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)# 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) - Notebooks
- Google Colab
- Kaggle
| { | |
| "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 | |
| } | |