legacy-datasets/common_voice
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How to use Robinjmf/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="Robinjmf/wav2vec2-common_voice-tr-demo") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Robinjmf/wav2vec2-common_voice-tr-demo")
model = AutoModelForCTC.from_pretrained("Robinjmf/wav2vec2-common_voice-tr-demo")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 | 3.6061 | 1.0 |
| No log | 1.83 | 200 | 3.0203 | 0.9999 |
| No log | 2.75 | 300 | 0.9479 | 0.7916 |
| No log | 3.67 | 400 | 0.6024 | 0.6285 |
| 3.1561 | 4.59 | 500 | 0.5112 | 0.5369 |
| 3.1561 | 5.5 | 600 | 0.4581 | 0.4900 |
| 3.1561 | 6.42 | 700 | 0.4321 | 0.4633 |
| 3.1561 | 7.34 | 800 | 0.4252 | 0.4400 |
| 3.1561 | 8.26 | 900 | 0.4204 | 0.4229 |
| 0.2247 | 9.17 | 1000 | 0.3948 | 0.3971 |
| 0.2247 | 10.09 | 1100 | 0.3997 | 0.3963 |
| 0.2247 | 11.01 | 1200 | 0.4157 | 0.3894 |
| 0.2247 | 11.93 | 1300 | 0.4142 | 0.3855 |
| 0.2247 | 12.84 | 1400 | 0.4108 | 0.3638 |
| 0.1022 | 13.76 | 1500 | 0.3929 | 0.3618 |
| 0.1022 | 14.68 | 1600 | 0.4004 | 0.3544 |