google/fleurs
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How to use Clement33/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Clement33/whisper-small-dv") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Clement33/whisper-small-dv")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Clement33/whisper-small-dv")This model is a fine-tuned version of openai/whisper-small on the fleurs_voice 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 |
|---|---|---|---|---|---|
| 0.1018 | 3.4722 | 500 | 0.6240 | 32.5941 | 31.4514 |
Base model
openai/whisper-small