Orpheus

Serving Voice AI at $1/hr: Open-source, LoRAs, Latency, Load Balancing - Neil Dwyer, Gabber

Jul 31, 2025 · 16:09

Neil Dwyer, CTO of Gabber, details how his startup serves real-time voice AI for under $1 per hour using open-source Orpheus TTS, LoRAs for emotive voice cloning, and vLLM with FP8 dynamic quantization to achieve 95-105 tokens per second on L40S GPUs. He explains the critical latency challenge of 'head of line silence' in Orpheus (600ms in default voices) and how fine-tuning LoRAs reduces it to ~100ms, fitting within a 1.5-second budget for real-time conversation. Gabber batches multiple LoRA generations per GPU and uses a consistent hash ring for load balancing across servers, enabling popular clones to be dynamically replicated. The talk argues that with current open-source tools, building affordable consumer voice AI is accessible to small teams.

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.