---
license: apache-2.0
language:
- fa
base_model:
- SadeghK/whisper-large-v3-turbo
tags:
- ASR
- Persian
- Farsi
---
# 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).

---

## 🚀 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.

---


## 🔬 Usage with WhisperX (ASR + alignment)
```python
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)

```

---

## 📁 Repository Structure

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