# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("Kibalama/Speech_Commands_Model")
model = AutoModelForAudioClassification.from_pretrained("Kibalama/Speech_Commands_Model", device_map="auto")Quick Links
Speech_Commands_Model
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: 0.1543
- Accuracy: 0.9755
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: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4629 | 1.0 | 320 | 0.2712 | 0.9683 |
| 0.3143 | 2.0 | 640 | 0.1806 | 0.9717 |
| 0.2891 | 3.0 | 960 | 0.1543 | 0.9755 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
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Model tree for Kibalama/Speech_Commands_Model
Base model
facebook/wav2vec2-baseDataset used to train Kibalama/Speech_Commands_Model
Evaluation results
- Accuracy on speech_commandsself-reported0.976
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Kibalama/Speech_Commands_Model")