legacy-datasets/common_voice
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How to use rossevine/wav2vec2_Indonesia_4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="rossevine/wav2vec2_Indonesia_4") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("rossevine/wav2vec2_Indonesia_4")
model = AutoModelForCTC.from_pretrained("rossevine/wav2vec2_Indonesia_4")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:
Model yang dilatih dengan data train common voice dan data test data perkuliahan
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.9949 | 3.23 | 400 | 1.3340 | 0.8916 |
| 0.4469 | 6.45 | 800 | 1.0507 | 0.6859 |
| 0.2003 | 9.68 | 1200 | 1.1115 | 0.6369 |
| 0.1432 | 12.9 | 1600 | 1.1307 | 0.6297 |
| 0.1138 | 16.13 | 2000 | 1.2157 | 0.6369 |
| 0.089 | 19.35 | 2400 | 1.2834 | 0.6058 |
| 0.0712 | 22.58 | 2800 | 1.3283 | 0.5947 |
| 0.057 | 25.81 | 3200 | 1.3345 | 0.5827 |
| 0.0467 | 29.03 | 3600 | 1.3147 | 0.5914 |