legacy-datasets/common_voice
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How to use Monsia/test-model-lg-data with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Monsia/test-model-lg-data") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Monsia/test-model-lg-data")
model = AutoModelForCTC.from_pretrained("Monsia/test-model-lg-data")This model is a fine-tuned version of Monsia/test-model-lg-data 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 |
|---|---|---|---|---|
| 0.0236 | 0.67 | 100 | 0.4048 | 0.4222 |
| 0.0304 | 1.35 | 200 | 0.4266 | 0.4809 |
| 0.0545 | 2.03 | 300 | 0.4309 | 0.4735 |
| 0.0415 | 2.7 | 400 | 0.4269 | 0.4595 |
| 0.033 | 3.38 | 500 | 0.4085 | 0.4537 |
| 0.0328 | 4.05 | 600 | 0.3642 | 0.4224 |
| 0.0414 | 4.73 | 700 | 0.3354 | 0.4150 |