Instructions to use kosinebolisa/igbotts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kosinebolisa/igbotts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="kosinebolisa/igbotts")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("kosinebolisa/igbotts") model = AutoModelForTextToSpectrogram.from_pretrained("kosinebolisa/igbotts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from kosinebolisa/igbotts: direct link, hf CLI and curl.
- Browser
- Download file 1.68 kB
-
https://huggingface.co/kosinebolisa/igbotts/resolve/main/README.md
- Command line
-
hf download hf://kosinebolisa/igbotts/README.md
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curl -L -o README.md https://huggingface.co/kosinebolisa/igbotts/resolve/main/README.md
1.68 kB
| library_name: transformers | |
| license: mit | |
| base_model: microsoft/speecht5_tts | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: igbotts | |
| 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. --> | |
| # igbotts | |
| This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5972 | |
| ## 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 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: 500 | |
| - training_steps: 4000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 0.4855 | 250.0 | 1000 | 0.5836 | | |
| | 0.4515 | 500.0 | 2000 | 0.5947 | | |
| | 0.4349 | 750.0 | 3000 | 0.5947 | | |
| | 0.4327 | 1000.0 | 4000 | 0.5972 | | |
| ### Framework versions | |
| - Transformers 4.51.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 | |