On: BASE TTS: The largest text-to-speech model to-date

[qwertox]

I'm using Whisper to transcribe notes I record with a lavalier mic during my bike rides (wind is no problem), but am using OpenAI's service. When it was released I tested it on a Ryzen 5950x and it was too slow and memory hungry for my taste. Using large was necessary for that use case (also, I'm recording in German).

[kkielhofner → qwertox]

The original release was full precision model weights running in an old version of PyTorch with no optimizations.

Fast forward to now and you have faster-whisper (using Ctranslate2) and distil-whisper optimized weights.

Between the two of them Whisper Large uses something like 1/8th the memory and is likely at least an order of magnitude faster on your hardware.

German has no effect on these metrics and for accuracy it actually has a lower word error rate than English.

[GaggiX → qwertox]

With Whisper, you can find many smaller models that are fine-tuned for a particular language, so even smaller models can perform adequately.