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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/DiCoW_v3_3 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", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/DiCoW_v3_3", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- a3b9156ef83382c05a370d94d4c90a81ebd1d1140b9056f051884225a5ab6ed6
- Size of remote file:
- 123 kB
- SHA256:
- 511159b854facd36c733ddcb6a1c79f465cac80c3ab5d381a182c4e5d7c42449
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