faster-whisper is by SYSTRAN — not by us.
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Indexed Aug 3, 2026 · 24,719 stars at index time. Maintainers: claiming verifies your identity and unlocks a higher assurance tier. Removal requests are honored.
Application · for humans & their agents
faster-whisper
unclaimed listingactively maintainedFreeMIT
Faster Whisper transcription with CTranslate2
by Open Source Community · New publisher
Faster Whisper transcription with CTranslate2
SYSTRAN/faster-whisper is an open-source project by SYSTRAN: Faster Whisper transcription with CTranslate2. Indexed here so it can be found — not resold.
It is free. Get it from the upstream repository: https://github.com/SYSTRAN/faster-whisper
From the project's own README (excerpt, reproduced for discovery under its MIT license):
Faster Whisper transcription with CTranslate2
faster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models.
This implementation is up to 4 times faster than openai/whisper for the same accuracy while using less memory. The efficiency can be further improved with 8-bit quantization on both CPU and GPU.
Benchmark
Whisper
For reference, here's the time and memory usage that are required to transcribe 13 minutes of audio using different implementations:
openai/whisper@v20240930 whisper.cpp@v1.7.2 transformers@v4.46.3 faster-whisper@v1.1.0
Large-v2 model on GPU
Implementation Precision Beam size Time VRAM Usage --- --- --- --- --- openai/whisper fp16 5 2m23s 4708MB whisper.cpp (Flash Attention) fp16 5 1m05s 4127MB transformers (SDPA)[^1] fp16 5 1m52s 4960MB faster-whisper fp16 5 1m03s 4525MB faster-whisper (batchsize=8) fp16 5 17s 6090MB faster-whisper int8 5 59s 2926MB faster-whisper (batchsize=8) int8 5 16s 4500MB
distil-whisper-large-v3 model on GPU
Implementation Precision Beam size Time YT Commons WER --- --- --- --- --- transformers (SDPA) (batchsize=16) fp16 5 46m12s 14.801 faster-whisper (batchsize=16) fp16 5 25m50s 13.527
GPU Benchmarks are Executed with CUDA 12.4 on a NVIDIA RTX 3070 Ti 8GB. [^1]: transformers OOM for any batch size > 1
Small model on CPU
Implementation Precision Beam size Time RAM Usage --- --- --- --- --- openai/whisper fp32 5 6m58s 2335MB whisper.cpp fp32 5 2m05s 1049MB whisper.cpp (OpenVINO) fp32 5 1m45s 1642MB faster-whisper fp32 5 2m37s 2257MB faster-whisper (batchsize=8) fp32 5 1m06s 4230MB faster-whisper int8 5 1m42s 1477MB faster-whisper (batchsize=8) int8 5 51s 3608MB
Executed with 8 threads on an…
01Capabilities
Does
- + Audio codec
- + Voice input capture
- + Media processing
Doesn’t
- No exclusions declared
02Requirements & stack
Depends on
No declared dependencies
Credentials needed
None declared
Stack
03Community
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Issues 0
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04Trust Passport
Full passport →0/0 automated components pass. An automated score is never a security guarantee.
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- capability contract All 0 observed capability reference(s) match the declared manifest
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05Versions
Full history →| Version | Channel | Released | Notes |
|---|---|---|---|
| 0.0.0 | stable | Aug 3, 2026 | Indexed listing — see the upstream repository for real release history. |