Voice to text interface

Overview

This project aims to provide voice to text using faster-whisper as backend.

Origin

This project started as a vibe-coded experiment but this version is somewhat more hands on.

Setup

Setup venv for python

We will have two different setups here depending on if you want to build ctranslate2 locally or not. This shall be documented.

Model selection

Pass --model <name> to stt-server.py. Models are downloaded automatically from HuggingFace on first use.

Model	VRAM	Quality	Notes
base.en	~1 GB	Low	Default. Fast, but struggles with similar-sounding consonants (V/B/D).
small.en	~2 GB	Medium	Noticeable improvement over base for most speech.
medium.en	~5 GB	Good	Recommended starting point for production use.
large-v3	~10 GB	Best	Highest accuracy, use if VRAM allows.

English-only models (.en suffix) are faster and more accurate than multilingual models for English speech.

Compute type

Pass --compute-type <type> to control the numeric precision used during inference.

Type	Notes
int8_float16	Default. Good balance of speed and accuracy on modern GPUs.
float16	Slightly better accuracy, higher VRAM usage.
int8	CPU-friendly, lower quality.

If you see a CUDA error about mismatched library versions at startup, use setup-venv-local-build.sh to build ctranslate2 against your system CUDA version rather than using the PyPI wheel.