Instructions to use osman/whisper-small-ug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osman/whisper-small-ug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="osman/whisper-small-ug")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("osman/whisper-small-ug") model = AutoModelForSpeechSeq2Seq.from_pretrained("osman/whisper-small-ug") - Notebooks
- Google Colab
- Kaggle
Toturial of finetune?
Hello osman. Thank you for sharing the model. I take your suggestion and converted the text from uas to uls. I use the peft_bnb_whisper_large_v2_training for the finetune process. The training procese goes well, hovever the loss stuck at 0.7. I tried with small lr and warm_up step but does not help. The evaluation setp in the trainer keep giving error so I removed the evaluation during training. I tested the model after training and found the model only give a single "é
" with blank. I was wondering do you have any process for the text or audio except resample? Could you give a simple toturial? Thanks!
Hi, I have not faced such a problem. Have you used Uzbek tokeniser after the training?
Yes, I'm using Uzbek tokenizer and precessor. I found that your training goes down smooth to a quite small value(0.0073 at 4000 step). But mine stuck at 0.7. Dont know what's the problem.
