|
--- |
|
license: mit |
|
base_model: microsoft/speecht5_tts |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: zlm_b64_le5_s12000 |
|
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. --> |
|
|
|
# zlm_b64_le5_s12000 |
|
|
|
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: 0.3623 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 64 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 2000 |
|
- training_steps: 12000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-------:|:-----:|:---------------:| |
|
| 0.7129 | 0.4188 | 500 | 0.6066 | |
|
| 0.5922 | 0.8375 | 1000 | 0.4980 | |
|
| 0.5168 | 1.2563 | 1500 | 0.4579 | |
|
| 0.4954 | 1.6750 | 2000 | 0.4383 | |
|
| 0.4836 | 2.0938 | 2500 | 0.4208 | |
|
| 0.4623 | 2.5126 | 3000 | 0.4098 | |
|
| 0.4499 | 2.9313 | 3500 | 0.4003 | |
|
| 0.4421 | 3.3501 | 4000 | 0.3942 | |
|
| 0.4345 | 3.7688 | 4500 | 0.3894 | |
|
| 0.4233 | 4.1876 | 5000 | 0.3840 | |
|
| 0.4288 | 4.6064 | 5500 | 0.3808 | |
|
| 0.4218 | 5.0251 | 6000 | 0.3775 | |
|
| 0.4174 | 5.4439 | 6500 | 0.3746 | |
|
| 0.4075 | 5.8626 | 7000 | 0.3744 | |
|
| 0.4042 | 6.2814 | 7500 | 0.3715 | |
|
| 0.4075 | 6.7002 | 8000 | 0.3695 | |
|
| 0.4064 | 7.1189 | 8500 | 0.3666 | |
|
| 0.4007 | 7.5377 | 9000 | 0.3663 | |
|
| 0.399 | 7.9564 | 9500 | 0.3649 | |
|
| 0.4152 | 8.3752 | 10000 | 0.3647 | |
|
| 0.4033 | 8.7940 | 10500 | 0.3640 | |
|
| 0.4011 | 9.2127 | 11000 | 0.3628 | |
|
| 0.4002 | 9.6315 | 11500 | 0.3631 | |
|
| 0.3975 | 10.0503 | 12000 | 0.3623 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.0.dev0 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.0 |
|
- Tokenizers 0.19.1 |
|
|
