---
license: mit
base_model:
- openai/whisper-large-v3-turbo
tags:
- whisper
- faster
- int8
- ct2
- turbo
---
# Whisper Large v3 Turbo - CTranslate2

This is a CTranslate2-optimized version of OpenAI's Whisper Large v3 Turbo model for automatic speech recognition (ASR).

## Model Description

This model is a converted version of the original Whisper Large v3 Turbo model, optimized for inference using CTranslate2. CTranslate2 is a C++ and Python library for efficient inference with Transformer models, providing:

- **Faster inference**: Optimized implementations of attention mechanisms and feed-forward networks
- **Lower memory usage**: Quantization support and memory-efficient attention
- **Better throughput**: Batching and parallel processing optimizations
- **Cross-platform compatibility**: Support for CPU and GPU inference

## Conversion

This model has been converted using the following command:

```bash
ct2-transformers-converter --model openai/whisper-large-v3-turbo --output_dir whisper-large-v3-turbo-ct2-int8 --quantization int8 --copy_files tokenizer.json preprocessor_config.json
```

The conversion includes **int8 quantization**, which provides several benefits:

- **Reduced disk space**: Significantly smaller model size compared to the original float32 version
- **Lower memory consumption**: Requires less RAM during inference
- **Maintained accuracy**: Minimal quality loss while providing substantial efficiency gains
- **Faster loading**: Reduced time to load the model from disk

## Original Model

This model is based on OpenAI's Whisper Large v3 Turbo, which is a state-of-the-art automatic speech recognition model that:

- Supports 99 languages
- Provides high-quality transcription and translation
- Features improved accuracy and speed compared to previous Whisper versions
- Handles various audio conditions and accents

## Usage

To use this model, you'll need to install CTranslate2 and the appropriate Whisper integration (faster-whisper):

```bash
pip install ctranslate2 faster-whisper
```

```python
from faster_whisper import WhisperModel

model_size = "path/to/whisper-large-v3-turbo-ct2"
model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio.wav", beam_size=5)

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```

## Performance

This CTranslate2 version provides significant performance improvements over the original PyTorch implementation:

- Up to 4x faster inference
- Reduced memory consumption
- Support for quantization
- Optimized for both CPU and GPU inference

## Supported Languages

Same as the original Whisper Large v3 Turbo:
Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Welsh.

## Model Card

- **Developed by**: OpenAI (original), converted to CT2 format
- **Model type**: Automatic Speech Recognition
- **Language(s)**: Multilingual (99 languages)
- **License**: MIT
- **Model size**: Large (1550M parameters)