Music Genres Classification Model
| Property | Value |
|---|---|
| Parameter Count | 94.6M |
| Model Type | Audio Classification |
| Base Architecture | facebook/wav2vec2-base-960h |
| License | Apache-2.0 |
| Tensor Type | F32 |
What is music_genres_classification?
This is a sophisticated audio classification model designed to identify music genres across 10 different categories. Built on the wav2vec2 architecture, it leverages transfer learning from Facebook's pre-trained model to achieve accurate genre classification. The model was trained on the GTZAN Dataset, comprising 1000 30-second audio samples equally distributed across genres including blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, and rock.
Implementation Details
The model utilizes PyTorch and Transformers frameworks, implementing the wav2vec2-base-960h architecture with 94.6M parameters. It processes audio inputs and classifies them into predefined genre categories using advanced audio feature extraction techniques.
- Built on wav2vec2 architecture for robust audio processing
- Trained on balanced dataset of 1000 audio samples
- Supports 10 distinct music genres
- Uses F32 tensor type for computations
Core Capabilities
- Music genre classification from audio samples
- Content organization and discovery
- Audio identification and metadata enrichment
- Music recommendation system integration
- Copyright management and licensing support
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of wav2vec2's pre-trained audio understanding with specific music genre classification capabilities, making it particularly effective for real-world applications in music analysis and content organization.
Q: What are the recommended use cases?
The model is ideal for music streaming platforms, content organization systems, radio broadcasting, healthcare applications, and research purposes. It can be integrated into recommendation systems, content tagging solutions, and music analysis tools.
