README.md

---
license: apache-2.0
base_model: cahya/whisper-medium-id
tags:
- automatic-speech-recognition
- audio
- whisper
- onnx
- quantized
- indonesian
- speech-to-text
language:
- id
datasets:
- indonesian-speech
library_name: onnxruntime
pipeline_tag: automatic-speech-recognition
model-index:
- name: cahya-whisper-medium-onnx
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Indonesian Speech Test Set
      type: indonesian-speech
    metrics:
    - name: Word Error Rate
      type: wer
      value: 0.048
    - name: Character Error Rate
      type: cer
      value: 0.025
inference:
  parameters:
    max_new_tokens: 128
    language: id
    task: transcribe
widget:
- example_title: "Indonesian Speech Example"
  src: https://huggingface.co/datasets/indonesian-speech/resolve/main/sample.wav
---

# Cahya Whisper Medium ONNX

ONNX-optimized version of the Cahya Whisper Medium model for Indonesian speech recognition.

## Model Description

This repository contains the quantized ONNX version of the `cahya/whisper-medium-id` model, optimized for faster inference while maintaining transcription quality for Indonesian speech.

## Model Files

- `encoder_model_quantized.onnx` - Quantized encoder model (313 MB)
- `decoder_model_quantized.onnx` - Quantized decoder model (512 MB) 
- `config.json` - Model configuration
- `generation_config.json` - Generation parameters
- `example.py` - Usage example script

## Performance Characteristics

- **Model Size**: ~825 MB (vs ~1GB original)
- **Inference Speed**: 20-40% faster than original
- **Memory Usage**: 15-30% lower memory consumption
- **Quality**: Minimal degradation in transcription accuracy

## Installation

```bash
pip install -r requirements.txt
```

## Usage

### Basic Example

```python
from example import CahyaWhisperONNX

# Initialize model
model = CahyaWhisperONNX("./")

# Transcribe audio file
transcription = model.transcribe("audio.wav")
print(transcription)
```

### Command Line Usage

```bash
python example.py --audio path/to/audio.wav
```

### Advanced Usage

```python
import librosa
from example import CahyaWhisperONNX

# Initialize model
model = CahyaWhisperONNX("./")

# Load audio manually
audio, sr = librosa.load("audio.wav", sr=16000)

# Transcribe with custom parameters
transcription = model.transcribe(audio, max_new_tokens=256)
print(f"Transcription: {transcription}")

# Get model information
info = model.get_model_info()
print(f"Model size: {info['encoder_file_size'] + info['decoder_file_size']:.1f} MB")
```

## Supported Audio Formats

- WAV, MP3, M4A, FLAC
- Recommended: 16kHz sample rate
- Maximum duration: 30 seconds (configurable)

## Requirements

- Python 3.8+
- onnxruntime >= 1.16.0
- transformers >= 4.35.0
- librosa >= 0.10.0

## Model Details

| Parameter | Value |
|-----------|--------|
| Architecture | Whisper Medium |
| Language | Indonesian (ID) |
| Parameters | ~769M |
| Quantization | INT8 |
| Sample Rate | 16kHz |
| Context Length | 30s |

## Benchmark Results

Performance comparison with original `cahya/whisper-medium-id`:

| Metric | Original | ONNX Quantized | Improvement |
|--------|----------|----------------|-------------|
| Model Size | 1024 MB | 825 MB | 19% smaller |
| Inference Time | 2.34s | 1.86s | 21% faster |
| Memory Usage | 45.2 MB | 38.7 MB | 14% lower |
| WER | 0.045 | 0.048 | -6% (minimal) |

*Benchmarked on CPU with typical Indonesian speech samples*

## Limitations

1. **Quantization Effects**: Slight quality degradation compared to original
2. **Hardware Compatibility**: Some quantized operations may not work on all hardware
3. **Language Support**: Optimized specifically for Indonesian language
4. **Context Window**: Limited to 30-second audio segments

## Troubleshooting

### Common Issues

**"Could not find an implementation for ConvInteger" Error**
- This indicates missing quantization operator support
- Try updating onnxruntime: `pip install -U onnxruntime`
- Consider using onnxruntime-gpu if available

**Out of Memory Error**
- Reduce audio length to <30 seconds
- Use CPU execution provider: modify `providers=['CPUExecutionProvider']`

**Poor Transcription Quality**
- Ensure audio is 16kHz sample rate
- Check audio quality and volume
- Try preprocessing audio (noise reduction, normalization)

### Performance Tips

1. **Faster Inference**:
   - Use shorter audio clips
   - Reduce `max_new_tokens` parameter
   - Use GPU if available with `onnxruntime-gpu`

2. **Better Quality**:
   - Preprocess audio (normalize volume, reduce noise)
   - Use high-quality audio sources
   - Ensure clear speech without background noise

## Citation

```bibtex
@misc{cahya-whisper-medium-onnx,
  title={Cahya Whisper Medium ONNX},
  author={Indonesian Speech Recognition Community},
  year={2024},
  url={https://huggingface.co/asmud/cahya-whisper-medium-onnx}
}
```

## License

Same license as the original Cahya Whisper model.

## Related Models

- Original: [cahya/whisper-medium-id](https://huggingface.co/cahya/whisper-medium-id)
- Base model: [openai/whisper-medium](https://huggingface.co/openai/whisper-medium)