ivrit-ai/whisper-training
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How to use mike249/whisper-tiny-he-2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="mike249/whisper-tiny-he-2") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("mike249/whisper-tiny-he-2")
model = AutoModelForSpeechSeq2Seq.from_pretrained("mike249/whisper-tiny-he-2")This model is a fine-tuned version of openai/whisper-tiny on the ivrit-ai/whisper-training dataset.
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.973 | 0.13 | 500 | 0.8480 | 77.6213 |
| 0.9024 | 0.25 | 1000 | 0.7710 | 67.9838 |
| 0.8049 | 0.38 | 1500 | 0.7499 | 66.7384 |
| 0.7221 | 0.5 | 2000 | 0.7092 | 64.7953 |
| 0.7464 | 0.63 | 2500 | 0.6939 | 62.7543 |
| 0.7396 | 0.75 | 3000 | 0.6839 | 62.5261 |
| 0.7336 | 0.88 | 3500 | 0.6716 | 61.2350 |
| 0.6118 | 1.01 | 4000 | 0.6512 | 58.4637 |
| 0.6299 | 1.13 | 4500 | 0.6564 | 60.1721 |
| 0.6318 | 1.26 | 5000 | 0.6475 | 58.8550 |
| 0.6315 | 1.38 | 5500 | 0.6361 | 58.9724 |
| 0.6081 | 1.51 | 6000 | 0.6321 | 57.1596 |
| 0.6487 | 1.63 | 6500 | 0.6459 | 58.5616 |
| 0.6481 | 1.76 | 7000 | 0.6298 | 56.9379 |
| 0.5833 | 1.88 | 7500 | 0.6303 | 57.8965 |
| 0.5689 | 2.01 | 8000 | 0.6305 | 56.1750 |
| 0.5223 | 2.14 | 8500 | 0.6335 | 56.6967 |
| 0.574 | 2.26 | 9000 | 0.6248 | 55.3730 |
| 0.5841 | 2.39 | 9500 | 0.6320 | 55.6273 |
| 0.5533 | 2.51 | 10000 | 0.6254 | 55.8816 |
Base model
openai/whisper-tiny