google/fleurs
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How to use powervel/tam_whis with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="powervel/tam_whis") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("powervel/tam_whis")
model = AutoModelForSpeechSeq2Seq.from_pretrained("powervel/tam_whis")This model is a fine-tuned version of openai/whisper-small on the google/fleurs 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 |
|---|---|---|---|---|---|
| 0.05 | 4.2373 | 500 | 0.2260 | 36.7344 | 12.2676 |
Base model
openai/whisper-small