Automatic Speech Recognition
Transformers
Safetensors
DiCoW
speech
whisper
multilingual
fine-tuned
mlc-slm
speaker-diarization
meeting-transcription
BUT-FIT
custom_code
Instructions to use BUT-FIT/DiCoW_v3_MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/DiCoW_v3_MLC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/DiCoW_v3_MLC", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/DiCoW_v3_MLC", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag, license and link to paper and code
#1
by nielsr HF Staff - opened
This PR improves the model card by adding a pipeline_tag, which enables users to find the model at https://huggingface.co/models?pipeline_tag=automatic-speech-recognition. It also adds the license and a link to the paper and Github repo.
Jyhan003 changed pull request status to merged