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license: mit |
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# Audio Feature Extraction Models |
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This repository contains pre-trained models for audio feature extraction, specifically: |
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- **Key Detection:** Classifies the musical key of an audio track into relative key classes. |
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## Model Details |
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### Tempo Model |
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- **Model Type:** Custom CNN architecture for tempo classification. |
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- **Input:** Audio segments converted to Mel spectrograms followed by autocorrelation. |
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- **Output:** Predicts Beats Per Minute (BPM) in a range from [85, 170]. |
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### Key Detection Models |
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- **Key Class Model:** Classifies into 12 relative key classes. |
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- **Key Quality Model:** Determines if the key is Major or Minor. |
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- **Input:** Audio segments converted to Mel spectrograms. |
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- **Output:** |
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- Key Class: One of 12 key signatures. |
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- Key Quality: Binary classification (0 for Major, 1 for Minor). |
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## Usage |
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### Prerequisites |
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- Python 3.7+ |
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- PyTorch |
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- torchaudio |
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- transformers |
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### Loading Models |
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To use these models with Hugging Face's transformers library: |
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```python |
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from transformers import [AutoModelForAudioClassification](https://x.com/i/grok?text=AutoModelForAudioClassification) |
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# Load Tempo Model |
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tempo_model = AutoModelForAudioClassification.from_pretrained("your_username/tempo_model") |
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# Load Key Models |
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key_class_model = AutoModelForAudioClassification.from_pretrained("your_username/key_class_model") |
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key_quality_model = AutoModelForAudioClassification.from_pretrained("your_username/key_quality_model") |
