Instructions to use drrobot9/wav2vec2-nigerian-lid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drrobot9/wav2vec2-nigerian-lid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="drrobot9/wav2vec2-nigerian-lid")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("drrobot9/wav2vec2-nigerian-lid") model = AutoModelForAudioClassification.from_pretrained("drrobot9/wav2vec2-nigerian-lid") - Notebooks
- Google Colab
- Kaggle
wav2vec2-nigerian-lid
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5562
- Accuracy: 0.6611
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1807 | 1.0 | 94 | 1.0534 | 0.52 |
| 1.6626 | 2.0 | 188 | 0.6934 | 0.6556 |
| 1.3793 | 3.0 | 282 | 0.5916 | 0.6544 |
| 1.3017 | 4.0 | 376 | 0.5613 | 0.6722 |
| 1.2201 | 5.0 | 470 | 0.5562 | 0.6611 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for drrobot9/wav2vec2-nigerian-lid
Base model
facebook/wav2vec2-large-xlsr-53