DigitalUmuganda/Afrivoice_legacy
Updated • 5 • 3
How to use KasuleTrevor/whisper-lingala-small-test with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="KasuleTrevor/whisper-lingala-small-test") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("KasuleTrevor/whisper-lingala-small-test")
model = AutoModelForSpeechSeq2Seq.from_pretrained("KasuleTrevor/whisper-lingala-small-test")This model is a fine-tuned version of openai/whisper-small on the AfriVoice 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 |
|---|---|---|---|---|
| 0.0002 | 153.8462 | 1000 | 1.4799 | 52.3072 |
| 0.0001 | 307.6923 | 2000 | 1.5528 | 52.1298 |
| 0.0 | 461.5385 | 3000 | 1.6058 | 52.3033 |
| 0.0 | 615.3846 | 4000 | 1.6294 | 52.8546 |
| 0.0 | 769.2308 | 5000 | 1.6526 | 53.5292 |
Base model
openai/whisper-small