Text-to-Speech
Transformers
TensorBoard
Safetensors
Spanish
speecht5
text-to-audio
Generated from Trainer
Instructions to use neopolita/speecht5_finetuned_voxpopuli_es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neopolita/speecht5_finetuned_voxpopuli_es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="neopolita/speecht5_finetuned_voxpopuli_es")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("neopolita/speecht5_finetuned_voxpopuli_es") model = AutoModelForTextToSpectrogram.from_pretrained("neopolita/speecht5_finetuned_voxpopuli_es") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_es
results: []
language:
- es
pipeline_tag: text-to-speech
speecht5_finetuned_voxpopuli_es
This model is a fine-tuned version of microsoft/speecht5_tts on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set:
- Loss: 0.4936
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: 50
- training_steps: 400
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6356 | 0.43 | 100 | 0.5583 |
| 0.566 | 0.86 | 200 | 0.5113 |
| 0.55 | 1.3 | 300 | 0.4967 |
| 0.5462 | 1.73 | 400 | 0.4936 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2