Automatic Speech Recognition
Transformers
Safetensors
DiCoW
speech
whisper
multilingual
speaker-diarization
meeting-transcription
target-speaker-asr
SE-DiCoW
BUT-FIT
custom_code
Instructions to use BUT-FIT/SE_DiCoW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/SE_DiCoW with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/SE_DiCoW", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/SE_DiCoW", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 119ad317bf6c5174b3e3e125b925a2eb90ce18afccab29da9e5240f4adbc95c4
- Size of remote file:
- 191 kB
- SHA256:
- 543d98b6fb0ef59c3c66398e046d78c0a831865a05123afab537ea400d640ec2
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