dsfsi-anv/za-african-next-voices
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How to use dsfsi-anv/whisper-small-anv-sot with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="dsfsi-anv/whisper-small-anv-sot") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("dsfsi-anv/whisper-small-anv-sot")
model = AutoModelForSpeechSeq2Seq.from_pretrained("dsfsi-anv/whisper-small-anv-sot")This model is a fine-tuned version of openai/whisper-small on the dsfsi-anv/za-african-next-voices 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 |
|---|---|---|---|---|
| 0.6639 | 0.25 | 250 | 0.6573 | 36.9818 |
| 0.4406 | 0.5 | 500 | 0.5333 | 29.8496 |
| 0.3337 | 0.75 | 750 | 0.4794 | 25.3273 |
| 0.409 | 1.231 | 1000 | 0.4410 | 24.8628 |
Base model
openai/whisper-small