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--- |
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license: mit |
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base_model: microsoft/speecht5_tts |
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tags: |
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- generated_from_trainer |
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datasets: |
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- voxpopuli |
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model-index: |
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- name: speecht5_finetuned_voxpopuli_hu |
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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_voxpopuli_hu |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4309 |
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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: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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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: 500 |
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- training_steps: 4000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.6932 | 0.54 | 100 | 0.6017 | |
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| 0.6325 | 1.07 | 200 | 0.5632 | |
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| 0.5817 | 1.61 | 300 | 0.5078 | |
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| 0.5326 | 2.15 | 400 | 0.4830 | |
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| 0.5247 | 2.69 | 500 | 0.4703 | |
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| 0.5094 | 3.22 | 600 | 0.4630 | |
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| 0.5023 | 3.76 | 700 | 0.4568 | |
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| 0.4997 | 4.3 | 800 | 0.4541 | |
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| 0.4974 | 4.84 | 900 | 0.4504 | |
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| 0.4915 | 5.37 | 1000 | 0.4495 | |
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| 0.4885 | 5.91 | 1100 | 0.4475 | |
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| 0.4779 | 6.45 | 1200 | 0.4437 | |
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| 0.484 | 6.98 | 1300 | 0.4439 | |
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| 0.4799 | 7.52 | 1400 | 0.4419 | |
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| 0.4783 | 8.06 | 1500 | 0.4410 | |
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| 0.4764 | 8.6 | 1600 | 0.4401 | |
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| 0.4757 | 9.13 | 1700 | 0.4396 | |
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| 0.4742 | 9.67 | 1800 | 0.4378 | |
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| 0.4713 | 10.21 | 1900 | 0.4363 | |
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| 0.4747 | 10.75 | 2000 | 0.4370 | |
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| 0.4719 | 11.28 | 2100 | 0.4356 | |
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| 0.4694 | 11.82 | 2200 | 0.4349 | |
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| 0.4706 | 12.36 | 2300 | 0.4345 | |
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| 0.4757 | 12.89 | 2400 | 0.4341 | |
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| 0.466 | 13.43 | 2500 | 0.4334 | |
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| 0.4648 | 13.97 | 2600 | 0.4332 | |
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| 0.4663 | 14.51 | 2700 | 0.4329 | |
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| 0.4644 | 15.04 | 2800 | 0.4323 | |
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| 0.4646 | 15.58 | 2900 | 0.4324 | |
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| 0.4641 | 16.12 | 3000 | 0.4319 | |
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| 0.4644 | 16.66 | 3100 | 0.4316 | |
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| 0.463 | 17.19 | 3200 | 0.4312 | |
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| 0.4651 | 17.73 | 3300 | 0.4317 | |
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| 0.4637 | 18.27 | 3400 | 0.4315 | |
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| 0.4585 | 18.8 | 3500 | 0.4308 | |
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| 0.4605 | 19.34 | 3600 | 0.4310 | |
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| 0.4586 | 19.88 | 3700 | 0.4301 | |
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| 0.4636 | 20.42 | 3800 | 0.4308 | |
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| 0.4616 | 20.95 | 3900 | 0.4308 | |
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| 0.4593 | 21.49 | 4000 | 0.4309 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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