bond005/sberdevices_golos_10h_crowd
Viewer • Updated • 18.8k • 612 • 6
How to use Val123val/my_model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Val123val/my_model") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Val123val/my_model")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Val123val/my_model")This model is a fine-tuned version of openai/whisper-small on the Sberdevices_golos_10h_crowd dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1521 | 0.91 | 500 | 0.1824 | 29.3408 |
| 0.0824 | 1.82 | 1000 | 0.1702 | 27.5291 |
| 0.0304 | 2.73 | 1500 | 0.1726 | 45.1046 |
| 0.0114 | 3.64 | 2000 | 0.1704 | 40.1238 |
| 0.0039 | 4.55 | 2500 | 0.1692 | 32.1903 |
| 0.0013 | 5.45 | 3000 | 0.1704 | 34.0298 |
| 0.0029 | 6.36 | 3500 | 0.1712 | 39.8976 |
| 0.0007 | 7.27 | 4000 | 0.1738 | 39.4273 |
| 0.0006 | 8.18 | 4500 | 0.1755 | 41.0664 |
| 0.0005 | 9.09 | 5000 | 0.1761 | 42.2411 |
Base model
openai/whisper-small