# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("Thamer/wav2vec-fine_tuned-speech_command2")
model = AutoModelForAudioClassification.from_pretrained("Thamer/wav2vec-fine_tuned-speech_command2")Quick Links
wav2vec-fine_tuned-speech_command2
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: 0.1040
- Accuracy: 0.9735
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: 0.0003
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3874 | 1.0 | 50 | 0.9633 | 0.9229 |
| 0.5144 | 2.0 | 100 | 0.4398 | 0.9138 |
| 0.3538 | 3.0 | 150 | 0.1688 | 0.9651 |
| 0.2956 | 4.0 | 200 | 0.1622 | 0.9623 |
| 0.2662 | 5.0 | 250 | 0.1425 | 0.9665 |
| 0.2122 | 6.0 | 300 | 0.1301 | 0.9682 |
| 0.1948 | 7.0 | 350 | 0.1232 | 0.9693 |
| 0.1837 | 8.0 | 400 | 0.1116 | 0.9734 |
| 0.1631 | 9.0 | 450 | 0.1041 | 0.9734 |
| 0.1441 | 10.0 | 500 | 0.1040 | 0.9735 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Thamer/wav2vec-fine_tuned-speech_command2")