clariai-audio-quality-v1
Model Description
ClariAI Audio Quality Analysis Model v1.0 - Production Ready
Model Type
audio-quality-analysis
ClariAI Integration
This model is part of the ClariAI ecosystem for professional audio quality analysis.
Features
- Advanced Audio Processing: Professional-grade feature extraction
- LangChain Integration: Intelligent quality analysis
- Real-time Analysis: Lightning-fast inference
- Multiple ML Algorithms: Support for various classification methods
- Hugging Face Integration: Seamless deployment and sharing
Usage
Basic Usage
from audio_call_quality_model import ClariAIAnalyzer
# Initialize ClariAI analyzer
analyzer = ClariAIAnalyzer()
# Analyze audio quality
results = analyzer.analyze_call_quality("path/to/audio.wav")
# Print results
print(f"Overall Quality: {results['quality_scores']['overall_quality']:.3f}")
print(f"Clarity: {results['quality_scores']['clarity']:.3f}")
print(f"Volume: {results['quality_scores']['volume']:.3f}")
print(f"Noise Level: {results['quality_scores']['noise_level']:.3f}")
Training Custom Model
from training_pipeline import ClariAITrainer
# Initialize trainer
trainer = ClariAITrainer()
# Add training samples
trainer.add_training_sample("audio1.wav", "excellent")
trainer.add_training_sample("audio2.wav", "good")
trainer.add_training_sample("audio3.wav", "poor")
# Train model
metrics = trainer.train_model("random_forest")
# Save model
trainer.save_model("my_quality_model")
Model Performance
Installation
pip install clariai
Requirements
- Python 3.8+
- librosa
- soundfile
- scikit-learn
- langchain-community
- transformers
License
MIT License - See LICENSE file for details.
Citation
@software{clariai_audio_quality,
title={ClariAI: Professional Audio Quality Analysis Platform},
author={ClariAI Team},
year={2024},
url={https://github.com/kernelseed/audio-call-quality-analyzer}
}
Contact
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