Bark-Small
| Property | Value |
|---|---|
| License | MIT |
| Author | Suno |
| Languages Supported | 13 |
| Release Date | April 2023 |
What is bark-small?
Bark-small is a transformer-based text-to-audio model developed by Suno AI, representing a more compact version of the original Bark model. It's designed for generating highly realistic, multilingual speech, music, background noise, and sound effects. The model can handle 13 different languages and even produce nonverbal communications like laughing, sighing, and crying.
Implementation Details
The model architecture consists of three transformer models working in sequence: text to semantic tokens (80M parameters), semantic to coarse tokens, and coarse to fine tokens. It utilizes the BERT tokenizer for text input and the EnCodec Codec for audio processing.
- Text-to-semantic transformer with 80M parameters and causal attention
- Semantic-to-coarse transformer outputting two codebooks of 1,024 tokens each
- Coarse-to-fine transformer generating 6 additional codebooks
Core Capabilities
- Multilingual speech synthesis in 13 languages
- Generation of music and background sounds
- Production of nonverbal audio cues
- Support for both CPU and GPU deployment
- Integration with 🤗 Transformers library
Frequently Asked Questions
Q: What makes this model unique?
Bark-small offers a balanced compromise between performance and resource requirements, making it accessible for research and production environments while maintaining high-quality audio generation capabilities. Its ability to handle multiple languages and various audio types in a single model makes it versatile for different applications.
Q: What are the recommended use cases?
The model is primarily intended for research purposes and can be used for developing accessibility tools, content creation, and audio synthesis applications. However, users should be aware of potential dual-use implications and consider using the provided classifier to detect Bark-generated audio.
