---
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