Instructions to use mikhail-panzo/malay_micro_checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikhail-panzo/malay_micro_checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="mikhail-panzo/malay_micro_checkpoint")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("mikhail-panzo/malay_micro_checkpoint") model = AutoModelForTextToSpectrogram.from_pretrained("mikhail-panzo/malay_micro_checkpoint") - Notebooks
- Google Colab
- Kaggle
malay_micro_checkpoint
This model is a fine-tuned version of microsoft/speecht5_tts on an unknown 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for mikhail-panzo/malay_micro_checkpoint
Base model
microsoft/speecht5_tts