Instructions to use Willie999/speecht5_twi_tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Willie999/speecht5_twi_tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Willie999/speecht5_twi_tts")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Willie999/speecht5_twi_tts") model = AutoModelForTextToSpectrogram.from_pretrained("Willie999/speecht5_twi_tts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
speecht5_twi_tts
This model is a fine-tuned version of microsoft/speecht5_tts on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8945
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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 |
|---|---|---|---|
| 9.1533 | 12.8245 | 1000 | 1.7272 |
| 8.4436 | 25.6441 | 2000 | 1.9018 |
| 8.2113 | 38.4638 | 3000 | 1.8736 |
| 8.0208 | 51.2834 | 4000 | 1.8945 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Willie999/speecht5_twi_tts
Base model
microsoft/speecht5_tts