NLTK

Text-to-Speech Data Preparation and Fine-tuning Workshop - Ronan McGovern

Jun 3, 2025 · 34:00

Ronan McGovern walks through fine-tuning Sesame's CSM-1B text-to-speech model on a specific voice, using a YouTube video as the data source. He explains token-based TTS models, including how audio is represented via codebooks and how CSM-1B uses a main transformer for zeroth tokens and a secondary transformer for 31 hierarchical tokens. The workshop covers data preparation: downloading audio with yt-dlp, transcribing with Whisper Turbo, manually correcting the transcript, and splitting audio into 30-second chunks (41 clips from a 30-minute video). Fine-tuning uses Unsloth with LoRA adapters (rank 32, alpha 16) on linear layers, training for one epoch with a batch size of 2 and virtual size of 8, reducing loss from ~6.34 to ~3.72. Evaluation compares zero-shot inference (random speaker), voice cloning (closer but imperfect), and fine-tuned plus cloning (best result, producing an Irish-accented voice with natural errors). McGovern recommends 50+ 30-second clips for noticeable effect and notes that combining fine-tuning with voice cloning yields good performance even with limited data.

Retrieval Augmented Generation in the Wild: Anton Troynikov

Nov 15, 2023 · 12:20

Anton Troynikov, co-founder of Chroma, explains that retrieval augmented generation (RAG) requires more than simple vector search—it needs human feedback, self-updating memory, and agent interaction to handle dynamic data. He covers challenges like choosing the right embedding model, chunking strategies (including using language model perplexity), and determining result relevance without distractors. Chroma is building a horizontally scalable cluster, a cloud technical preview by December, and support for multimodal data. The episode argues that a capable memory system is key to making AI agents truly functional, citing the Voyager paper where Chroma stored learned skills for Minecraft agents.