Challenges in Current Text-to-Speech Systems
Current Text-to-Speech (TTS) systems, like VALL-E and Fastspeech, struggle with:
- Complex Linguistic Features: Difficulty in processing intricate language elements.
- Polyphonic Expressions: Challenges in managing words that sound alike but have different meanings.
- Natural Multilingual Speech: Producing realistic speech in multiple languages.
These issues affect applications like conversational AI and accessibility tools.
Introducing Fish Agent v0.1 3B
The Fish Audio Team has launched Fish Agent v0.1 3B, a solution that tackles these TTS challenges. Key features include:
- Dual Autoregressive Architecture: Combines Slow and Fast Transformers for better speech synthesis.
- Advanced Vocoder: Uses Firefly-GAN for high-quality audio output.
- No G2P Conversion: Directly extracts linguistic features from text, improving efficiency and multilingual capabilities.
How Fish Agent Works
Fish Agent v0.1 3B uses a unique architecture:
- Slow Transformer: Manages overall language structure.
- Fast Transformer: Captures detailed sound features.
- Grouped Finite Scalar Vector Quantization: Enhances efficiency and reduces latency.
This design allows for superior performance in voice cloning and real-time applications.
Performance and Benefits
Fish Agent v0.1 3B addresses long-standing TTS issues:
- Simplified Synthesis: Better handling of complex language and mixed languages.
- Extensive Training: Trained on 720,000 hours of multilingual audio for high-quality output.
- Impressive Metrics: Achieves a Word Error Rate (WER) of 6.89% and low latency of 150ms.
These features make it a strong choice for advancing AI-driven speech technologies.
Get Involved
Explore more about Fish Agent v0.1 3B through our Paper, GitHub, and Hugging Face Model. Follow us on Twitter, join our Telegram Channel, and connect with our LinkedIn Group. If you enjoy our work, subscribe to our newsletter and join our 55k+ ML SubReddit.
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