legacy-datasets/common_voice
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How to use Samiul/wav2vec2-large-xls-r-300m-turkish-colab with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Samiul/wav2vec2-large-xls-r-300m-turkish-colab") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Samiul/wav2vec2-large-xls-r-300m-turkish-colab")
model = AutoModelForCTC.from_pretrained("Samiul/wav2vec2-large-xls-r-300m-turkish-colab", 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 |
|---|---|---|---|---|
| 3.9162 | 3.67 | 400 | 0.6340 | 0.6360 |
| 0.4033 | 7.34 | 800 | 0.4588 | 0.4911 |
| 0.1919 | 11.01 | 1200 | 0.4392 | 0.4460 |
| 0.1315 | 14.68 | 1600 | 0.4269 | 0.4270 |
| 0.0963 | 18.35 | 2000 | 0.4327 | 0.3834 |
| 0.0801 | 22.02 | 2400 | 0.3867 | 0.3643 |
| 0.0631 | 25.69 | 2800 | 0.3854 | 0.3441 |
| 0.0492 | 29.36 | 3200 | 0.3821 | 0.3208 |