# Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("iqrabatool/Finetuned_speecht5_2")
model = AutoModelForTextToSpectrogram.from_pretrained("iqrabatool/Finetuned_speecht5_2")Quick Links
Finetuned_speecht5_2
This model is a fine-tuned version of microsoft/speecht5_tts on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4976
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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5279 | 5.9172 | 500 | 0.4841 |
| 0.5081 | 11.8343 | 1000 | 0.4887 |
| 0.4844 | 17.7515 | 1500 | 0.4822 |
| 0.4897 | 23.6686 | 2000 | 0.4829 |
| 0.4708 | 29.5858 | 2500 | 0.4898 |
| 0.4801 | 35.5030 | 3000 | 0.4916 |
| 0.4805 | 41.4201 | 3500 | 0.4923 |
| 0.4617 | 47.3373 | 4000 | 0.5006 |
| 0.4761 | 53.2544 | 4500 | 0.4999 |
| 0.4616 | 59.1716 | 5000 | 0.4976 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for iqrabatool/Finetuned_speecht5_2
Base model
microsoft/speecht5_tts
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="iqrabatool/Finetuned_speecht5_2")