Automatic Speech Recognition
Transformers
Safetensors
DiCoW
speech
whisper
multilingual
speaker-diarization
meeting-transcription
target-speaker-asr
BUT-FIT
custom_code
Instructions to use bohatey/DiCoW_v3_2_SF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bohatey/DiCoW_v3_2_SF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bohatey/DiCoW_v3_2_SF", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("bohatey/DiCoW_v3_2_SF", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from bohatey/DiCoW_v3_2_SF: direct link, hf CLI and curl.
- Browser
- Download file 3.93 MB
-
https://huggingface.co/bohatey/DiCoW_v3_2_SF/resolve/main/tokenizer.json
- Command line
-
hf download hf://bohatey/DiCoW_v3_2_SF/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/bohatey/DiCoW_v3_2_SF/resolve/main/tokenizer.json
3.93 MB
File too large to display, you can check the raw version instead.