Automatic Speech Recognition
Transformers
Safetensors
voxtral
feature-extraction
speech
speech-language-model
question-answering
spoken-question-answering
speaker-diarization
meeting-transcription
Dixtral
Voxtral
DiCoW
BUT-FIT
custom_code
Instructions to use BUT-FIT/Dixtral_QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/Dixtral_QA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/Dixtral_QA", trust_remote_code=True, device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("BUT-FIT/Dixtral_QA", trust_remote_code=True) model = AutoModel.from_pretrained("BUT-FIT/Dixtral_QA", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- .gitattributes +1 -0
- preprocessor_config.json +15 -0
- tekken.json +3 -0
.gitattributes
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preprocessor_config.json
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{
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"chunk_length": 30,
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"dither": 0.0,
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"feature_extractor_type": "WhisperFeatureExtractor",
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"feature_size": 128,
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"hop_length": 160,
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"n_fft": 400,
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"n_samples": 480000,
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"nb_max_frames": 3000,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "VoxtralProcessor",
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"return_attention_mask": false,
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"sampling_rate": 16000
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}
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tekken.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:4aaf3836c2a5332f029ce85a7a62255c966f47b6797ef81dedd0ade9c862e4a8
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size 14894206
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