Instructions to use sil-ai/yao_tts_swh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sil-ai/yao_tts_swh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="sil-ai/yao_tts_swh")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("sil-ai/yao_tts_swh") model = AutoModelForTextToSpectrogram.from_pretrained("sil-ai/yao_tts_swh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
yao_tts_swh
This model is a fine-tuned version of microsoft/speecht5_tts on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0589
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: 8
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- 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: cosine
- lr_scheduler_warmup_steps: 1500
- training_steps: 15000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0812 | 11.6297 | 1000 | 0.0663 |
| 0.071 | 23.2566 | 2000 | 0.0629 |
| 0.0652 | 34.8863 | 3000 | 0.0610 |
| 0.063 | 46.5131 | 4000 | 0.0606 |
| 0.0591 | 58.1399 | 5000 | 0.0592 |
| 0.0558 | 69.7697 | 6000 | 0.0599 |
| 0.0544 | 81.3965 | 7000 | 0.0595 |
| 0.0532 | 93.0233 | 8000 | 0.0594 |
| 0.051 | 104.6531 | 9000 | 0.0596 |
| 0.05 | 116.2799 | 10000 | 0.0594 |
| 0.0479 | 127.9096 | 11000 | 0.0599 |
| 0.047 | 139.5364 | 12000 | 0.0594 |
| 0.0478 | 151.1633 | 13000 | 0.0598 |
| 0.049 | 162.7930 | 14000 | 0.0601 |
| 0.0485 | 174.4198 | 15000 | 0.0589 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.2
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Model tree for sil-ai/yao_tts_swh
Base model
microsoft/speecht5_tts