Third-party cyber evaluations involving OpenAI models
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AI Research
arXiv CS.AI·Jun 9·5 min read
End-to-End Training for Discrete Token LLM based TTS System
TL;DR
Traditional text-to-speech (TTS) systems use a separate pipeline for components like speech tokenization and language modeling, leading to inefficiencies. An end-to-end (E2E) training framework was developed to jointly optimize the speech tokenizer, large language model (LLM), and flow-matching model, enhancing their integration.
✦ Why It Matters
Engineers can leverage this E2E framework to improve TTS system efficiency and accuracy significantly.
Key Takeaways
- Implement joint training for TTS components to enhance performance.
- Utilize multi-task objectives for better tokenizer optimization.
- Reduce inference-time mismatches by optimizing LLM with downstream tasks.
- Aim for state-of-the-art benchmarks in TTS systems.
How It Works
The proposed E2E framework integrates the training of multiple components—speech tokenizer, LLM, FM model, and RM—using a unified optimization approach. By employing multi-task objectives, the tokenizer learns to represent speech tokens that are both acoustically and semantically relevant.
This joint training process ensures that all components are aligned, leading to improved performance during inference.