Text-to-Speech
Transformers
TensorBoard
Safetensors
German
speecht5
text-to-audio
Generated from Trainer
Instructions to use emptx/speecht5-voxpopuli-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emptx/speecht5-voxpopuli-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="emptx/speecht5-voxpopuli-de")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("emptx/speecht5-voxpopuli-de") model = AutoModelForTextToSpectrogram.from_pretrained("emptx/speecht5-voxpopuli-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
speecht5-voxpopuli-de
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: 0.4881
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 |
|---|---|---|---|
| 4.2158 | 15.6287 | 1000 | 0.4962 |
| 3.9700 | 31.2515 | 2000 | 0.4893 |
| 3.9515 | 46.8802 | 3000 | 0.4882 |
| 3.9330 | 62.5029 | 4000 | 0.4881 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.2
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Model tree for emptx/speecht5-voxpopuli-de
Base model
microsoft/speecht5_tts