Instructions to use Beehzod/uz_2301_4_tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Beehzod/uz_2301_4_tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Beehzod/uz_2301_4_tts")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Beehzod/uz_2301_4_tts") model = AutoModelForTextToSpectrogram.from_pretrained("Beehzod/uz_2301_4_tts") - Notebooks
- Google Colab
- Kaggle
uz_2301_4_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: 0.4661
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 500
- num_epochs: 120
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3792 | 20.0 | 500 | 0.4262 |
| 0.3585 | 40.0 | 1000 | 0.4389 |
| 0.3556 | 60.0 | 1500 | 0.4512 |
| 0.3493 | 80.0 | 2000 | 0.4535 |
| 0.3465 | 100.0 | 2500 | 0.4634 |
| 0.3421 | 120.0 | 3000 | 0.4661 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Beehzod/uz_2301_4_tts
Base model
microsoft/speecht5_tts