Instructions to use alirzb/S2_M1_R3_Wav2Vec_42738245 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alirzb/S2_M1_R3_Wav2Vec_42738245 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="alirzb/S2_M1_R3_Wav2Vec_42738245")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("alirzb/S2_M1_R3_Wav2Vec_42738245") model = AutoModelForAudioClassification.from_pretrained("alirzb/S2_M1_R3_Wav2Vec_42738245", device_map="auto") - Notebooks
- Google Colab
- Kaggle
S2_M1_R3_Wav2Vec_42738245
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0003
- 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0197 | 1.0 | 307 | 0.0089 | 0.9990 |
| 0.0027 | 2.0 | 614 | 0.0174 | 0.9971 |
| 0.0042 | 3.0 | 921 | 0.0004 | 1.0 |
| 0.0005 | 4.0 | 1229 | 0.0004 | 1.0 |
| 0.0005 | 5.0 | 1535 | 0.0003 | 1.0 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for alirzb/S2_M1_R3_Wav2Vec_42738245
Base model
facebook/wav2vec2-base