Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("Zarakun/wav2vec")
model = AutoModelForAudioClassification.from_pretrained("Zarakun/wav2vec")Quick Links
wav2vec
This model is a fine-tuned version of facebook/wav2vec2-base on the speech_commands dataset. It achieves the following results on the evaluation set:
- Loss: 0.4992
- Accuracy: 0.8939
- F1: 0.8872
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: 80
- eval_batch_size: 80
- 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 | F1 |
|---|---|---|---|---|---|
| 0.6895 | 1.0 | 639 | 0.7875 | 0.8773 | 0.7995 |
| 0.4171 | 2.0 | 1278 | 0.5445 | 0.8932 | 0.8675 |
| 0.2706 | 3.0 | 1917 | 0.4992 | 0.8939 | 0.8872 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for Zarakun/wav2vec
Base model
facebook/wav2vec2-baseDataset used to train Zarakun/wav2vec
Evaluation results
- Accuracy on speech_commandstest set self-reported0.894
- F1 on speech_commandstest set self-reported0.887
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Zarakun/wav2vec")