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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/DiCoW_v3_MLC", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
Verified signature99c64e8
-
Update generation_config.json
Verified signaturefe93b05 -
Update generation_config.json
Verified signature4afe3ab
-
Update README.md
Verified signature10d19dc -
Update README.md
Verified signature8f3666d -
Update README.md
Verified signatured3a6cd8 -
Update README.md
Verified signature989b69f -
Update README.md
Verified signaturebd2e2ab -
Add pipeline tag, license and link to paper and code (#1)
Verified signature- Add pipeline tag, license and link to paper and code (3d704a4b465de575962dd81daf40b293fa3226f3) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>5e4bd00
-
Update generation_config.json
Verified signatureaf2d51d -
Upload DiCoWForConditionalGeneration
Verified signature324bced -
Upload tokenizer
Verified signature2d133ab -
Upload feature extractor
Verified signature07aa3ed -
Upload DiCoWForConditionalGeneration
Verified signature702de8f -
initial commit
Verified signature35c11e8