Instructions to use wenxinkoh06/speecht5_tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wenxinkoh06/speecht5_tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="wenxinkoh06/speecht5_tts")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("wenxinkoh06/speecht5_tts") model = AutoModelForTextToSpectrogram.from_pretrained("wenxinkoh06/speecht5_tts") - Notebooks
- Google Colab
- Kaggle
speecht5_tailo_Hokkien_ver1.0.a
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: 0.3882
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: 16
- eval_batch_size: 8
- seed: 1
- 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: 400.0
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9773 | 0.5376 | 400 | 0.5568 |
| 0.5720 | 1.0753 | 800 | 0.4502 |
| 0.5098 | 1.6129 | 1200 | 0.4448 |
| 0.4776 | 2.1505 | 1600 | 0.4181 |
| 0.4564 | 2.6882 | 2000 | 0.4099 |
| 0.4461 | 3.2258 | 2400 | 0.4007 |
| 0.4391 | 3.7634 | 2800 | 0.3968 |
| 0.4353 | 4.3011 | 3200 | 0.3900 |
| 0.4296 | 4.8387 | 3600 | 0.3913 |
| 0.4254 | 5.3763 | 4000 | 0.3882 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for wenxinkoh06/speecht5_tts
Base model
microsoft/speecht5_tts