legacy-datasets/common_voice
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How to use jiobiala24/wav2vec2-base-checkpoint-1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="jiobiala24/wav2vec2-base-checkpoint-1") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("jiobiala24/wav2vec2-base-checkpoint-1")
model = AutoModelForCTC.from_pretrained("jiobiala24/wav2vec2-base-checkpoint-1")This model is a fine-tuned version of facebook/wav2vec2-base 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 |
|---|---|---|---|---|
| 3.1017 | 8.88 | 400 | 1.4635 | 0.7084 |
| 0.436 | 17.77 | 800 | 1.4765 | 0.6231 |
| 0.1339 | 26.66 | 1200 | 1.6987 | 0.6019 |