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_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/DiCoW_v3_3_large 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_large", 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_large", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from BUT-FIT/DiCoW_v3_3_large: direct link, hf CLI and curl.
- Browser
- Download file 3.93 MB
-
https://huggingface.co/BUT-FIT/DiCoW_v3_3_large/resolve/main/tokenizer.json
- Command line
-
hf download hf://BUT-FIT/DiCoW_v3_3_large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BUT-FIT/DiCoW_v3_3_large/resolve/main/tokenizer.json
3.93 MB
File too large to display, you can check the raw version instead.