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
metadata
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: igbotts
results: []
igbotts
This model is a fine-tuned version of 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