EYEDOL/naija-voices-hausa-split_0-0
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How to use EYEDOL/whisper-tiny-hausa with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="EYEDOL/whisper-tiny-hausa") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("EYEDOL/whisper-tiny-hausa")
model = AutoModelForSpeechSeq2Seq.from_pretrained("EYEDOL/whisper-tiny-hausa")This model is a fine-tuned version of openai/whisper-tiny on the EYEDOL/naija-voices-hausa-split_0-0 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 |
|---|---|---|---|---|---|
| 4.5185 | 1.0 | 665 | 1.2291 | 0.8407 | 0.7963 |
| 2.0880 | 2.0 | 1330 | 0.9774 | 0.7245 | 0.6717 |
| 1.6905 | 3.0 | 1995 | 0.8828 | 0.6599 | 0.6016 |
| 1.4668 | 4.0 | 2660 | 0.8334 | 0.6431 | 0.5829 |
| 1.3050 | 5.0 | 3325 | 0.7984 | 0.6149 | 0.5562 |
| 1.1746 | 6.0 | 3990 | 0.7819 | 0.6115 | 0.5516 |
| 1.0632 | 7.0 | 4655 | 0.7707 | 0.5996 | 0.5419 |
| 0.9630 | 8.0 | 5320 | 0.7678 | 0.5939 | 0.5360 |
| 0.8731 | 9.0 | 5985 | 0.7667 | 0.5963 | 0.5376 |
| 0.7893 | 10.0 | 6650 | 0.7752 | 0.5775 | 0.5222 |
Base model
openai/whisper-tiny