#1313th of 19 in Voice

faster-whisper

Python Whisper speech-to-text library built on CTranslate2

Stars
25.8k
License
MIT
Last commit
Oct 2026
Last release
Oct 2025
Language
Python

Missed the latest GitHub check; these facts may be older.

Overview

Reimplementation of OpenAI's Whisper on the CTranslate2 inference engine, installed with pip and used from Python. The README reports up to 4x faster transcription than openai/whisper with lower memory, with int8 quantization on CPU and GPU. It supports batched transcription, word-level timestamps, Silero VAD filtering, and Distil-Whisper checkpoints.

Who it is for: Python developers adding fast local Whisper transcription to apps or pipelines

Strengths

  • README benchmark: 13 min audio in 1m03s fp16 on RTX 3070 Ti, 17s with batch_size=8
  • int8 quantization on CPU and GPU cuts memory (2926MB vs 4525MB VRAM for large-v2)
  • No system FFmpeg needed; audio decoded through PyAV bundled libraries
  • Converts any Transformers-compatible Whisper model, including fine-tuned ones, with ct2-transformers-converter

Weaknesses

  • Library only; no built-in server, UI or CLI (these come from community projects)
  • GPU needs separately installed cuBLAS for CUDA 12 and cuDNN 9
  • Older CUDA 11 or cuDNN 8 setups require downgrading ctranslate2
  • whisper.cpp was faster on the README's CPU fp32 benchmark (2m05s vs 2m37s)

What it needs

  • GPU optional
  • Docker
  • Needs CTranslate2, PyAV, cuBLAS for CUDA 12 (GPU), cuDNN 9 (GPU), Silero VAD
  • Models: Whisper (CTranslate2 format), Distil-Whisper, Hugging Face Transformers Whisper conversion

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