4 ms·

This looks huge. Anyone know how this compares with Whisper in terms of quality and speed?

• crakenzak 3y ago
  according to their blog post[1], MMS achieves ~half the error rate on words, while supporting 11x more languages. pretty impressive. [1] https://ai.facebook.com/blog/multilingual-model-speech-recognition/ https://ai.facebook.com/blog/multilingual-model-speech-recog...
  • youssefabdelm 3y ago
    I wonder what the performance is on English specifically. Edit: Just checked the paper, it seems to be worse[1][2] but feel free to correct me. I feel like they should've just taken the Whipser architecture, scaled it, and scaled the dataset as they did. [1] Page: https://i.imgur.com/bq15Tno.png https://i.imgur.com/bq15Tno.png [2] Paper: https://scontent.fcai19-5.fna.fbcdn.net/v/t39.8562-6/348827959_6967534189927933_6819186233244071998_n.pdf?_nc_cat=104&ccb=1-7&_nc_sid=ad8a9d&_nc_ohc=wTyA0szEAwUAX8Nvucd&_nc_ht=scontent.fcai19-5.fna&oh=00_AfAaYE10KYHmvim-QAXCb8fLjsyqVFFLr-97CACfVAyU0Q&oe=64705542 https://scontent.fcai19-5.fna.fbcdn.net/v/t39.8562-6/3488279...
    • sacred_numbers 3y ago
      It's worse on English and a lot of other common languages (see Appendix C of the paper). It does better on less common languages like Latvian or Tajik, though.
      • hackernewds 3y ago
        Which implies, Whisper just hasn't focused on those languages? Seems disingenuous to make the claim that the error rate has halved, when it's worse in the apex language
    • whimsicalism 3y ago
      My guess is wav2vec performs better on low resource than whisper.
  • 93po 3y ago
    lack of labels on graph axes should be a crime