| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/speecht5_tts |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: Youtube3kTTSModel |
| 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. --> |
|
|
| # Youtube3kTTSModel |
|
|
| 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: 0.4839 |
|
|
| ## 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: 0.0001 |
| - 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: 100 |
| - training_steps: 5000 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-------:|:----:|:---------------:| |
| | 0.6301 | 0.2222 | 100 | 0.5639 | |
| | 0.6013 | 0.4444 | 200 | 0.5471 | |
| | 0.5666 | 0.6667 | 300 | 0.5315 | |
| | 0.5632 | 0.8889 | 400 | 0.5254 | |
| | 0.5547 | 1.1111 | 500 | 0.5184 | |
| | 0.5583 | 1.3333 | 600 | 0.5211 | |
| | 0.5527 | 1.5556 | 700 | 0.5150 | |
| | 0.5508 | 1.7778 | 800 | 0.5123 | |
| | 0.5432 | 2.0 | 900 | 0.5135 | |
| | 0.5478 | 2.2222 | 1000 | 0.5077 | |
| | 0.5419 | 2.4444 | 1100 | 0.5073 | |
| | 0.5439 | 2.6667 | 1200 | 0.5083 | |
| | 0.5381 | 2.8889 | 1300 | 0.5108 | |
| | 0.5355 | 3.1111 | 1400 | 0.5075 | |
| | 0.5317 | 3.3333 | 1500 | 0.5053 | |
| | 0.5345 | 3.5556 | 1600 | 0.5022 | |
| | 0.5329 | 3.7778 | 1700 | 0.5006 | |
| | 0.53 | 4.0 | 1800 | 0.4965 | |
| | 0.5261 | 4.2222 | 1900 | 0.4971 | |
| | 0.5272 | 4.4444 | 2000 | 0.4976 | |
| | 0.5272 | 4.6667 | 2100 | 0.4943 | |
| | 0.5282 | 4.8889 | 2200 | 0.4938 | |
| | 0.5188 | 5.1111 | 2300 | 0.4980 | |
| | 0.523 | 5.3333 | 2400 | 0.4894 | |
| | 0.5225 | 5.5556 | 2500 | 0.4915 | |
| | 0.5178 | 5.7778 | 2600 | 0.4960 | |
| | 0.5165 | 6.0 | 2700 | 0.4893 | |
| | 0.5098 | 6.2222 | 2800 | 0.4892 | |
| | 0.512 | 6.4444 | 2900 | 0.4868 | |
| | 0.5177 | 6.6667 | 3000 | 0.4868 | |
| | 0.5128 | 6.8889 | 3100 | 0.4883 | |
| | 0.5062 | 7.1111 | 3200 | 0.4852 | |
| | 0.5104 | 7.3333 | 3300 | 0.4898 | |
| | 0.5126 | 7.5556 | 3400 | 0.4887 | |
| | 0.5093 | 7.7778 | 3500 | 0.4908 | |
| | 0.5075 | 8.0 | 3600 | 0.4828 | |
| | 0.5029 | 8.2222 | 3700 | 0.4842 | |
| | 0.5079 | 8.4444 | 3800 | 0.4850 | |
| | 0.5049 | 8.6667 | 3900 | 0.4853 | |
| | 0.5034 | 8.8889 | 4000 | 0.4849 | |
| | 0.4984 | 9.1111 | 4100 | 0.4833 | |
| | 0.5079 | 9.3333 | 4200 | 0.4863 | |
| | 0.5023 | 9.5556 | 4300 | 0.4830 | |
| | 0.5023 | 9.7778 | 4400 | 0.4833 | |
| | 0.5037 | 10.0 | 4500 | 0.4825 | |
| | 0.5035 | 10.2222 | 4600 | 0.4822 | |
| | 0.5011 | 10.4444 | 4700 | 0.4826 | |
| | 0.4969 | 10.6667 | 4800 | 0.4815 | |
| | 0.4958 | 10.8889 | 4900 | 0.4839 | |
| | 0.4972 | 11.1111 | 5000 | 0.4839 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.44.2 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 3.0.0 |
| - Tokenizers 0.19.1 |
|
|
