Instructions to use sumish67/senad-speecht5-som with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumish67/senad-speecht5-som with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="sumish67/senad-speecht5-som")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("sumish67/senad-speecht5-som") model = AutoModelForTextToSpectrogram.from_pretrained("sumish67/senad-speecht5-som", device_map="auto") - Notebooks
- Google Colab
- Kaggle
senad-speecht5-som
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: 0.3055
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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3935 | 6.2257 | 1000 | 0.3684 |
| 0.3655 | 12.4514 | 2000 | 0.3229 |
| 0.3396 | 18.6770 | 3000 | 0.3109 |
| 0.3287 | 24.9027 | 4000 | 0.3055 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.11.0+cu128
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for sumish67/senad-speecht5-som
Base model
microsoft/speecht5_tts