin 
cp ./cmake-build-release/whisper_server_base_on_uwebsockets bin

simplest

cmake-build-debug/simplest -m models/ggml-base.en.bin samples/jfk.wav

simplest [options] file0.wav file1.wav ...

options:                                                                                                                                                                                                
-h,        --help              [default] show this help message and exit                                                                                                                              
-m FNAME,  --model FNAME       [models/ggml-base.en.bin] model path                                                                                                                                   
-di,       --diarize           [false  ] stereo audio diarization

whisper_http_server_base_httplib

Simple http service. WAV mp4 and m4a Files are passed to the inference model via http requests.

./whisper_http_server_base_httplib -h

usage: ./bin/whisper_http_server_base_httplib [options]

options:
  -h,        --help              [default] show this help message and exit
  -t N,      --threads N         [4      ] number of threads to use during computation
  -p N,      --processors N      [1      ] number of processors to use during computation
  -ot N,     --offset-t N        [0      ] time offset in milliseconds
  -on N,     --offset-n N        [0      ] segment index offset
  -d  N,     --duration N        [0      ] duration of audio to process in milliseconds
  -mc N,     --max-context N     [-1     ] maximum number of text context tokens to store
  -ml N,     --max-len N         [0      ] maximum segment length in characters
  -sow,      --split-on-word     [false  ] split on word rather than on token
  -bo N,     --best-of N         [2      ] number of best candidates to keep
  -bs N,     --beam-size N       [-1     ] beam size for beam search
  -wt N,     --word-thold N      [0.01   ] word timestamp probability threshold
  -et N,     --entropy-thold N   [2.40   ] entropy threshold for decoder fail
  -lpt N,    --logprob-thold N   [-1.00  ] log probability threshold for decoder fail
  -debug,    --debug-mode        [false  ] enable debug mode (eg. dump log_mel)
  -tr,       --translate         [false  ] translate from source language to english
  -di,       --diarize           [false  ] stereo audio diarization
  -tdrz,     --tinydiarize       [false  ] enable tinydiarize (requires a tdrz model)
  -nf,       --no-fallback       [false  ] do not use temperature fallback while decoding
  -ps,       --print-special     [false  ] print special tokens
  -pc,       --print-colors      [false  ] print colors
  -pp,       --print-progress    [false  ] print progress
  -nt,       --no-timestamps     [false  ] do not print timestamps
  -l LANG,   --language LANG     [en     ] spoken language ('auto' for auto-detect)
  -dl,       --detect-language   [false  ] exit after automatically detecting language
             --prompt PROMPT     [       ] initial prompt
  -m FNAME,  --model FNAME       [models/ggml-base.en.bin] model path
  -oved D,   --ov-e-device DNAME [CPU    ] the OpenVINO device used for encode inference
  --host HOST,                   [127.0.0.1] Hostname/ip-adress for the service
  --port PORT,                   [8080   ] Port number for the service

start whisper_http_server_base_httplib

./cmake-build-debug/whisper_http_server_base_httplib -m models/ggml-base.en.bin

Test server
see request doc in doc

request examples

/inference

curl --location --request POST http://127.0.0.1:8080/inference \
--form file=@"./samples/jfk.wav" \
--form temperature="0.2" \
--form response-format="json"
--form audio_format="wav"

/load

curl 127.0.0.1:8080/load \
-H "Content-Type: multipart/form-data" \
-F model="<path-to-model-file>"

whisper_server_base_on_uwebsockets

web socket server
start server

./cmake-build-debug/whisper_server_base_on_uwebsockets -m models/ggml-base.en.bin

Test server see python client

Docker

run whisper-cpp-server:1.0.0

Dockerfile

docker run -dit --name=whisper-server -p 8080:8080 -v "$(pwd)/models/ggml-base.en.bin":/models/ggml-base.en.bin litongjava/whisper-cpp-server:1.0.0 /app/whisper_http_server_base_httplib -m /models/ggml-base.en.bin

the port is 8080

test

curl --location --request POST 'http://127.0.0.1:8080/inference' \
--header 'Accept: */*' \
--header 'Content-Type: multipart/form-data; boundary=--------------------------671827497522367123871197' \
--form 'file=@"E:\\code\\cpp\\cpp-study\\cpp-study-clion\\audio\\jfk.wav"' \
--form 'temperature="0.2"' \
--form 'response-format="json"' \
--form 'audio_format="wav"'

run whisper-cpp-server:1.0.0-base-en

Dockerfile

docker run -dit --name whisper-server -p 8080:8080 litongjava/whisper-cpp-server:1.0.0-base-en

run whisper-cpp-server:1.0.0-large-v3

Dockerfile

docker run -dit --name whisper-server -p 8080:8080 litongjava/whisper-cpp-server:1.0.0-large-v3

run whisper-cpp-server:1.0.0-tiny.en-q5_1

Dockerfile

docker run -dit --name whisper-server -p 8080:8080 litongjava/whisper-cpp-server:1.0.0-tiny.en-q5_1

Client code

Client code