octava/InaVoCript-1.6
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How to use octava/combined-augmentation-strategy with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="octava/combined-augmentation-strategy") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("octava/combined-augmentation-strategy")
model = AutoModelForSpeechSeq2Seq.from_pretrained("octava/combined-augmentation-strategy")This model is a fine-tuned version of openai/whisper-small on the Extracted Youtube with self developed dataset 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 |
|---|---|---|---|---|
| 0.0493 | 1.6447 | 1000 | 0.2928 | 15.9091 |
| 0.0021 | 3.2895 | 2000 | 0.3111 | 14.3506 |
| 0.0008 | 4.9342 | 3000 | 0.3206 | 14.8052 |
Base model
openai/whisper-small