onerahmet/openai-whisper-asr-webservice Docker 镜像 - 轩辕镜像 | Docker 镜像高效稳定拉取服务

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Whisper ASR Box

Whisper ASR Box is a general-purpose speech recognition toolkit. Whisper Models are trained on a large dataset of diverse audio and is also a multitask model that can perform multilingual speech recognition as well as speech translation and language identification.

Features

Current release (v1.8.2) supports following whisper models:

• openai/whisper@v20240930
• SYSTRAN/faster-whisper@v1.1.0
• whisperX@v3.1.1

Quick Usage

CPU

shell
docker run -d -p 9000:9000 \
  -e ASR_MODEL=base \
  -e ASR_ENGINE=openai_whisper \
  onerahmet/openai-whisper-asr-webservice:latest

GPU

shell
docker run -d --gpus all -p 9000:9000 \
  -e ASR_MODEL=base \
  -e ASR_ENGINE=openai_whisper \
  onerahmet/openai-whisper-asr-webservice:latest-gpu

Cache

To reduce container startup time by avoiding repeated downloads, you can persist the cache directory:

shell
docker run -d -p 9000:9000 \
  -v $PWD/cache:/root/.cache/ \
  onerahmet/openai-whisper-asr-webservice:latest

Key Features

• Multiple ASR engines support (OpenAI Whisper, Faster Whisper, WhisperX)
• Multiple output formats (text, JSON, VTT, SRT, TSV)
• Word-level timestamps support
• Voice activity detection (VAD) filtering
• Speaker diarization (with WhisperX)
• FFmpeg integration for broad audio/video format support
• GPU acceleration support
• Configurable model loading/unloading
• REST API with Swagger documentation

Environment Variables

Key configuration options:

• ASR_ENGINE: Engine selection (openai_whisper, faster_whisper, whisperx)
• ASR_MODEL: Model selection (tiny, base, small, medium, large-v3, etc.)
• ASR_MODEL_PATH: Custom path to store/load models
• ASR_DEVICE: Device selection (cuda, cpu)
• MODEL_IDLE_TIMEOUT: Timeout for model unloading

Documentation

For complete documentation, visit: [***]

Development

shell
# Install poetry
pip3 install poetry

# Install dependencies
poetry install

# Run service
poetry run whisper-asr-webservice --host 0.0.0.0 --port 9000

After starting the service, visit http://localhost:9000 or [***] in your browser to access the Swagger UI documentation and try out the API endpoints.

Credits

• This software uses libraries from the FFmpeg project under the LGPLv2.1