---
datasets:
- lewtun/music_genres_small
base_model:
- facebook/wav2vec2-large
metrics:
- accuracy
- f1
tags:
- audio
- music
- classification
- Wav2Vec2
---

# My Music Genre Classification Model 🎶
This model classifies music genres based on audio signals. It was fine-tuned on the `music_genres_small` dataset using the Wav2Vec2 architecture.

## Metrics
- **Validation Accuracy**: 75%
- **F1 Score**: 74%
- **Validation Loss**: 0.77

## Usage
```python
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
import torch

# Load the model and feature extractor
model = Wav2Vec2ForSequenceClassification.from_pretrained("username/repo-name")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("username/repo-name")

# Prepare input
audio = ... # Your audio array
inputs = feature_extractor(audio, sampling_rate=16000, return_tensors="pt")

# Make predictions
logits = model(**inputs).logits
predicted_class = torch.argmax(logits, dim=-1).item()
print(predicted_class)