Text-to-Speech
Transformers
PyTorch
TensorBoard
Safetensors
speecht5
text-to-audio
Generated from Trainer
Instructions to use juancopi81/speecht5_finetuned_voxpopuli_es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juancopi81/speecht5_finetuned_voxpopuli_es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="juancopi81/speecht5_finetuned_voxpopuli_es")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("juancopi81/speecht5_finetuned_voxpopuli_es") model = AutoModelForTextToSpectrogram.from_pretrained("juancopi81/speecht5_finetuned_voxpopuli_es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
speecht5_finetuned_voxpopuli_es
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: 0.4454
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 |
|---|---|---|---|
| 0.5097 | 4.32 | 1000 | 0.4626 |
| 0.4842 | 8.64 | 2000 | 0.4507 |
| 0.4828 | 12.97 | 3000 | 0.4483 |
| 0.4807 | 17.29 | 4000 | 0.4454 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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Model tree for juancopi81/speecht5_finetuned_voxpopuli_es
Base model
microsoft/speecht5_tts