---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- lj_speech
model-index:
- name: speecht5_finetuned_sysdesign
  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_sysdesign

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the lj_speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3717

## 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: 0.0001
- 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: 100
- training_steps: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4684        | 0.31  | 100  | 0.4141          |
| 0.4496        | 0.61  | 200  | 0.4108          |
| 0.4334        | 0.92  | 300  | 0.3955          |
| 0.4245        | 1.22  | 400  | 0.3921          |
| 0.4225        | 1.53  | 500  | 0.3892          |
| 0.4207        | 1.83  | 600  | 0.3858          |
| 0.4151        | 2.14  | 700  | 0.3820          |
| 0.4136        | 2.44  | 800  | 0.3803          |
| 0.4105        | 2.75  | 900  | 0.3782          |
| 0.4083        | 3.05  | 1000 | 0.3763          |
| 0.4046        | 3.36  | 1100 | 0.3764          |
| 0.4012        | 3.66  | 1200 | 0.3748          |
| 0.4004        | 3.97  | 1300 | 0.3733          |
| 0.3998        | 4.27  | 1400 | 0.3726          |
| 0.4013        | 4.58  | 1500 | 0.3717          |


### Framework versions

- Transformers 4.38.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.15.2