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---
license: mit
language:
- vi
base_model:
- vinai/PhoWhisper-large
pipeline_tag: automatic-speech-recognition
---
# PhoWhisper-large-ct2
This repository contains the PhoWhisper-large model converted to use CTranslate2 for faster inference. This allows for significant performance improvements, especially on CPU.
## Usage
1. **Installation:**
Ensure you have the necessary libraries installed:
```bash
pip install transformers ctranslate2 faster-whisper
```
2. **Conversion (only needed once):**
This step converts the original Hugging Face model to the CTranslate2 format.
```bash
ct2-transformers-converter --model vinai/PhoWhisper-large --output_dir PhoWhisper-large-ct2 --copy_files tokenizer_config.json --quantization float16
```
3. **Transcription:**
```python
import os
from faster_whisper import WhisperModel
model_size = "kiendt/PhoWhisper-large-ct2"
# Run on GPU with FP16
#model = WhisperModel(model_size, device="cuda", compute_type="float16")
# or run on GPU with INT8
# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
model = WhisperModel(model_size, device="cpu", compute_type="int8")
segments, info = model.transcribe("audio.wav", beam_size=5) # Replace audio.wav with your audio file
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Model Details
* Based on the `vinai/PhoWhisper-large` model.
* Converted using `ct2-transformers-converter`.
* Optimized for faster inference with CTranslate2.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request.
## License
MIT |