Automatic Speech Recognition
Transformers
Safetensors
voxtral
feature-extraction
speech
speech-language-model
target-speaker-asr
multi-talker
speaker-diarization
meeting-transcription
Dixtral
Voxtral
DiCoW
BUT-FIT
custom_code
Instructions to use BUT-FIT/Dixtral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/Dixtral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/Dixtral", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("BUT-FIT/Dixtral", trust_remote_code=True) model = AutoModel.from_pretrained("BUT-FIT/Dixtral", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42901e5ebea33b91fdfe4e6fe1c6c23e80ff8e061e478aa04bba10addfa8e9a4
- Size of remote file:
- 14.9 MB
- SHA256:
- 4aaf3836c2a5332f029ce85a7a62255c966f47b6797ef81dedd0ade9c862e4a8
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