# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("annaces/wav2vec_birb_fintuned")
model = AutoModelForAudioClassification.from_pretrained("annaces/wav2vec_birb_fintuned")Quick Links
wav2vec_birb_fintuned
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.2675
- Accuracy: 1.0
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: 2
- 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 | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 9 | 4.7477 | 1.0 |
| 5.047 | 2.0 | 18 | 4.3604 | 1.0 |
| 4.6126 | 3.0 | 27 | 4.2675 | 1.0 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for annaces/wav2vec_birb_fintuned
Base model
facebook/wav2vec2-base-960h
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="annaces/wav2vec_birb_fintuned")