google/WaxalNLP
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How to use olaolugbenle/african-lid with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="olaolugbenle/african-lid") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("olaolugbenle/african-lid")
model = AutoModelForAudioClassification.from_pretrained("olaolugbenle/african-lid", device_map="auto")This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Google WAXAL 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 | Accuracy |
|---|---|---|---|---|
| 0.7744 | 1.0 | 2212 | 0.2831 | 0.9654 |
| 0.2572 | 2.0 | 4424 | 0.3328 | 0.9612 |
| 0.1595 | 3.0 | 6636 | 0.1538 | 0.9772 |
| 0.1158 | 4.0 | 8848 | 0.2646 | 0.9772 |
| 0.0932 | 5.0 | 11060 | 0.2279 | 0.9745 |
| 0.0755 | 6.0 | 13272 | 0.2802 | 0.9748 |
Base model
facebook/wav2vec2-xls-r-300m