|
--- |
|
license: mit |
|
base_model: microsoft/speecht5_tts |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: speecht5_tts-wolof-v0.2 |
|
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_tts-wolof-v0.2 |
|
|
|
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: 0.3924 |
|
|
|
## 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: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 16 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 500 |
|
- num_epochs: 30 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-------:|:-----:|:---------------:| |
|
| 0.5083 | 0.9997 | 1908 | 0.4490 | |
|
| 0.4789 | 2.0 | 3817 | 0.4399 | |
|
| 0.4684 | 2.9997 | 5725 | 0.4297 | |
|
| 0.4549 | 4.0 | 7634 | 0.4173 | |
|
| 0.4448 | 4.9997 | 9542 | 0.4123 | |
|
| 0.443 | 6.0 | 11451 | 0.4080 | |
|
| 0.4368 | 6.9997 | 13359 | 0.4059 | |
|
| 0.4351 | 8.0 | 15268 | 0.4030 | |
|
| 0.4319 | 8.9997 | 17176 | 0.4027 | |
|
| 0.4298 | 10.0 | 19085 | 0.4005 | |
|
| 0.4286 | 10.9997 | 20993 | 0.3996 | |
|
| 0.428 | 12.0 | 22902 | 0.3989 | |
|
| 0.4251 | 12.9997 | 24810 | 0.3962 | |
|
| 0.4257 | 14.0 | 26719 | 0.3971 | |
|
| 0.4213 | 14.9997 | 28627 | 0.3956 | |
|
| 0.4245 | 16.0 | 30536 | 0.3949 | |
|
| 0.4186 | 16.9997 | 32444 | 0.3950 | |
|
| 0.4213 | 18.0 | 34353 | 0.3948 | |
|
| 0.4179 | 18.9997 | 36261 | 0.3943 | |
|
| 0.4177 | 20.0 | 38170 | 0.3952 | |
|
| 0.416 | 20.9997 | 40078 | 0.3932 | |
|
| 0.4167 | 22.0 | 41987 | 0.3921 | |
|
| 0.4148 | 22.9997 | 43895 | 0.3935 | |
|
| 0.4133 | 24.0 | 45804 | 0.3938 | |
|
| 0.4169 | 24.9997 | 47712 | 0.3924 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.4.0+cu121 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.19.1 |
|
|
