Instructions to use mihael974/speecht5_finetuned_voxpopuli_nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mihael974/speecht5_finetuned_voxpopuli_nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="mihael974/speecht5_finetuned_voxpopuli_nl")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("mihael974/speecht5_finetuned_voxpopuli_nl") model = AutoModelForTextToSpectrogram.from_pretrained("mihael974/speecht5_finetuned_voxpopuli_nl", device_map="auto") - 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: 0.5641
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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5316 | 1.08 | 1000 | 0.5836 |
| 0.5176 | 2.15 | 2000 | 0.5690 |
| 0.512 | 3.23 | 3000 | 0.5641 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
- 7
Model tree for mihael974/speecht5_finetuned_voxpopuli_nl
Base model
microsoft/speecht5_tts