Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Ukrainian
wav2vec2
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_8_0
robust-speech-event
Eval Results (legacy)
Instructions to use arampacha/wav2vec2-xls-r-1b-uk-cv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arampacha/wav2vec2-xls-r-1b-uk-cv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arampacha/wav2vec2-xls-r-1b-uk-cv")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("arampacha/wav2vec2-xls-r-1b-uk-cv") model = AutoModelForCTC.from_pretrained("arampacha/wav2vec2-xls-r-1b-uk-cv") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "Wav2Vec2ProcessorWithLM", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |