whisper-ctranslate2
Whisper command-line client compatible with original OpenAI client based on CTranslate2
GraphCanon updated 3w · GitHub synced 3w · 31 views this month
Decision brief
Whisper-ctranslate2 provides a GPU-accelerated Docker-based command-line interface for Whisper speech recognition models with easy file input.
Good fit when
- Require Whisper model compatibility in a preconfigured Docker container
- Need to leverage GPU acceleration for faster processing
Avoid when
- Seeking non-Dockerized tool or do not want to use containers
- Do not have access to a GPU and need better CPU performance options
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (165d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No criticals
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install whisper-ctranslate2How it fits your stack(1)
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Evidence and technical details
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Overview
This project offers a command-line interface for Whisper speech recognition models using CTranslate2, accessible via Docker containers that include preconfigured model sizes.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 30, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 30, 2026
- Languages
- python
Source: github.language · Jul 30, 2026
Categories
Tags
README
Using prebuild Docker image
You can use build docker image. First pull the image:
docker pull ghcr.io/softcatala/whisper-ctranslate2:latest
The Docker image includes the small, medium and large-v2 models.
To run it:
docker run --gpus "device=0" \
-v "$(pwd)":/srv/files/ \
-it ghcr.io/softcatala/whisper-ctranslate2:latest \
/srv/files/e2e-tests/gossos.mp3 \
--output_dir /srv/files/
Notes:
- --gpus "device=0" gives access to the GPU. If you do not have a GPU, remove this.
- "$(pwd)":/srv/files/ maps your current directory to /srv/files/ inside the container
If you always need to use a model that is not in the image, you can create a derived Docker image with the model preloaded or use Docker volumes to persist and share the model files.
For agents
This page has a .md twin and JSON over the API.