PolyAI/minds14
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How to use DevforMM/exo-5 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="DevforMM/exo-5") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("DevforMM/exo-5")
model = AutoModelForSpeechSeq2Seq.from_pretrained("DevforMM/exo-5")This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 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 |
|---|---|---|---|---|---|
| 0.0443 | 1.0 | 57 | 0.0811 | 0.1175 | 0.0680 |
| 0.0262 | 2.0 | 114 | 0.1065 | 0.1454 | 0.0977 |
| 0.0409 | 3.0 | 171 | 0.1275 | 0.1074 | 0.0748 |
| 0.0204 | 4.0 | 228 | 0.1301 | 0.1371 | 0.1057 |
| 0.0095 | 5.0 | 285 | 0.1293 | 0.1982 | 0.1605 |
| 0.0071 | 6.0 | 342 | 0.1422 | 0.1822 | 0.1365 |
| 0.0011 | 7.0 | 399 | 0.1329 | 0.1448 | 0.0982 |
| 0.0049 | 8.0 | 456 | 0.1294 | 0.1359 | 0.0788 |
| 0.0012 | 9.0 | 513 | 0.1296 | 0.1478 | 0.0891 |
| 0.0002 | 10.0 | 570 | 0.1305 | 0.1484 | 0.0891 |
| 0.0013 | 11.0 | 627 | 0.1298 | 0.1490 | 0.0897 |
| 0.0002 | 12.0 | 684 | 0.1309 | 0.1490 | 0.0891 |
| 0.0004 | 13.0 | 741 | 0.1311 | 0.1513 | 0.0914 |
| 0.0001 | 14.0 | 798 | 0.1313 | 0.1507 | 0.0908 |
| 0.0001 | 15.0 | 855 | 0.1314 | 0.1507 | 0.0908 |
Base model
openai/whisper-tiny