| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: microsoft/speecht5_tts |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: last_vc |
| | 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. --> |
| |
|
| | # last_vc |
| | |
| | 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: 0.5195 |
| | |
| | ## 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: 5e-06 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 2 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 8 |
| | - total_train_batch_size: 64 |
| | - 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 |
| | - training_steps: 4000 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-------:|:----:|:---------------:| |
| | | 0.7194 | 0.3820 | 100 | 0.6257 | |
| | | 0.6475 | 0.7641 | 200 | 0.5903 | |
| | | 0.622 | 1.1452 | 300 | 0.5744 | |
| | | 0.6042 | 1.5272 | 400 | 0.5645 | |
| | | 0.5932 | 1.9093 | 500 | 0.5568 | |
| | | 0.5962 | 2.2904 | 600 | 0.5545 | |
| | | 0.5877 | 2.6724 | 700 | 0.5494 | |
| | | 0.572 | 3.0535 | 800 | 0.5465 | |
| | | 0.5705 | 3.4355 | 900 | 0.5434 | |
| | | 0.5698 | 3.8176 | 1000 | 0.5394 | |
| | | 0.5661 | 4.1987 | 1100 | 0.5393 | |
| | | 0.5569 | 4.5807 | 1200 | 0.5378 | |
| | | 0.5627 | 4.9628 | 1300 | 0.5363 | |
| | | 0.5596 | 5.3438 | 1400 | 0.5338 | |
| | | 0.5581 | 5.7259 | 1500 | 0.5310 | |
| | | 0.5542 | 6.1070 | 1600 | 0.5307 | |
| | | 0.5483 | 6.4890 | 1700 | 0.5304 | |
| | | 0.5536 | 6.8711 | 1800 | 0.5273 | |
| | | 0.5595 | 7.2521 | 1900 | 0.5273 | |
| | | 0.5448 | 7.6342 | 2000 | 0.5276 | |
| | | 0.5429 | 8.0153 | 2100 | 0.5270 | |
| | | 0.5507 | 8.3973 | 2200 | 0.5261 | |
| | | 0.5511 | 8.7794 | 2300 | 0.5251 | |
| | | 0.5501 | 9.1605 | 2400 | 0.5243 | |
| | | 0.5434 | 9.5425 | 2500 | 0.5254 | |
| | | 0.5434 | 9.9245 | 2600 | 0.5249 | |
| | | 0.5477 | 10.3056 | 2700 | 0.5210 | |
| | | 0.5455 | 10.6877 | 2800 | 0.5213 | |
| | | 0.5412 | 11.0688 | 2900 | 0.5212 | |
| | | 0.5416 | 11.4508 | 3000 | 0.5203 | |
| | | 0.5417 | 11.8329 | 3100 | 0.5236 | |
| | | 0.5361 | 12.2139 | 3200 | 0.5220 | |
| | | 0.5411 | 12.5960 | 3300 | 0.5220 | |
| | | 0.5446 | 12.9780 | 3400 | 0.5191 | |
| | | 0.5415 | 13.3591 | 3500 | 0.5199 | |
| | | 0.5426 | 13.7412 | 3600 | 0.5210 | |
| | | 0.5391 | 14.1223 | 3700 | 0.5198 | |
| | | 0.5418 | 14.5043 | 3800 | 0.5196 | |
| | | 0.5437 | 14.8863 | 3900 | 0.5195 | |
| | | 0.539 | 15.2674 | 4000 | 0.5195 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.47.0 |
| | - Pytorch 2.5.1+cu121 |
| | - Datasets 3.3.1 |
| | - Tokenizers 0.21.0 |
| |
|
