legacy-datasets/common_voice
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How to use Jaewan/wav2vec2-common_voice-tr-demo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Jaewan/wav2vec2-common_voice-tr-demo") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Jaewan/wav2vec2-common_voice-tr-demo")
model = AutoModelForCTC.from_pretrained("Jaewan/wav2vec2-common_voice-tr-demo", device_map="auto")This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - TR 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 |
|---|---|---|---|---|
| No log | 0.92 | 100 | 3.5956 | 1.0 |
| No log | 1.83 | 200 | 3.0269 | 0.9999 |
| No log | 2.75 | 300 | 0.9827 | 0.8111 |
| No log | 3.67 | 400 | 0.6236 | 0.6304 |
| 3.1866 | 4.59 | 500 | 0.5016 | 0.5264 |
| 3.1866 | 5.5 | 600 | 0.4523 | 0.4935 |
| 3.1866 | 6.42 | 700 | 0.4306 | 0.4528 |
| 3.1866 | 7.34 | 800 | 0.4328 | 0.4329 |
| 3.1866 | 8.26 | 900 | 0.4026 | 0.4105 |
| 0.227 | 9.17 | 1000 | 0.4096 | 0.4080 |
| 0.227 | 10.09 | 1100 | 0.3921 | 0.3915 |
| 0.227 | 11.01 | 1200 | 0.3830 | 0.3778 |
| 0.227 | 11.93 | 1300 | 0.3846 | 0.3616 |
| 0.227 | 12.84 | 1400 | 0.3888 | 0.3619 |
| 0.1046 | 13.76 | 1500 | 0.3861 | 0.3509 |
| 0.1046 | 14.68 | 1600 | 0.3798 | 0.3455 |