Those claiming AI training on copyrighted works is “theft” misunderstand key aspects of copyright law and AI technology. Copyright protects specific expressions of ideas, not the ideas themselves. When AI systems ingest copyrighted works, they’re extracting general patterns and concepts - the “Bob Dylan-ness” or “Hemingway-ness” - not copying specific text or images.

This process is akin to how humans learn by reading widely and absorbing styles and techniques, rather than memorizing and reproducing exact passages. The AI discards the original text, keeping only abstract representations in “vector space”. When generating new content, the AI isn’t recreating copyrighted works, but producing new expressions inspired by the concepts it’s learned.

This is fundamentally different from copying a book or song. It’s more like the long-standing artistic tradition of being influenced by others’ work. The law has always recognized that ideas themselves can’t be owned - only particular expressions of them.

Moreover, there’s precedent for this kind of use being considered “transformative” and thus fair use. The Google Books project, which scanned millions of books to create a searchable index, was ruled legal despite protests from authors and publishers. AI training is arguably even more transformative.

While it’s understandable that creators feel uneasy about this new technology, labeling it “theft” is both legally and technically inaccurate. We may need new ways to support and compensate creators in the AI age, but that doesn’t make the current use of copyrighted works for AI training illegal or unethical.

For those interested, this argument is nicely laid out by Damien Riehl in FLOSS Weekly episode 744. https://twit.tv/shows/floss-weekly/episodes/744

  • @QuadratureSurferEnglish
    1•2 months ago

    Ok, but the most important part of that research paper is published on the github repository, which explains how to provide audio data and text data to recreate any STT model in the same way that they have done.

    See the “Approach” section of the github repository: https://github.com/openai/whisper?tab=readme-ov-file#approach

    And the Traning Data section of their github: https://github.com/openai/whisper/blob/main/model-card.md#training-data

    With this you don’t really need to use the paper hosted on arxiv, you have enough information on how to train/modify the model.

    There are guides on how to Finetune the model yourself: https://huggingface.co/blog/fine-tune-whisper

    Which, from what I understand on the link to the OSAID, is exactly what they are asking for. The ability to retrain/finetune a model fits this definition very well:

    The preferred form of making modifications to a machine-learning system is:

    • Data information […]
    • Code […]
    • Weights […]

    All 3 of those have been provided.

    • @[email protected]English
      1•2 months ago

      From the approach section:

      A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.

      This is not sufficient data information to recreate the model.

      From the training data section:

      The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.

      This is also insufficient data information and links to the paper itself for that data information.

      Additionally, model cards =/= data cards. It’s an important distinction in AI training.

      There are guides on how to Finetune the model yourself: https://huggingface.co/blog/fine-tune-whisper

      Fine-tuning is not re-creating the model. This is an important distinction.

      The OSAID has a pretty simple checklist for the OSAID definition: https://opensource.org/deepdive/drafts/the-open-source-ai-definition-checklist-draft-v-0-0-9

      To go through the list of materials required to fit the OSAID:

      Datasets Available under OSD-compliant license

      Whisper does not provide the datasets.

      Research paper Available under OSD-compliant license

      The research paper is available, but does not fit an OSD-compliant license.

      Technical report Available under OSD-compliant license

      Whisper does not provide the technical report.

      Data card Available under OSD-compliant license

      Whisper provides the model card, but not the data card.