Instructions to use VoicesColeby/speecht5-finetuned-voxpopuli-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VoicesColeby/speecht5-finetuned-voxpopuli-nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="VoicesColeby/speecht5-finetuned-voxpopuli-nl")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("VoicesColeby/speecht5-finetuned-voxpopuli-nl") model = AutoModelForTextToSpectrogram.from_pretrained("VoicesColeby/speecht5-finetuned-voxpopuli-nl") - Notebooks
- Google Colab
- Kaggle
speecht5-finetuned-voxpopuli-nl
This model is a fine-tuned version of microsoft/speecht5_tts on the VoxPopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5147
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: 100
training_steps: 800
mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.5376 | 19.2365 | 500 | 0.5147 |
Framework versions
Transformers 4.57.6
Pytorch 2.8.0+cu128
Datasets 4.8.4
Tokenizers 0.22.2
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Model tree for VoicesColeby/speecht5-finetuned-voxpopuli-nl
Base model
microsoft/speecht5_tts