Instructions to use VijayChoudhari/speecht5_finetuned_mr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VijayChoudhari/speecht5_finetuned_mr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="VijayChoudhari/speecht5_finetuned_mr")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("VijayChoudhari/speecht5_finetuned_mr") model = AutoModelForTextToSpectrogram.from_pretrained("VijayChoudhari/speecht5_finetuned_mr") - Notebooks
- Google Colab
- Kaggle
speecht5_finetuned_mr
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5768
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3277 | 17.8571 | 500 | 0.5882 |
| 0.3036 | 35.7143 | 1000 | 0.5768 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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