legacy-datasets/common_voice
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How to use rossevine/Model_G_Wav2Vec2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_Wav2Vec2") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("rossevine/Model_G_Wav2Vec2")
model = AutoModelForCTC.from_pretrained("rossevine/Model_G_Wav2Vec2")# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("rossevine/Model_G_Wav2Vec2")
model = AutoModelForCTC.from_pretrained("rossevine/Model_G_Wav2Vec2")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 | Cer |
|---|---|---|---|---|---|
| 3.759 | 5.97 | 400 | 0.7276 | 0.7372 | 0.2083 |
| 0.3491 | 11.94 | 800 | 0.5168 | 0.4853 | 0.1312 |
| 0.1556 | 17.91 | 1200 | 0.4654 | 0.4210 | 0.1117 |
| 0.0981 | 23.88 | 1600 | 0.4576 | 0.3896 | 0.1020 |
| 0.065 | 29.85 | 2000 | 0.4452 | 0.3653 | 0.0942 |
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_Wav2Vec2")