Yuriy Perezhohin PRO

Thanks! Just pushed the repo public: github.com/yuriyvnv/TTS-Augmented-ASR

This is the codebase behind a paper I wrote on Estonian and Slovenian, so you'll find the full pipeline there: not just the Parakeet fine-tuning scripts, but also the synthetic data generation (LLM text diversification + OpenAI TTS synthesis) that powers the augmentation. Everything was trained on a single NVIDIA H100.

One thing worth knowing for African languages:

Parakeet v3 is only pretrained on 25 languages, so you'd be doing cross-lingual transfer from scratch. The base won't recognize the language zero-shot, but fine-tuning still works — just expect a much rougher starting point than what you saw in my models.
Always evaluate zero-shot first. I had one language (Polish) where fine-tuning actually made things worse due to domain mismatch, or the learning rate was too low (still analyzing why this happened).
Standard recipe worked across everything I tried: AdamW, lr=5e-5, cosine annealing, 10% warmup, bf16, batch 32-64, early stopping on val_wer. The larger the batch size, especially for parakeet models, the better the gradient flow during training, since the model is compact.
Happy to help if you hit anything weird.

🔥 Hello Everyone, given the community's increased interest in the WAVe for the Portuguese Language, the team has retrained the model for over 100 epochs to further extend learning. The results are much better than those from the previous version with 30 epochs.
Key improvements:

The biggest win is the alignment gap nearly doubling -- the model is now much better at catching word-level errors like mispronunciations and timing artifacts. Corrupt pairs get
penalized harder (0.23 vs 0.31), so the filtering threshold becomes more reliable.

Same repo, same API, drop-in replacement:

model = AutoModel.from_pretrained("yuriyvnv/WAVe-1B-Multimodal-PT", trust_remote_code=True)

Updated README of the model card includes side-by-side training curves for both versions, check it out.

Hello everyone, yesterday there were minor problems that prevented the usage of the Embedding model. Mainly because of the Processor Class.
Posting here that the team has already solved the bugs.
If there is any problem with your usage, first delete the cached model (.cache folder in Hugging Face), redownload it, and if the issue persists, post a thread on the model page.