Automatic Speech Recognition
Transformers
Safetensors
voxtral
image-feature-extraction
speech
speech-language-model
target-speaker-asr
multi-talker
speaker-diarization
meeting-transcription
Dixtral
Voxtral
DiCoW
BUT-FIT
custom_code
Instructions to use BUT-FIT/Dixtral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/Dixtral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/Dixtral", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("BUT-FIT/Dixtral", trust_remote_code=True) model = AutoModel.from_pretrained("BUT-FIT/Dixtral", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 357 Bytes
f100721 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"chunk_length": 30,
"dither": 0.0,
"feature_extractor_type": "WhisperFeatureExtractor",
"feature_size": 128,
"hop_length": 160,
"n_fft": 400,
"n_samples": 480000,
"nb_max_frames": 3000,
"padding_side": "right",
"padding_value": 0.0,
"processor_class": "VoxtralProcessor",
"return_attention_mask": false,
"sampling_rate": 16000
}
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