Instructions to use ManuD/speecht5_finetuned_voxpopuli_de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManuD/speecht5_finetuned_voxpopuli_de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="ManuD/speecht5_finetuned_voxpopuli_de")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("ManuD/speecht5_finetuned_voxpopuli_de") model = AutoModelForTextToSpectrogram.from_pretrained("ManuD/speecht5_finetuned_voxpopuli_de") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_de
results: []
speecht5_finetuned_voxpopuli_de
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: 0.4636
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 |
|---|---|---|---|
| 0.5307 | 2.26 | 1000 | 0.4842 |
| 0.5081 | 4.52 | 2000 | 0.4712 |
| 0.505 | 6.79 | 3000 | 0.4646 |
| 0.4986 | 9.05 | 4000 | 0.4636 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3