1/7/2024 0 Comments Lingvanex offline translator![]() whisper-standalone-win contains the portable ready to run binaries of faster-whisper for Windows.whisper-diarize is a speaker diarization tool that is based on faster-whisper and NVIDIA NeMo.whisper-ctranslate2 is a command line client based on faster-whisper and compatible with the original client from openai/whisper.Feel free to add your project to the list! Here is a non exhaustive list of open-source projects using faster-whisper. See more model and transcription options in the WhisperModel class implementation. Note that LD_LIBRARY_PATH must be set before launching Python. On Linux these libraries can be installed with pip. The libraries are installed in this official NVIDIA Docker image: nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04. Other installation methods (click to expand) Use Docker The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below. There are multiple ways to install these libraries. GPU execution requires the following NVIDIA libraries to be installed: The audio is decoded with the Python library PyAV which bundles the FFmpeg libraries in its package. ![]() Unlike openai-whisper, FFmpeg does not need to be installed on the system. Small model on CPU ImplementationĮxecuted with 8 threads on a Intel(R) Xeon(R) Gold 6226R. Benchmarkįor reference, here's the time and memory usage that are required to transcribe 13 minutes of audio using different implementations:Įxecuted with CUDA 11.7.1 on a NVIDIA Tesla V100S. The efficiency can be further improved with 8-bit quantization on both CPU and GPU. This implementation is up to 4 times faster than openai/whisper for the same accuracy while using less memory. Faster Whisper transcription with CTranslate2įaster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models. ![]()
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