Instructions to use FadQ/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FadQ/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="FadQ/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("FadQ/results") model = AutoModelForAudioClassification.from_pretrained("FadQ/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from FadQ/results: direct link, hf CLI and curl.
- Browser
- Download file 2.47 kB
-
https://huggingface.co/FadQ/results/resolve/main/README.md
- Command line
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hf download hf://FadQ/results/README.md
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curl -L -o README.md https://huggingface.co/FadQ/results/resolve/main/README.md
2.47 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: r-f/wav2vec-english-speech-emotion-recognition | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: results | |
| 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. --> | |
| # results | |
| This model is a fine-tuned version of [r-f/wav2vec-english-speech-emotion-recognition](https://huggingface.co/r-f/wav2vec-english-speech-emotion-recognition) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1011 | |
| - Accuracy: 0.9724 | |
| ## 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: 0.001 | |
| - train_batch_size: 10 | |
| - eval_batch_size: 5 | |
| - seed: 42 | |
| - 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_steps: 50 | |
| - num_epochs: 15 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 1.4918 | 1.0 | 232 | 1.3591 | 0.3672 | | |
| | 1.0899 | 2.0 | 464 | 0.9012 | 0.5672 | | |
| | 0.9523 | 3.0 | 696 | 1.2430 | 0.4862 | | |
| | 0.8062 | 4.0 | 928 | 0.6423 | 0.7759 | | |
| | 0.5591 | 5.0 | 1160 | 0.5161 | 0.8276 | | |
| | 0.4538 | 6.0 | 1392 | 0.6369 | 0.8069 | | |
| | 0.3527 | 7.0 | 1624 | 0.2526 | 0.9207 | | |
| | 0.3833 | 8.0 | 1856 | 0.2226 | 0.9328 | | |
| | 0.2532 | 9.0 | 2088 | 0.1955 | 0.9466 | | |
| | 0.1296 | 10.0 | 2320 | 0.1860 | 0.9483 | | |
| | 0.144 | 11.0 | 2552 | 0.1885 | 0.9552 | | |
| | 0.1976 | 12.0 | 2784 | 0.1243 | 0.9655 | | |
| | 0.0147 | 13.0 | 3016 | 0.1375 | 0.9655 | | |
| | 0.0149 | 14.0 | 3248 | 0.1061 | 0.9776 | | |
| | 0.0199 | 15.0 | 3480 | 0.1011 | 0.9724 | | |
| ### Framework versions | |
| - Transformers 4.53.0 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.2 | |