EYEDOL/naija-voices-yoruba-split_0-4
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How to use EYEDOL/whisper-tiny-yoruba1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="EYEDOL/whisper-tiny-yoruba1") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("EYEDOL/whisper-tiny-yoruba1")
model = AutoModelForSpeechSeq2Seq.from_pretrained("EYEDOL/whisper-tiny-yoruba1")This model is a fine-tuned version of EYEDOL/whisper-tiny-yoruba on the EYEDOL/naija-voices-yoruba-split_0-4 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 Ortho | Wer |
|---|---|---|---|---|---|
| 1.5706 | 1.0 | 583 | 0.7699 | 0.8067 | 0.7173 |
| 1.4778 | 2.0 | 1166 | 0.7536 | 0.7432 | 0.6515 |
| 1.3621 | 3.0 | 1749 | 0.7465 | 0.7352 | 0.6440 |
| 1.2687 | 4.0 | 2332 | 0.7418 | 0.7670 | 0.6748 |
| 1.1844 | 5.0 | 2915 | 0.7442 | 0.7682 | 0.6766 |
| 1.1091 | 6.0 | 3498 | 0.7471 | 0.7690 | 0.6733 |
| 1.0363 | 7.0 | 4081 | 0.7512 | 0.7385 | 0.6481 |
| 0.9677 | 8.0 | 4664 | 0.7607 | 0.7673 | 0.6829 |
| 0.9016 | 9.0 | 5247 | 0.7728 | 0.7618 | 0.6768 |
| 0.8397 | 10.0 | 5830 | 0.7889 | 0.7395 | 0.6530 |
| 0.7784 | 11.0 | 6413 | 0.7975 | 0.7635 | 0.6761 |
| 0.7197 | 12.0 | 6996 | 0.8130 | 0.7657 | 0.6755 |
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