Whisper Large V3 Turbo (CTranslate2) — Optimized for Faster-Whisper & WhisperX

This repository contains a CTranslate2 (CT2) optimized version of the whisper-large-v3-turbo model finetuned by SadeghK.
It is designed for high-speed inference, low-latency ASR, and full WhisperX compatibility (ASR + alignment + diarization).

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🚀 Model Overview

This is a converted version of the original SadeghK/whisper-large-v3-turbo model into CTranslate2 format, which enables:

• ✔ Faster inference (up to 4× vs PyTorch)
• ✔ Lower memory usage (supports float16 / int8 / int8_float16)
• ✔ Full compatibility with faster-whisper
• ✔ Full compatibility with WhisperX for:
  • ASR transcription
  • Word-level alignment
  • (optional) speaker diarization

All weights in this repository are ready-to-use, no additional conversion required.

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🔬 Usage with WhisperX (ASR + alignment)

import whisperx

device = "cuda"

# ASR
asr_model = whisperx.load_model(
    "SadeghK/whisper-large-v3-turbo-ct2",
    device=device,
    compute_type="float16"
)

result = asr_model.transcribe("audio.wav")

# Alignment (example for Persian)
align_model, metadata = whisperx.load_align_model("fa", device)
aligned = whisperx.align(result["segments"], align_model, metadata, "audio.wav", device)

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📁 Repository Structure

whisper-large-v3-turbo-ct2/
│
├── config.json
├── model.bin
├── preprocessor_config.json
├── tokenizer.json
└── vocabulary.json