Instructions to use davron04/speecht5_finetuned_uzbek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davron04/speecht5_finetuned_uzbek with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="davron04/speecht5_finetuned_uzbek")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("davron04/speecht5_finetuned_uzbek") model = AutoModelForTextToSpectrogram.from_pretrained("davron04/speecht5_finetuned_uzbek", device_map="auto") - Notebooks
- Google Colab
- Kaggle
speecht5_finetuned_uzbek
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: 0.3794
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_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 |
|---|---|---|---|
| 3.4293 | 13.5144 | 1000 | 0.4006 |
| 3.2541 | 27.0271 | 2000 | 0.3844 |
| 3.2228 | 40.5415 | 3000 | 0.3810 |
| 3.2477 | 54.0541 | 4000 | 0.3794 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.10.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.2
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Model tree for davron04/speecht5_finetuned_uzbek
Base model
microsoft/speecht5_tts