faster-whisper-large-v3

Name: faster-whisper-large-v3
Author: Systran

by Systran

Whisper large-v3 converted to CTranslate2, for transcribing archives rather than clips

1.1Mdl/month

631likes

HuggingFace Run it on your own data, free

Identifier

Model ID

Systran/faster-whisper-large-v3

Overview

This is not a different model from Whisper large-v3. It is the same weights converted to the CTranslate2 runtime, and the reason it matters is throughput per dollar rather than accuracy.

Transcription is usually the step that decides whether you process an entire back catalogue or only the files someone asks about. Reference Whisper is accurate and expensive enough that teams sample. A faster runtime over identical weights changes that arithmetic without changing what the output says, which is the rare case where you can take the speed and lose nothing.

MIT licensed, multilingual, and it is what most self-hosted transcription pipelines actually run in production.

Architecture

OpenAI Whisper large-v3 weights converted to the CTranslate2 inference engine. The HuggingFace record carries no parameter count because the repository holds a converted format rather than safetensors; the underlying model is Whisper large-v3.

Key Capabilities

•Transcription with Whisper large-v3 accuracy
•Substantially higher throughput than the reference implementation on the same hardware
•Multilingual, with the same language coverage as the source model
•Word-level timestamps for locating a moment rather than a file

Use Cases on Mixpeek

•Transcribing a whole media archive where reference Whisper would only be affordable on a sample
•Producing the text side of a hybrid index over spoken content
•Timestamped transcripts so a search result points at a second, not a filename
•Self-hosted pipelines where per-minute API pricing is the constraint

Use faster-whisper-large-v3 on Mixpeek

Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

Open Studio

How It Runs on Mixpeek

On Mixpeek, faster-whisper-large-v3 runs as a managed extractor inside a processing pipeline. Point a bucket of automatic speech recognition data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.