Instructions to use GlowLED/wav2vec2-gtzan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GlowLED/wav2vec2-gtzan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="GlowLED/wav2vec2-gtzan")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("GlowLED/wav2vec2-gtzan") model = AutoModelForAudioClassification.from_pretrained("GlowLED/wav2vec2-gtzan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add model card
Browse files
README.md
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---
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library_name: transformers
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pipeline_tag: audio-classification
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tags:
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- audio-classification
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- gtzan
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- music
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- wav2vec2
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- fine-tuned
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---
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# wav2vec2-gtzan
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Fine-tuned `facebook/wav2vec2-base` for GTZAN music-genre classification (10 classes). Trained with 15s-window augmentation on `sanchit-gandhi/gtzan`; song-level accuracy 0.92 (window 0.8894). Use with `AutoModelForAudioClassification`.
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## Metrics
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- **accuracy**: 0.92
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## Usage
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```python
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# audio-classification
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```
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