Instructions to use Chithekitale/PhD_tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chithekitale/PhD_tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Chithekitale/PhD_tts")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Chithekitale/PhD_tts") model = AutoModelForTextToSpectrogram.from_pretrained("Chithekitale/PhD_tts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: mit | |
| base_model: microsoft/speecht5_tts | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - Chithekitale/virus_one_and_two | |
| model-index: | |
| - name: Chichewa TTS_final | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Chichewa TTS_final | |
| This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the chichewaspeechset dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3945 | |
| ## 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: 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: 500 | |
| - training_steps: 4000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-------:|:----:|:---------------:| | |
| | 0.4413 | 6.9965 | 1000 | 0.4014 | | |
| | 0.431 | 13.9895 | 2000 | 0.3980 | | |
| | 0.4227 | 20.9825 | 3000 | 0.3953 | | |
| | 0.4154 | 27.9754 | 4000 | 0.3945 | | |
| ### Framework versions | |
| - Transformers 4.50.3 | |
| - Pytorch 2.6.0+cu118 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.1 | |