Text-to-Audio
Transformers
TensorBoard
Safetensors
English
speecht5
[finetuned_model, lj_speech11]
Generated from Trainer
Instructions to use Asim037/lj_speech_asim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Asim037/lj_speech_asim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Asim037/lj_speech_asim")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Asim037/lj_speech_asim") model = AutoModelForTextToSpectrogram.from_pretrained("Asim037/lj_speech_asim") - Notebooks
- Google Colab
- Kaggle
SpeechT5 TTS
This model is a fine-tuned version of microsoft/speecht5_tts on the Lj-Speech dataset.
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 100
- training_steps: 30
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
- 6
Model tree for Asim037/lj_speech_asim
Base model
microsoft/speecht5_tts