Automatic Speech Recognition
Transformers
Safetensors
DiCoW
speech
whisper
multilingual
speaker-diarization
meeting-transcription
target-speaker-asr
BUT-FIT
custom_code
Instructions to use BUT-FIT/DiCoW_v3_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/DiCoW_v3_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/DiCoW_v3_3", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/DiCoW_v3_3", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Link model to SE-DiCoW paper and update metadata
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Linking the latest paper associated with this work: SE-DiCoW: Self-Enrolled Diarization-Conditioned Whisper, which describes the stabilization and enhancements implemented in version 3.3.
- Adding
base_model: openai/whisper-large-v3-turboto the metadata to improve discoverability and correctly attribute the architecture. - Updating the citation section to include the SE-DiCoW paper.
LGTM, merging, thanks!
Lakoc changed pull request status to merged