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Whispers2T

Whispers2T is an open-source speech-to-text pipeline that transcribes audio with Whisper models through multiple inference backends.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Whispers2T — 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 it when you need a locally installed Whisper transcription pipeline and want to select among the supported model backends or incorporate VAD into processing.

Who it is for

  • Developers building Python-based speech transcription workflows.
  • Teams processing multiple audio files or integrating custom voice activity detection models.

Documented uses

  • Transcribe audio files with a selected Whisper model backend.
  • Batch audio segments from multiple files and decode multiple languages or tasks.
  • Add custom voice activity detection models to a transcription workflow.
  • Export transcripts in TXT, JSON, TSV, SRT or VTT formats.
Before you choose

Requirements & limits

The project is open source under the MIT license. The README documents Python package installation and Docker usage.

  • The README lists the original OpenAI model, Hugging Face with FlashAttention 2, CTranslate2 and TensorRT-LLM backend options.
  • TensorRT-LLM requires installing TensorRT and TensorRT-LLM separately.
  • Custom VAD models are supported; multiple-language and task batching has best support with CTranslate2.
  • Dynamic variable-length batching is marked experimental and is limited to CTranslate2.
  • The README's speed comparisons use benchmarks without timestamps, and it notes timestamp settings can affect speed and word error rate.

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 WhisperS2T and its audio dependencies

Prepare a local Python environment with WhisperS2T and the documented audio packages.

Before you start

  • Python with Conda, or a system where the listed package installation commands can be used.

Steps

  1. Install FFmpeg and libsndfile1 on Ubuntu with apt-get, install FFmpeg on macOS with Homebrew, or install FFmpeg through Conda.

  2. Install the released package with pip install -U whisper-s2t, or install the latest repository commit using the Git URL shown in the README.

Expected outcome

The README's package and audio-dependency installation steps are complete.

Guide limitations

  • This guide covers installation only; it does not establish that a particular machine or environment is compatible.
  • TensorRT-LLM requires separate TensorRT and TensorRT-LLM installation.
Documentation 1 ↗
Source-based practical guide

Transcribe a WAV file with the CTranslate2 example

Use the README's Python example to transcribe an audio file with VAD.

Before you start

  • WhisperS2T and the documented audio dependencies installed.
  • An audio file available at a path supplied in the example.
  • A Whisper model identifier and language and task values.

Steps

  1. Import whisper_s2t and load the large-v2 model with the CTranslate2 backend.

  2. Set the audio-file path, language code, task and initial prompt values as shown in the example.

  3. Call transcribe_with_vad with those values and a batch_size of 32.

  4. Read the first returned utterance from the output.

Expected outcome

The example returns transcription text and associated fields such as start and end times for the shown utterance.

Guide limitations

  • The README example demonstrates a particular CTranslate2 workflow; it does not establish results for other environments or audio files.
  • The README says multiple-language/task batching has best support with CTranslate2.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Whispers2TTranscribe audio files with a selected Whisper model backend.The project is open source under the MIT license. The README documents Python package installation and Docker usage.
BluettsGenerate speech audio from text in a supported language.CLI, Linux, macOS, WindowsThe repository is public and identifies the project license as MIT.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
shashikg/WhisperS2T · Official homepage title · retained in its source language
How we review sourcesSuggest a correction

Related options

WhisperX — Named in the README as a comparison for reported transcription speed benchmarks.

Explore project →

Hugging Face Pipeline — Named in the README as a comparison for reported speed benchmarks.