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) - Notebooks
- Google Colab
- Kaggle
Auto language detection ?
Dear Cohere team,
I have tested this model and am really impressed with it. The WER is lower, and it can transcribe different languages and accents better than Whisper.
One thing I am wondering about: does this model support automatic language detection? I believe requiring a fixed language ID when integrating this model would significantly reduce its flexibility.
Hi - we didnt specifically train for auto language detection but we have noted your request to improve our future release.
While it doesnt work out of the box, you could hack it in some way. One method would be to let the model generate the language token by passing the partial input prompt and then later use that predicted language token to create the final prompt and use it again. The drawback is the increased latency due increased number of model call.