Automatic Speech Recognition
Transformers
Safetensors
DiCoW
speech
whisper
multilingual
speaker-diarization
meeting-transcription
BUT-FIT
custom_code
Instructions to use BUT-FIT/DiCoW_v3_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/DiCoW_v3_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/DiCoW_v3_2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/DiCoW_v3_2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from BUT-FIT/DiCoW_v3_2: direct link, hf CLI and curl.
- Browser
- Download file 2.48 MB
-
https://huggingface.co/BUT-FIT/DiCoW_v3_2/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://BUT-FIT/DiCoW_v3_2@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BUT-FIT/DiCoW_v3_2/resolve/refs%2Fpr%2F2/tokenizer.json
2.48 MB
File too large to display, you can check the raw version instead.