legacy-datasets/common_voice
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How to use arampacha/wav2vec2-xls-r-1b-hy 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-hy") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("arampacha/wav2vec2-xls-r-1b-hy")
model = AutoModelForCTC.from_pretrained("arampacha/wav2vec2-xls-r-1b-hy")This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_4/ - NA dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.255 | 7.24 | 500 | 0.2978 | 0.4294 | 0.0758 |
| 1.0058 | 14.49 | 1000 | 0.1883 | 0.2838 | 0.0483 |
| 0.9371 | 21.73 | 1500 | 0.1813 | 0.2627 | 0.0457 |
| 0.8999 | 28.98 | 2000 | 0.1693 | 0.2373 | 0.0429 |
| 0.8814 | 36.23 | 2500 | 0.1760 | 0.2420 | 0.0435 |
| 0.8364 | 43.47 | 3000 | 0.1765 | 0.2416 | 0.0419 |
| 0.8019 | 50.72 | 3500 | 0.1758 | 0.2311 | 0.0398 |
| 0.7665 | 57.96 | 4000 | 0.1745 | 0.2240 | 0.0399 |
| 0.7376 | 65.22 | 4500 | 0.1717 | 0.2190 | 0.0385 |
| 0.716 | 72.46 | 5000 | 0.1700 | 0.2147 | 0.0382 |