Instructions to use NishaPrem/speecht5_finetuned_tamil_voice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NishaPrem/speecht5_finetuned_tamil_voice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="NishaPrem/speecht5_finetuned_tamil_voice")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("NishaPrem/speecht5_finetuned_tamil_voice") model = AutoModelForTextToSpectrogram.from_pretrained("NishaPrem/speecht5_finetuned_tamil_voice") - Notebooks
- Google Colab
- Kaggle
speecht5_finetuned_tamil_voice
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: 0.4654
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_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: 100
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6246 | 1.0312 | 100 | 0.5362 |
| 0.5464 | 2.0623 | 200 | 0.5074 |
| 0.5189 | 3.0935 | 300 | 0.4845 |
| 0.5035 | 4.1247 | 400 | 0.4683 |
| 0.4984 | 5.1558 | 500 | 0.4654 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Downloads last month
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Model tree for NishaPrem/speecht5_finetuned_tamil_voice
Base model
microsoft/speecht5_tts