---
license: mit
base_model: bregsi/speecht5_finetuned_voxpopuli_de
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_de_16
  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_de_16

This model is a fine-tuned version of [bregsi/speecht5_finetuned_voxpopuli_de](https://huggingface.co/bregsi/speecht5_finetuned_voxpopuli_de) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4477

## 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: 12000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 0.4928        | 2.2812  | 1000  | 0.4584          |
| 0.4885        | 4.5623  | 2000  | 0.4555          |
| 0.4831        | 6.8435  | 3000  | 0.4523          |
| 0.4815        | 9.1246  | 4000  | 0.4515          |
| 0.4786        | 11.4058 | 5000  | 0.4508          |
| 0.4735        | 13.6869 | 6000  | 0.4491          |
| 0.4734        | 15.9681 | 7000  | 0.4494          |
| 0.4729        | 18.2492 | 8000  | 0.4482          |
| 0.4678        | 20.5304 | 9000  | 0.4483          |
| 0.4722        | 22.8115 | 10000 | 0.4479          |
| 0.47          | 25.0927 | 11000 | 0.4481          |
| 0.4682        | 27.3738 | 12000 | 0.4477          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1