Instructions to use CohereLabs/cohere-transcribe-03-2026 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CohereLabs/cohere-transcribe-03-2026 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CohereLabs/cohere-transcribe-03-2026", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CohereLabs/cohere-transcribe-03-2026", trust_remote_code=True) model = AutoModelForSpeechSeq2Seq.from_pretrained("CohereLabs/cohere-transcribe-03-2026", trust_remote_code=True, device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Fine-tuning for Punjabi (Gurmukhi). Looking for guidance
Congrats on the impressive release!
I'd like to fine-tune for Punjabi (Gurmukhi script), not in the 14 supported languages. I have:
~60K clean segments with verified transcriptions
~60 hours
~1 speaker
~ < 30 seconds each
I can expand this x 5 speakers
Questions:
Does adding a new language require vocabulary/tokenizer expansion, or can this be handled via fine-tuning alone?
Are there recommended data augmentation strategies to improve robustness beyond studio conditions?
Any published fine-tuning scripts, data augmentation strategies, or recommended hyperparameters?
Is Punjabi on your roadmap?
Happy to work with your team if there is guidance.
Ping, checking in for updated guidance / sample code