How Much Voice Data Is Enough?

Daniel fine-tuned NVIDIA Parakeet V2 on two hours of LLM-generated sentences recorded on his phone, and Hebrew-English code-switching recognition jumped from roughly 20% to 60%. Now he wants to record fifteen minutes every morning for weeks — so the real question is where the fine-tuning curve flattens. The literature disagrees: a 2023 Singapore study on Mandarin-English code-switching suggests saturation can arrive as early as one to ten hours, while a Maltese study that trained thirty models found no diminishing returns for Whisper. A single-speaker Swedish study shows most of the gain captured by four hours, with a long shallow tail after. This episode digs into the contested research, what extra hours actually buy, why transcription noise may matter more than fresh clips, and whether a dataset built from LLM-written sentences is a diet version of how you really speak. Key sources: • NVIDIA Riva — https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-customizing.html • Rosehill — https://huggingface.co/blog/danielrosehill/whisper-hebrish • thewh1teagle/parakeet-tdt-0.6b-v3-he — https://huggingface.co/thewh1teagle/parakeet-tdt-0.6b-v3-he Episode 6023 — myweirdprompts.com/n/6023 (permanent short link: myweirdprompts.com/872707)