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