legacy-datasets/common_voice
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How to use rossevine/Check_Model_1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="rossevine/Check_Model_1") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("rossevine/Check_Model_1")
model = AutoModelForCTC.from_pretrained("rossevine/Check_Model_1")This model is a fine-tuned version of facebook/wav2vec2-large on the common_voice 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 |
|---|---|---|---|---|---|
| 2.1839 | 3.23 | 400 | 0.8796 | 0.7306 | 0.2332 |
| 0.6388 | 6.45 | 800 | 0.8702 | 0.6410 | 0.2200 |
| 0.4695 | 9.68 | 1200 | 0.7064 | 0.5360 | 0.1632 |
| 0.3659 | 12.9 | 1600 | 0.5814 | 0.5211 | 0.1662 |
| 0.285 | 16.13 | 2000 | 0.6394 | 0.5041 | 0.1663 |
| 0.2254 | 19.35 | 2400 | 0.5889 | 0.4428 | 0.1405 |
| 0.1801 | 22.58 | 2800 | 0.5712 | 0.4013 | 0.1182 |
| 0.1392 | 25.81 | 3200 | 0.5914 | 0.3934 | 0.1177 |
| 0.1051 | 29.03 | 3600 | 0.5522 | 0.3748 | 0.1158 |
Base model
facebook/wav2vec2-large