Instructions to use lalacelik/BirdClef-wav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lalacelik/BirdClef-wav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="lalacelik/BirdClef-wav2vec")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("lalacelik/BirdClef-wav2vec") model = AutoModelForAudioClassification.from_pretrained("lalacelik/BirdClef-wav2vec") - Notebooks
- Google Colab
- Kaggle
BirdClef-wav2vec
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the BirdCLEF24 dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.0204
- Loss: 4.6406
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: 5e-05
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 4.6298 | 1.0 | 8153 | 0.0204 | 4.6469 |
| 4.649 | 2.0 | 16306 | 0.0204 | 4.6439 |
| 4.6759 | 3.0 | 24459 | 0.0204 | 4.6406 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
- Downloads last month
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Model tree for lalacelik/BirdClef-wav2vec
Base model
facebook/wav2vec2-base-960h