legacy-datasets/common_voice
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How to use burakyldrm/wav2vec2-burak-v2.4-large with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="burakyldrm/wav2vec2-burak-v2.4-large") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("burakyldrm/wav2vec2-burak-v2.4-large")
model = AutoModelForCTC.from_pretrained("burakyldrm/wav2vec2-burak-v2.4-large", device_map="auto")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 |
|---|---|---|---|---|
| 4.8354 | 3.51 | 400 | 3.0971 | 1.0 |
| 1.4155 | 7.02 | 800 | 0.6770 | 0.6790 |
| 0.4583 | 10.52 | 1200 | 0.6025 | 0.5591 |
| 0.3014 | 14.03 | 1600 | 0.6563 | 0.5150 |
| 0.2231 | 17.54 | 2000 | 0.6393 | 0.4892 |
| 0.1738 | 21.05 | 2400 | 0.6653 | 0.4677 |
| 0.1451 | 24.56 | 2800 | 0.6167 | 0.4428 |
| 0.1277 | 28.07 | 3200 | 0.6419 | 0.4358 |