legacy-datasets/common_voice
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How to use Foxasdf/ArabicSpeechToText with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Foxasdf/ArabicSpeechToText") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Foxasdf/ArabicSpeechToText")
model = AutoModelForCTC.from_pretrained("Foxasdf/ArabicSpeechToText")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 |
|---|---|---|---|---|
| 5.6589 | 0.52 | 700 | 0.8361 | 0.7598 |
| 0.7367 | 1.03 | 1400 | 0.5243 | 0.4771 |
| 0.479 | 1.55 | 2100 | 0.4025 | 0.3756 |
| 0.3742 | 2.06 | 2800 | 0.3651 | 0.3272 |
| 0.2846 | 2.58 | 3500 | 0.3207 | 0.2901 |