cahya-whisper-medium-onnx

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

pip install -r requirements.txt

Usage

Basic Example

from example import CahyaWhisperONNX

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

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

Command Line Usage

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

Advanced Usage

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

Benchmark Results

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

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

@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
• Base model: openai/whisper-medium