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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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pipeline_tag: text-to-speech |
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base_model: microsoft/speecht5_tts |
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model-index: |
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- name: speecht5_finetuned_google_fleurs_greek |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# speecht5_finetuned_google_fleurs_greek |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3920 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2.5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.5174 | 1.0 | 583 | 0.4863 | |
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| 0.4948 | 2.0 | 1166 | 0.4611 | |
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| 0.4723 | 3.0 | 1749 | 0.4503 | |
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| 0.4763 | 4.0 | 2333 | 0.4438 | |
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| 0.4614 | 5.0 | 2916 | 0.4407 | |
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| 0.4569 | 6.0 | 3499 | 0.4387 | |
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| 0.4538 | 7.0 | 4082 | 0.4306 | |
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| 0.4539 | 8.0 | 4666 | 0.4282 | |
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| 0.4564 | 9.0 | 5249 | 0.4230 | |
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| 0.4493 | 10.0 | 5832 | 0.4222 | |
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| 0.445 | 11.0 | 6415 | 0.4190 | |
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| 0.4564 | 12.0 | 6999 | 0.4195 | |
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| 0.4381 | 13.0 | 7582 | 0.4161 | |
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| 0.4328 | 14.0 | 8165 | 0.4147 | |
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| 0.4424 | 15.0 | 8748 | 0.4140 | |
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| 0.4282 | 16.0 | 9332 | 0.4117 | |
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| 0.4349 | 17.0 | 9915 | 0.4090 | |
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| 0.4381 | 18.0 | 10498 | 0.4090 | |
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| 0.4328 | 19.0 | 11081 | 0.4073 | |
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| 0.4347 | 20.0 | 11665 | 0.4079 | |
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| 0.4293 | 21.0 | 12248 | 0.4055 | |
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| 0.4251 | 22.0 | 12831 | 0.4052 | |
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| 0.4359 | 23.0 | 13414 | 0.4023 | |
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| 0.4311 | 24.0 | 13998 | 0.4016 | |
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| 0.421 | 25.0 | 14581 | 0.4014 | |
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| 0.4162 | 26.0 | 15164 | 0.3991 | |
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| 0.4219 | 27.0 | 15747 | 0.3990 | |
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| 0.4247 | 28.0 | 16331 | 0.3989 | |
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| 0.4188 | 29.0 | 16914 | 0.3974 | |
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| 0.4229 | 30.0 | 17497 | 0.3976 | |
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| 0.4246 | 31.0 | 18080 | 0.3960 | |
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| 0.4219 | 32.0 | 18664 | 0.3956 | |
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| 0.4228 | 33.0 | 19247 | 0.3951 | |
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| 0.4183 | 34.0 | 19830 | 0.3946 | |
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| 0.4097 | 35.0 | 20413 | 0.3936 | |
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| 0.4245 | 36.0 | 20997 | 0.3935 | |
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| 0.4184 | 37.0 | 21580 | 0.3930 | |
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| 0.4198 | 38.0 | 22163 | 0.3937 | |
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| 0.4193 | 39.0 | 22746 | 0.3925 | |
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| 0.4096 | 39.98 | 23320 | 0.3920 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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