Instructions to use radned/speecht5_voxpopuli_nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radned/speecht5_voxpopuli_nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="radned/speecht5_voxpopuli_nl")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("radned/speecht5_voxpopuli_nl") model = AutoModelForTextToSpectrogram.from_pretrained("radned/speecht5_voxpopuli_nl") - Notebooks
- Google Colab
- Kaggle
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speecht5_voxpopuli_nl
This model is a fine-tuned version of on the voxpopuli dataset. It achieves the following results on the evaluation set:
- Loss: 0.9541
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2804 | 4.3 | 1000 | 1.1664 |
| 1.054 | 8.61 | 2000 | 0.9818 |
| 1.0183 | 12.91 | 3000 | 0.9600 |
| 1.0028 | 17.21 | 4000 | 0.9541 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
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