Instructions to use Futuresony/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Futuresony/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Futuresony/output")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Futuresony/output") model = AutoModelForTextToSpectrogram.from_pretrained("Futuresony/output") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: mit | |
| base_model: microsoft/speecht5_tts | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - voxpopuli | |
| model-index: | |
| - name: output | |
| 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. --> | |
| # output | |
| This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5978 | |
| ## 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: 4 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 32 | |
| - 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_steps: 50 | |
| - training_steps: 100 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 0.7334 | 0.2152 | 50 | 0.6322 | | |
| | 0.6672 | 0.4303 | 100 | 0.5975 | | |
| ### Framework versions | |
| - Transformers 4.48.0 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 | |