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It is built for transcription pipelines that need lower latency, lower memory usage, optional quantization, and practical Python integration for batch or real-time speech workflows.\n\n## Installation\n\nUse the upstream install or setup path that matches your environment:\n- #### Use Docker\n- pip install nvidia-cublas-cu12 nvidia-cudnn-cu12==9.*\n- pip install faster-whisper\n- pip install --force-reinstall \"faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz\"\n\nRequirements and caveats from upstream:\n- Python 3.9 or greater\n- Unlike openai-whisper, FFmpeg does **not** need to be installed on the system. The audio is decoded with the Python library [PyAV](https://github.com/PyAV-Org/PyAV) which bundles the FFmpeg libraries in its package.\n- GPU execution requires the following NVIDIA libraries to be installed:\n\nBasic usage or getting-started notes:\n- For reference, here's the time and memory usage that are required to transcribe [**13 minutes**](https://www.youtube.com/watch?v=0u7tTptBo9I) of audio using different implementations:\n- | Implementation | Precision | Beam size | Time | VRAM Usage |\n- | Implementation | Precision | Beam size | Time | RAM Usage |\n\n- Source: https://github.com/SYSTRAN/faster-whisper\n- Extracted from upstream docs: https://raw.githubusercontent.com/SYSTRAN/faster-whisper/HEAD/README.md\n\n## Source\n\n- [Agent Skill 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