mozilla-foundation/common_voice_17_0
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How to use mdudek/whisper-tiny-dv with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="mdudek/whisper-tiny-dv") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("mdudek/whisper-tiny-dv")
model = AutoModelForSpeechSeq2Seq.from_pretrained("mdudek/whisper-tiny-dv")This model is a fine-tuned version of openai/whisper-tiny on the mozilla-foundation/common_voice_17_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 |
|---|---|---|---|---|---|
| 1.0239 | 0.4078 | 500 | 1.0369 | 129.9576 | 87.4794 |
| 0.912 | 0.8157 | 1000 | 0.9221 | 100.7646 | 72.9711 |
| 0.6815 | 1.2235 | 1500 | 0.7174 | 99.4926 | 62.4261 |
| 0.2529 | 1.6313 | 2000 | 0.3188 | 87.6972 | 34.2327 |
| 0.1906 | 2.0392 | 2500 | 0.2608 | 80.2808 | 24.9043 |
| 0.1666 | 2.4470 | 3000 | 0.2457 | 76.8054 | 21.4851 |
| 0.1511 | 2.8548 | 3500 | 0.2271 | 75.5265 | 21.5943 |
| 0.117 | 3.2626 | 4000 | 0.2334 | 75.2207 | 21.4903 |
Base model
openai/whisper-tiny