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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/DiCoW_v3_3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Link model to SE-DiCoW paper and update metadata (#1)
Verified signature- Link model to SE-DiCoW paper and update metadata (139de11368a54c8b2a414ded96b111f37fddad53) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>c34b64d
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