Instructions to use assoni2002/trained_model_with_zscaler_TTS_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use assoni2002/trained_model_with_zscaler_TTS_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="assoni2002/trained_model_with_zscaler_TTS_data")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("assoni2002/trained_model_with_zscaler_TTS_data") model = AutoModelForAudioClassification.from_pretrained("assoni2002/trained_model_with_zscaler_TTS_data") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: training_zscaler_dataset | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # training_zscaler_dataset | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5501 | |
| - Accuracy: 0.7355 | |
| ## 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: 1e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - optimizer: Use 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: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.6553 | 1.0 | 10 | 0.7152 | 0.4416 | | |
| | 0.6279 | 2.0 | 20 | 0.5893 | 0.7532 | | |
| | 0.5993 | 3.0 | 30 | 0.5791 | 0.7532 | | |
| | 0.5761 | 4.0 | 40 | 0.5538 | 0.7727 | | |
| | 0.5483 | 5.0 | 50 | 0.5169 | 0.8052 | | |
| | 0.5291 | 6.0 | 60 | 0.5496 | 0.7662 | | |
| | 0.5016 | 7.0 | 70 | 0.6360 | 0.6883 | | |
| | 0.4962 | 8.0 | 80 | 0.4710 | 0.8312 | | |
| | 0.4807 | 9.0 | 90 | 0.5224 | 0.7987 | | |
| | 0.4798 | 10.0 | 100 | 0.4764 | 0.8182 | | |
| ### Framework versions | |
| - Transformers 4.53.3 | |
| - Pytorch 2.7.1+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.21.2 | |