---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- keithito/lj_speech
model-index:
- name: speecht5_finetuned_lj_speech
  results: []
pipeline_tag: text-to-speech
---

<!-- 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_lj_speech

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the keithito/lj_speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3772

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.4519        | 1.3569  | 500  | 0.4035          |
| 0.4307        | 2.7137  | 1000 | 0.3897          |
| 0.4243        | 4.0706  | 1500 | 0.3842          |
| 0.4154        | 5.4274  | 2000 | 0.3814          |
| 0.4158        | 6.7843  | 2500 | 0.3793          |
| 0.409         | 8.1411  | 3000 | 0.3783          |
| 0.4112        | 9.4980  | 3500 | 0.3774          |
| 0.4135        | 10.8548 | 4000 | 0.3772          |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0