asmud/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. 1.Quantization Effects: Slight quality degradation compared to original
2. 2.Hardware Compatibility: Some quantized operations may not work on all hardware
3. 3.Language Support: Optimized specifically for Indonesian language
4. 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. 1.Faster Inference:
2. 2.Use shorter audio clips
3. 3.Reduce max_new_tokens parameter
4. 4.Use GPU if available with onnxruntime-gpu

1. 1.Better Quality:
2. 2.Preprocess audio (normalize volume, reduce noise)
3. 3.Use high-quality audio sources
4. 4.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