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---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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_nl
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: 0.4591
## 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: 16
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7034 | 0.43 | 100 | 0.6427 |
| 0.6812 | 0.86 | 200 | 0.5998 |
| 0.5928 | 1.29 | 300 | 0.5292 |
| 0.5662 | 1.72 | 400 | 0.5095 |
| 0.5547 | 2.15 | 500 | 0.5024 |
| 0.5356 | 2.58 | 600 | 0.4929 |
| 0.532 | 3.01 | 700 | 0.4902 |
| 0.5312 | 3.44 | 800 | 0.4835 |
| 0.5171 | 3.87 | 900 | 0.4800 |
| 0.5178 | 4.3 | 1000 | 0.4780 |
| 0.5152 | 4.73 | 1100 | 0.4777 |
| 0.5064 | 5.16 | 1200 | 0.4737 |
| 0.5122 | 5.59 | 1300 | 0.4726 |
| 0.5042 | 6.02 | 1400 | 0.4700 |
| 0.5075 | 6.45 | 1500 | 0.4712 |
| 0.5057 | 6.88 | 1600 | 0.4688 |
| 0.5037 | 7.31 | 1700 | 0.4674 |
| 0.504 | 7.74 | 1800 | 0.4665 |
| 0.4977 | 8.17 | 1900 | 0.4652 |
| 0.4976 | 8.6 | 2000 | 0.4653 |
| 0.5014 | 9.03 | 2100 | 0.4650 |
| 0.4951 | 9.46 | 2200 | 0.4632 |
| 0.493 | 9.89 | 2300 | 0.4626 |
| 0.4983 | 10.32 | 2400 | 0.4628 |
| 0.4952 | 10.75 | 2500 | 0.4627 |
| 0.4961 | 11.18 | 2600 | 0.4616 |
| 0.4965 | 11.61 | 2700 | 0.4618 |
| 0.4895 | 12.04 | 2800 | 0.4615 |
| 0.4898 | 12.47 | 2900 | 0.4600 |
| 0.5008 | 12.9 | 3000 | 0.4614 |
| 0.4896 | 13.33 | 3100 | 0.4603 |
| 0.4957 | 13.76 | 3200 | 0.4612 |
| 0.4878 | 14.19 | 3300 | 0.4598 |
| 0.4923 | 14.62 | 3400 | 0.4594 |
| 0.4939 | 15.05 | 3500 | 0.4596 |
| 0.4828 | 15.48 | 3600 | 0.4591 |
| 0.4865 | 15.91 | 3700 | 0.4588 |
| 0.4999 | 16.34 | 3800 | 0.4600 |
| 0.4895 | 16.77 | 3900 | 0.4587 |
| 0.4929 | 17.2 | 4000 | 0.4591 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
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