Instructions to use griffing52/speecht5_finetuned_griffin_ch_lu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use griffing52/speecht5_finetuned_griffin_ch_lu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="griffing52/speecht5_finetuned_griffin_ch_lu")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("griffing52/speecht5_finetuned_griffin_ch_lu") model = AutoModelForTextToSpectrogram.from_pretrained("griffing52/speecht5_finetuned_griffin_ch_lu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
speecht5_finetuned_griffin_ch_lu
This model is a fine-tuned version of microsoft/speecht5_tts on the SwissDial lucerne dialect. It achieves the following results on the evaluation set:
- Loss: 0.4034
Model description
Text-to-speech model for Luzern Swiss German dialect trained using SwissDial lucerne dialect samples.
Intended uses & limitations
A helper for learning Swiss German--though it does not perform well with single word phrases.
Training and evaluation data
SwissDial lucerne dialecct samples.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 100
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5249 | 1.3142 | 100 | 0.4648 |
| 0.4782 | 2.6285 | 200 | 0.4286 |
| 0.4537 | 3.9427 | 300 | 0.4166 |
| 0.437 | 5.2619 | 400 | 0.4078 |
| 0.4268 | 6.5761 | 500 | 0.4034 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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Model tree for griffing52/speecht5_finetuned_griffin_ch_lu
Base model
microsoft/speecht5_tts