dsfsi-anv/za-african-next-voices
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How to use dsfsi-anv/whisper-large-v3-turbo-anv-zul with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="dsfsi-anv/whisper-large-v3-turbo-anv-zul") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("dsfsi-anv/whisper-large-v3-turbo-anv-zul")
model = AutoModelForSpeechSeq2Seq.from_pretrained("dsfsi-anv/whisper-large-v3-turbo-anv-zul")This model is a fine-tuned version of openai/whisper-large-v3-turbo 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 |
|---|---|---|---|---|
| 0.4397 | 0.2 | 200 | 0.4422 | 32.3634 |
| 0.3516 | 0.4 | 400 | 0.3710 | 27.0171 |
| 0.3005 | 1.135 | 600 | 0.3342 | 23.8024 |
| 0.2317 | 1.335 | 800 | 0.3125 | 22.9160 |
| 0.2232 | 2.07 | 1000 | 0.3028 | 21.7553 |
Base model
openai/whisper-large-v3