mozilla-foundation/common_voice_13_0
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How to use Wishwa98/TESTASR_FOR_DV with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Wishwa98/TESTASR_FOR_DV") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Wishwa98/TESTASR_FOR_DV")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Wishwa98/TESTASR_FOR_DV")This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 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 |
|---|---|---|---|---|---|
| 0.1252 | 1.63 | 500 | 0.1689 | 62.8317 | 13.2907 |
Base model
openai/whisper-small