Instructions to use kingmhd1519/speecht5_Mehdi_Final_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kingmhd1519/speecht5_Mehdi_Final_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="kingmhd1519/speecht5_Mehdi_Final_Model")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("kingmhd1519/speecht5_Mehdi_Final_Model") model = AutoModelForTextToSpectrogram.from_pretrained("kingmhd1519/speecht5_Mehdi_Final_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
speecht5_Mehdi_Final_Model
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: 0.4061
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: 0.0001
- 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: 100
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5191 | 1.3189 | 100 | 0.4804 |
| 0.4849 | 2.6379 | 200 | 0.4396 |
| 0.4628 | 3.9568 | 300 | 0.4248 |
| 0.4324 | 5.2658 | 400 | 0.4125 |
| 0.4395 | 6.5847 | 500 | 0.4061 |
Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
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