# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("Ezhilraj69999/wav2vec2-base-wakeword")
model = AutoModelForAudioClassification.from_pretrained("Ezhilraj69999/wav2vec2-base-wakeword", device_map="auto")Quick Links
wav2vec2-base-wakeword
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset. It achieves the following results on the evaluation set:
- Loss: 0.1988
- Accuracy: 0.8980
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: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5835 | 0.9832 | 44 | 0.4825 | 0.8305 |
| 0.3198 | 1.9888 | 89 | 0.3220 | 0.8681 |
| 0.2074 | 2.9944 | 134 | 0.2879 | 0.8571 |
| 0.164 | 4.0 | 179 | 0.2867 | 0.8454 |
| 0.1524 | 4.9832 | 223 | 0.2757 | 0.8414 |
| 0.1529 | 5.9888 | 268 | 0.3233 | 0.8273 |
| 0.1256 | 6.9944 | 313 | 0.2192 | 0.8666 |
| 0.1169 | 8.0 | 358 | 0.1988 | 0.8980 |
| 0.1128 | 8.9832 | 402 | 0.2188 | 0.8713 |
| 0.1252 | 9.8324 | 440 | 0.2259 | 0.8689 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for Ezhilraj69999/wav2vec2-base-wakeword
Base model
facebook/wav2vec2-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ezhilraj69999/wav2vec2-base-wakeword")