|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: microsoft/speecht5_tts |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: speecht5_finetuned_voxpopuli_nl |
|
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. --> |
|
|
|
# speecht5_finetuned_voxpopuli_nl |
|
|
|
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3776 |
|
|
|
## 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: 125 |
|
- training_steps: 3000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-------:|:----:|:---------------:| |
|
| 0.4834 | 2.3529 | 250 | 0.4367 | |
|
| 0.4405 | 4.7059 | 500 | 0.4013 | |
|
| 0.4323 | 7.0588 | 750 | 0.3927 | |
|
| 0.4244 | 9.4118 | 1000 | 0.3877 | |
|
| 0.4186 | 11.7647 | 1250 | 0.3844 | |
|
| 0.4176 | 14.1176 | 1500 | 0.3831 | |
|
| 0.4137 | 16.4706 | 1750 | 0.3807 | |
|
| 0.4098 | 18.8235 | 2000 | 0.3804 | |
|
| 0.413 | 21.1765 | 2250 | 0.3768 | |
|
| 0.4094 | 23.5294 | 2500 | 0.3781 | |
|
| 0.409 | 25.8824 | 2750 | 0.3789 | |
|
| 0.4028 | 28.2353 | 3000 | 0.3776 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.1+cu121 |
|
- Datasets 3.0.1 |
|
- Tokenizers 0.19.1 |
|
|
