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--- |
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title: Automatic speech recognition |
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sdk: gradio |
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app_file: src/app.py |
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python_version: 3.10 |
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sdk_version: 4.21.0 |
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app_port: 7860 |
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tags: [asr, stt, speech-to-text, whisper, pyannote, diarization] |
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pinned: true |
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emoji: ✍️ |
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--- |
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# Automatic speech recognition |
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[](https://www.gnu.org/licenses/gpl-3.0) |
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[](https://huggingface.co/spaces/tools4eu/asr) |
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Automatic speech recognition uses [Whisper](https://github.com/openai/whisper) to transcribe audio files and [pyannote-audio](https://github.com/pyannote/pyannote-audio) to add speaker diarization. |
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It has optimized inference because of batching and Scale-Product-Attention (SDPA) or flash attention (if available). |
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> :warning: **Always review transcriptions.** Transcriptions are done using AI models which are never 100% accurate. |
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The repo contains (will contain) code to run the software |
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- as a command-line tool |
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- as graphical interface |
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- as an inference API |
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## Installation |
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### Prerequisites |
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The host machine must have an NVidia graphics card with CUDA 12.x installed natively, preferably [CUDA 12.1](https://developer.nvidia.com/cuda-12-1-0-download-archive), even when using Docker. |
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The graphics card should have at least 12GB VRAM for the largest model. |
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The host machine must have Docker installed. |
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For a Linux server, follow [these instructions](https://docs.docker.com/engine/install/) |
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For a desktop (visual UI available), follow [these instructions](https://www.docker.com/products/docker-desktop/) |
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### Docker (recommended) |
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Build the Docker image |
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`docker build -t asr .` (make sure Docker is running on your system) |
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Run the Docker image, forward port 7860 (Gradio) and pass your GPU(s) to the container |
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`docker run -p 7860:7860 --gpus all asr` |
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Or in detached mode (in background) |
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`docker run -d -p 7860:7860 --gpus all asr` |
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You can check whether it is running with |
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`docker ps` |
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If you want to follow terminal output of a detached container, you can use |
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`docker logs -f <first n digits of the container id>` |
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The first time a transcription is requested, it will download the model. |
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To avoid this happening each time, make sure you stop and start the same container, instead of using |
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`docker run ...` again |
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use `docker start <first n digits of container>` |
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You can find the list of all containers, also stopped ones by using |
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`docker ps -a` |
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To open the app, open your **browser** and go to `localhost:7860` |
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### Dev Container |
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Open the project Visual Studio Code and use CTRL + SHIFT + P and type "Rebuild and reopen in container". |
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After building, open up a terminal and activate the virtual environment |
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`source /home/jovyan/venv/bin/activate` |
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Then run the app |
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`python src/app.py` |
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## License |
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GNU General Public License v3.0 or later |
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See [COPYING](COPYING) to see the full text. |
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