Automatic Speech Recognition
Transformers
PyTorch
Abkhaz
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
Instructions to use mattchurgin/xls-r-eng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mattchurgin/xls-r-eng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mattchurgin/xls-r-eng")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mattchurgin/xls-r-eng") model = AutoModelForCTC.from_pretrained("mattchurgin/xls-r-eng", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import AutoModelForCTC, AutoProcessor | |
| from datasets import load_dataset | |
| import torch | |
| dummy_dataset = load_dataset("common_voice", "ab", split="test") | |
| model = AutoModelForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2") | |
| model.to("cuda") | |
| processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2") | |
| input_values = processor(dummy_dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=16_000).input_values | |
| input_values = input_values.to("cuda") | |
| logits = model(input_values).logits | |
| assert logits.shape[-1] == 32 |