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
Update generation.py
Browse files- generation.py +3 -2
generation.py
CHANGED
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@@ -1197,8 +1197,9 @@ class DiCoWGenerationMixin(WhisperForConditionalGeneration):
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self.vad_seek_callback(kwargs["stno_mask"])
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if "is_valid" in kwargs:
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kwargs['is_valid'] = kwargs["is_valid"][batch_idx_map]
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return kwargs
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def generate_with_fallback(
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self.vad_seek_callback(kwargs["stno_mask"])
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if "is_valid" in kwargs:
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kwargs['is_valid'] = kwargs["is_valid"][batch_idx_map]
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+
if "labels" in kwargs:
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kwargs['labels'] = kwargs["labels"][batch_idx_map]
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kwargs['upp_labels'] = kwargs["upp_labels"][batch_idx_map]
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return kwargs
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def generate_with_fallback(
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