faster-whisper
Faster Whisper transcription with CTranslate2
Decision gist · record as of 2026-08-14
Yes, if you need Whisper transcription and speed or memory efficiency matters. The package is mature (Beta status, 24910 stars, no known vulnerabilities), permissively licensed, and has low install friction. Maintenance is aging (287 days since last release), but the repository remains active. GPU users must have CUDA 12 libraries available; CPU-only use is simpler. Start here if you're choosing between openai/whisper and faster-whisper for the same accuracy at lower cost.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- GPU execution requires NVIDIA cuBLAS and cuDNN libraries for CUDA 12; CPU-only use works without them.
- Python 3.9 or greater required.
- Low friction install with a pure-Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use this package freely in commercial and private projects with minimal restrictions.
last release 2025-10-31 (287 days) · last repo commit 2025-11-19 · 24,910 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 8,952,579 downloads/mo, #1,572 on PyPI
Alternatives
Verify before relying
pip install faster-whisper
from faster_whisper import WhisperModel
model = WhisperModel("large-v3", device="cuda", compute_type="float16")
segments, info = model.transcribe("audio.mp3", beam_size=5)
for segment in segments:
print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")- Whether the 4x speedup claim applies to all model sizes and hardware configurations, or only specific benchmarked scenarios.
- Real-world accuracy parity with openai/whisper across diverse audio types and languages.
- Whether batched inference (batch_size parameter) is stable and recommended for production use.
What it is and what it does
Faster-whisper is a reimplementation of OpenAI's Whisper speech-to-text model using CTranslate2, a fast inference engine for Transformer models. It trades the original Whisper library for a more efficient backend, achieving measurable speed and memory improvements on both CPU and GPU while maintaining transcription accuracy. The package handles audio decoding via PyAV (bundled FFmpeg), so you don't need to install FFmpeg separately.
The package supports multiple precision modes (fp32, fp16, int8) and batch processing, allowing you to tune speed and memory trade-offs for your hardware. It can run on CPU or GPU, and works with Whisper's standard model sizes (tiny, base, small, medium, large) as well as Distil-Whisper checkpoints. The transcription API returns segments with timestamps and detected language information.
Use it for
- Transcribe long audio or video files on GPU with reduced latency and VRAM compared to openai/whisper.
- Run speech-to-text on CPU with int8 quantization to fit within constrained memory budgets.
- Batch-process multiple audio files in parallel using the BatchedInferencePipeline for throughput.
- Deploy Whisper transcription in production where inference speed and memory efficiency are critical.
- Use Distil-Whisper checkpoints for faster, lighter-weight transcription with acceptable accuracy trade-off.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need Whisper transcription and speed or memory efficiency matters.
The package is mature (Beta status, 24910 stars, no known vulnerabilities), permissively licensed, and has low install friction. Maintenance is aging (287 days since last release), but the repository remains active. GPU users must have CUDA 12 libraries available; CPU-only use is simpler. Start here if you're choosing between openai/whisper and faster-whisper for the same accuracy at lower cost.
Install
faster-whisper on PyPI
Before you install
Low friction install with a pure-Python wheel. Maintenance is aging—last release was 287 days ago—but the repository remains active with recent commits and substantial community engagement (24910 stars). Six runtime dependencies are all established packages.
GPU execution requires NVIDIA cuBLAS and cuDNN libraries for CUDA 12; CPU-only use works without them. Python 3.9 or greater required.
License in practice
MIT license is permissive; you can use this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install faster-whisper
from faster_whisper import WhisperModel
model = WhisperModel("large-v3", device="cuda", compute_type="float16")
segments, info = model.transcribe("audio.mp3", beam_size=5)
for segment in segments:
print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")Verify before relying
- Whether the 4x speedup claim applies to all model sizes and hardware configurations, or only specific benchmarked scenarios.
- Real-world accuracy parity with openai/whisper across diverse audio types and languages.
- Whether batched inference (batch_size parameter) is stable and recommended for production use.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesctranslate2huggingface-hubtokenizersonnxruntimeavtqdm |
| Maintenance | Aging 287 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 8,952,579 / month, #1,572 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: faster_whisper-1.2.1-py3-none-any.whl
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See also ctranslate2 · openai-whisper · pywhispercpp · realtimestt · whisperx · whisper-timestamped · mlx-whisper · SpeechRecognition · omnivoice · pyctcdecode