Parakeet is not really more accurate than Whisper, but it's much faster - faster than realtime even on CPU: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 . You have to use Nemo though, or mess around with third-party conversions. (Also has a big brother Canary: https://huggingface.co/nvidia/canary-1b-v2. There's also the confusingly named/positioned Nemotron speech: https://huggingface.co/nvidia/nemotron-speech-streaming-en-0...)

from the other day https://github.com/cjpais/Handy

And OS's. Mac has some decent models, but kokoro is much better. Even this one is better.

Gabriel from Kyutai here, we do support outputting wav to stdout. We don't support reading text from stdin but that should be easy enough. Feel free to drop a pull request!

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Gradium (https://gradium.ai/), a commercial company offshoot of Kyutai (open source lab), are focusing on emotion (both being able to recognise emotion and also understanding what emotion to use depending on context). I don't think any of their public existing models already does that, but they demoed it pretty impressively at the ai-Pulse conference.

Chatterbox does something like that. For example, if the input is

"so and so," he <verb>

and the verb is not just "said", but "chuckled", or "whispered", or "said shakily", the output is modified accordingly, or if there's an indication that it's a woman speaking it may pitch up during the quotation. It also tries to guess emotive content from textual content, such if a passage reads angry it may try to make it sound angry. That's more hit-and-miss, but when it hits, it hits really well. A very common failure case is, imagine someone is trying to psych themselves up and they say internally "come on, Steve, stand up and keep going", it'll read it in a deeper voice like it was being spoken by a WW2 sergeant to a soldier.

After pip install pocket-tts all dependencies are 7.4 GB. And it generates at 2x speed on CPu. Neat!