mozilla-foundation/common_voice_13_0
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How to use Vickyee/whisper-small-ja with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Vickyee/whisper-small-ja") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Vickyee/whisper-small-ja")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Vickyee/whisper-small-ja")This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 - whisper_small on ASR_JA 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 |
|---|---|---|---|---|---|
| 0.1701 | 1.13 | 500 | 0.4424 | 480.6484 | 2104.7685 |
| 0.0606 | 2.26 | 1000 | 0.4455 | 238.1393 | 1241.4749 |
| 0.0334 | 3.39 | 1500 | 0.4821 | 496.1941 | 1038.0787 |
| 0.0167 | 4.52 | 2000 | 0.4851 | 614.7604 | 1104.6160 |
| 0.0113 | 5.66 | 2500 | 0.5100 | 265.9686 | 457.5270 |
| 0.007 | 6.79 | 3000 | 0.5387 | 338.3004 | 569.8087 |
| 0.0072 | 7.92 | 3500 | 0.5301 | 213.4515 | 597.5880 |
| 0.005 | 9.05 | 4000 | 0.5735 | 731.1720 | 622.0128 |
Base model
openai/whisper-small