legacy-datasets/common_voice
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How to use ebonazza2910/model-1h with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="ebonazza2910/model-1h") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("ebonazza2910/model-1h")
model = AutoModelForCTC.from_pretrained("ebonazza2910/model-1h", 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 |
|---|---|---|---|---|
| 11.4106 | 1.24 | 10 | 7.1597 | 1.0 |
| 4.777 | 2.47 | 20 | 3.9782 | 1.0 |
| 3.6585 | 3.71 | 30 | 3.3961 | 1.0 |
| 3.3143 | 4.94 | 40 | 3.1481 | 1.0 |
| 3.3318 | 6.24 | 50 | 3.0596 | 1.0 |
| 3.1368 | 7.47 | 60 | 2.9751 | 1.0 |
| 3.1058 | 8.71 | 70 | 2.9510 | 1.0 |
| 3.0605 | 9.94 | 80 | 2.9479 | 1.0 |
| 3.2043 | 11.24 | 90 | 2.9270 | 1.0 |
| 3.0424 | 12.47 | 100 | 2.9349 | 1.0 |
| 3.0374 | 13.71 | 110 | 2.9316 | 1.0 |
| 3.0256 | 14.94 | 120 | 2.9165 | 1.0 |
| 3.1724 | 16.24 | 130 | 2.9076 | 1.0 |
| 3.0119 | 17.47 | 140 | 2.9034 | 1.0 |
| 2.9937 | 18.71 | 150 | 2.8812 | 1.0 |
| 2.9775 | 19.94 | 160 | 2.8674 | 1.0 |
| 3.0826 | 21.24 | 170 | 2.8147 | 1.0 |
| 2.8717 | 22.47 | 180 | 2.7212 | 1.0 |
| 2.7714 | 23.71 | 190 | 2.6149 | 0.9952 |
| 2.634 | 24.94 | 200 | 2.4611 | 0.9984 |
| 2.5637 | 26.24 | 210 | 2.2734 | 1.0 |
| 2.237 | 27.47 | 220 | 2.0705 | 1.0 |
| 2.0381 | 28.71 | 230 | 1.9216 | 1.0 |
| 1.8788 | 29.94 | 240 | 1.8317 | 1.0 |