PolyAI/minds14
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How to use michaelsh/whisper-tiny-minds-v5-numproc1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="michaelsh/whisper-tiny-minds-v5-numproc1") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("michaelsh/whisper-tiny-minds-v5-numproc1")
model = AutoModelForSpeechSeq2Seq.from_pretrained("michaelsh/whisper-tiny-minds-v5-numproc1")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 |
|---|---|---|---|---|---|
| 3.1128 | 1.0 | 29 | 1.5110 | 0.8028 | 0.7574 |
| 0.6583 | 2.0 | 58 | 0.5695 | 0.4347 | 0.4316 |
| 0.3271 | 3.0 | 87 | 0.5171 | 0.3945 | 0.3913 |
| 0.2003 | 4.0 | 116 | 0.5165 | 0.3912 | 0.3907 |
| 0.1189 | 5.0 | 145 | 0.5296 | 0.3819 | 0.3825 |
| 0.0623 | 6.0 | 174 | 0.5532 | 0.3747 | 0.3737 |
| 0.0326 | 7.0 | 203 | 0.5614 | 0.3865 | 0.3882 |
| 0.0149 | 8.0 | 232 | 0.6009 | 0.3628 | 0.3655 |
| 0.0093 | 9.0 | 261 | 0.6024 | 0.3707 | 0.3762 |
| 0.0038 | 10.0 | 290 | 0.6160 | 0.3635 | 0.3661 |
Base model
openai/whisper-tiny