# Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("MaroueneSaidi/trainer_output")
model = AutoModelForTextToSpectrogram.from_pretrained("MaroueneSaidi/trainer_output")Quick Links
speecht5_tts-finetuned-voxpopuli_nl
This model is a fine-tuned version of speecht5_tts on the voxpopuli dataset. It achieves the following results on the evaluation set:
- Loss: 0.4905
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 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: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.536 | 8.6220 | 500 | 0.4905 |
Framework versions
- Transformers 4.52.0
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="MaroueneSaidi/trainer_output")