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
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Download README.md from GlowLED/wav2vec2-gtzan: direct link, hf CLI and curl.
- Browser
- Download file 474 Bytes
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https://huggingface.co/GlowLED/wav2vec2-gtzan/resolve/main/README.md
- Command line
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hf download hf://GlowLED/wav2vec2-gtzan/README.md
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curl -L -o README.md https://huggingface.co/GlowLED/wav2vec2-gtzan/resolve/main/README.md
474 Bytes
| library_name: transformers | |
| pipeline_tag: audio-classification | |
| tags: | |
| - audio-classification | |
| - gtzan | |
| - music | |
| - wav2vec2 | |
| - fine-tuned | |
| # wav2vec2-gtzan | |
| 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`. | |
| ## Metrics | |
| - **accuracy**: 0.92 | |
| ## Usage | |
| ```python | |
| # audio-classification | |
| ``` |