I was busy working on inference performance in the last few weeks and totally did not expect to land on Hackernews today. Only noticed it an hour ago because my GitHub stars jumped quite a bit
And the part of that which we use for WhisperSpeech is just the phonetic representation so our model is not able to recreate any of the Whisper training data in any way.
Is that less certain than the quote implies?
There may be a court ruling/new law that any training needs a special permission from the original author and then even a CC-BY license won't cover this.
Everyone using learned weights from other models, especially ones released by OpenAI, Stability and Google, such as text and audio encoders, is tainted by training materials that were not expressly licensed for the purpose of AI model training or unlimitedly licensed for any use.
Until courts or lawmakers make it clearer I personally believe non-generative models (Whisper, ResNet, DINOv2) should be legally trainable on publicly released data. Generative models (image or video generation, TTS, LLMs?) should be held to a much higher scrutiny since their outputs can potentially compete the creators who put a lot of creativity into their art. That not true for an ImageNet-trained classification model or Whisper ASR.
> "We are working only with properly licensed"
...versus:
> "fair use"
You're smart enough to know these are very different things - saying you believe you are protected by fair use, and claiming that the data is "properly licensed." In legal there is a colossal difference, you went from say you were authorized to use something to you believe you are unauthorized but still permitted due to fair use.
Our model is not a derivative of Whisper but we do use the transcripts (and encoder outputs) in our data preprocessing. I am convinced this data cannot be regarded as a derivative of the (proprietary) Whisper training set. Whisper itself is open-source (MIT) and has no special TOS (unlike, say, ChatGPT).
WhisperSpeech itself is trained on mostly Public Domain and some CC-BY-SA recordings. We are working on adding more data and we will provide clearer documentation on all the licenses.
The key here is that the Whisper multilingual ASR model has been trained on a huge amount of data, so its encoder output is a very good representation of the semantic content of speech. This can be used as an open-source, drop-in replacement for the semantic encoder in model architectures like SPEAR-TTS/VALL-E/etc (whose semantic encoders are not publicly available). This is then used to predict acoustic tokens (the output from the quantized/low-bandwidth Encodec audio codec) which is then upsampled/denoised/enhanced with the Vocos vocoder.
I know someone is working on Hindi but it would be great to see this extended to other languages for a properly open-source [1], multilingual TTS platform. I think the main bottleneck at the moment is finding people who can procure/clean compliant datasets.
[0] https://github.com/jpc [1] jpc/Collabora went to great efforts to ensure that they are only using properly licensed data to train this. I doubt Whisper itself was that compliant, so it's a bit muddy.