Instructions to use andre-coy/speecht5_tts_tandt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andre-coy/speecht5_tts_tandt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="andre-coy/speecht5_tts_tandt")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("andre-coy/speecht5_tts_tandt") model = AutoModelForTextToSpectrogram.from_pretrained("andre-coy/speecht5_tts_tandt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
SpeechT5_Trini
This model is a fine-tuned version of microsoft/speecht5_tts on the TandT dataset. It achieves the following results on the evaluation set:
- Loss: 0.3751
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: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4358 | 4.78 | 1000 | 0.3881 |
| 0.4205 | 9.57 | 2000 | 0.3799 |
| 0.4029 | 14.35 | 3000 | 0.3749 |
| 0.4106 | 19.14 | 4000 | 0.3751 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for andre-coy/speecht5_tts_tandt
Base model
microsoft/speecht5_tts