legacy-datasets/common_voice
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How to use azuur/wav2vec2-base-gn-demo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="azuur/wav2vec2-base-gn-demo") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("azuur/wav2vec2-base-gn-demo")
model = AutoModelForCTC.from_pretrained("azuur/wav2vec2-base-gn-demo")This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m 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 |
|---|---|---|---|---|
| No log | 4.0 | 100 | 0.7045 | 0.7409 |
| No log | 8.0 | 200 | 0.7200 | 0.75 |
| No log | 12.0 | 300 | 0.7400 | 0.7439 |
| No log | 16.0 | 400 | 0.7677 | 0.7515 |
| 0.0846 | 20.0 | 500 | 0.7765 | 0.7271 |
| 0.0846 | 24.0 | 600 | 0.7821 | 0.7287 |
| 0.0846 | 28.0 | 700 | 0.7671 | 0.7180 |
| 0.0846 | 32.0 | 800 | 0.7594 | 0.7180 |
| 0.0846 | 36.0 | 900 | 0.7500 | 0.7165 |
| 0.0713 | 40.0 | 1000 | 0.7351 | 0.7287 |
| 0.0713 | 44.0 | 1100 | 0.7361 | 0.7241 |
| 0.0713 | 48.0 | 1200 | 0.7389 | 0.7378 |
| 0.0713 | 52.0 | 1300 | 0.7424 | 0.7210 |
| 0.0713 | 56.0 | 1400 | 0.7425 | 0.7256 |
| 0.0669 | 60.0 | 1500 | 0.7426 | 0.7256 |