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Audio & Voice AI

Faster Whisper

Faster Whisper is an MIT-licensed Python library that reimplements OpenAI Whisper transcription using the CTranslate2 inference engine. It processes audio into timestamped text segments and provides detected-language information.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Faster Whisper — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Faster Whisper when you need a Python-based Whisper transcription component and want to choose between CPU and NVIDIA GPU inference or use the documented batching pipeline.

Who it is for

  • Python developers adding speech-to-text to an application
  • Teams building audio transcription or batch-processing workflows
  • Developers implementing server-side transcription with Whisper models

Documented uses

  • Transcribe an audio file and retrieve text segments with timestamps
  • Inspect the detected language and its probability
  • Process audio with CPU or NVIDIA GPU inference
  • Use the documented batched transcription pipeline for larger batches
Before you choose

Requirements & limits

The public repository identifies the project as MIT-licensed and documents installation from PyPI with pip.

  • Python 3.9 or greater is required.
  • A separate system FFmpeg installation is not required; audio decoding uses PyAV.
  • GPU execution requires NVIDIA cuBLAS and cuDNN libraries, with documented version constraints.
  • The README reports up to four times faster inference than openai/whisper at the same accuracy, but benchmark results depend on the stated model and hardware setup.
  • Transcription output is yielded through a generator, so iterating over the segments is necessary to start processing.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Faster Whisper and transcribe an audio file

Set up the Python package and run the documented single-file transcription pattern.

Before you start

  • Python 3.9 or greater
  • An audio file, such as audio.mp3

Steps

  1. Install the package with the command below.

    Documented command
    pip install faster-whisper

  2. Import WhisperModel and initialize a model, for example WhisperModel("large-v3", device="cpu", compute_type="int8").

  3. Call model.transcribe("audio.mp3", beam_size=5).

  4. Iterate over the returned segments or collect them into a list to run transcription and access the segment text and timestamps.

Expected outcome

A sequence of timestamped transcription segments; the example also exposes detected-language information.

Guide limitations

  • Transcription begins when the returned segment generator is iterated.
  • GPU inference requires the specified NVIDIA libraries; CPU inference is also documented.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
SYSTRAN/faster-whisper · Official homepage title · retained in its source language
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Related options

openai/whisper — The project describes Faster Whisper as a CTranslate2-based reimplementation of Whisper and includes benchmark comparisons with openai/whisper.