Instructions to use Nour17/speecht5_finetuned_Andrew_NG_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nour17/speecht5_finetuned_Andrew_NG_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Nour17/speecht5_finetuned_Andrew_NG_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Nour17/speecht5_finetuned_Andrew_NG_small") model = AutoModelForTextToSpectrogram.from_pretrained("Nour17/speecht5_finetuned_Andrew_NG_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
speecht5_finetuned_Andrew_NG_small
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: 0.4969
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: 100
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5266 | 35.4 | 500 | 0.4969 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.16.0
- Tokenizers 0.14.1
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Model tree for Nour17/speecht5_finetuned_Andrew_NG_small
Base model
microsoft/speecht5_tts