Instructions to use Mohsen21/EGP_DATA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mohsen21/EGP_DATA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Mohsen21/EGP_DATA")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Mohsen21/EGP_DATA") model = AutoModelForTextToSpectrogram.from_pretrained("Mohsen21/EGP_DATA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Mohsen21/EGP_DATA: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
-
https://huggingface.co/Mohsen21/EGP_DATA/resolve/main/README.md
- Command line
-
hf download hf://Mohsen21/EGP_DATA/README.md
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curl -L -o README.md https://huggingface.co/Mohsen21/EGP_DATA/resolve/main/README.md
1.44 kB
metadata
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: EGP_DATA
results: []
EGP_DATA
This model is a fine-tuned version of microsoft/speecht5_tts on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6109
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: 0.0001
- 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: 100
- training_steps: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6288 | 3.5556 | 100 | 0.6109 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1