---
language:
- en
license: mit
base_model: microsoft/speecht5_tts
tags:
- en_accent,mozilla,t5,common_voice_1_0
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_1_0
model-index:
- name: SpeechT5 TTS English Accented
  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 TTS English Accented

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Common Voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5854

## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 1.41  | 250   | 0.5448          |
| 0.6715        | 2.82  | 500   | 0.5147          |
| 0.6715        | 4.24  | 750   | 0.5225          |
| 0.5532        | 5.65  | 1000  | 0.5096          |
| 0.5532        | 7.06  | 1250  | 0.5293          |
| 0.5156        | 8.47  | 1500  | 0.5310          |
| 0.5156        | 9.89  | 1750  | 0.5417          |
| 0.4874        | 11.3  | 2000  | 0.5185          |
| 0.4874        | 12.71 | 2250  | 0.5112          |
| 0.4693        | 14.12 | 2500  | 0.5154          |
| 0.4693        | 15.54 | 2750  | 0.5148          |
| 0.4619        | 16.95 | 3000  | 0.5367          |
| 0.4619        | 18.36 | 3250  | 0.5207          |
| 0.447         | 19.77 | 3500  | 0.5318          |
| 0.447         | 21.19 | 3750  | 0.5286          |
| 0.4348        | 22.6  | 4000  | 0.5345          |
| 0.4348        | 24.01 | 4250  | 0.5362          |
| 0.4237        | 25.42 | 4500  | 0.5568          |
| 0.4237        | 26.84 | 4750  | 0.5352          |
| 0.4195        | 28.25 | 5000  | 0.5395          |
| 0.4195        | 29.66 | 5250  | 0.5487          |
| 0.4132        | 31.07 | 5500  | 0.5443          |
| 0.4132        | 32.49 | 5750  | 0.5491          |
| 0.3975        | 33.9  | 6000  | 0.5465          |
| 0.3975        | 35.31 | 6250  | 0.5505          |
| 0.396         | 36.72 | 6500  | 0.5450          |
| 0.396         | 38.14 | 6750  | 0.5510          |
| 0.3884        | 39.55 | 7000  | 0.5517          |
| 0.3884        | 40.96 | 7250  | 0.5685          |
| 0.383         | 42.37 | 7500  | 0.5622          |
| 0.383         | 43.79 | 7750  | 0.5659          |
| 0.3806        | 45.2  | 8000  | 0.5636          |
| 0.3806        | 46.61 | 8250  | 0.5681          |
| 0.3738        | 48.02 | 8500  | 0.5797          |
| 0.3738        | 49.44 | 8750  | 0.5741          |
| 0.3705        | 50.85 | 9000  | 0.5765          |
| 0.3705        | 52.26 | 9250  | 0.5770          |
| 0.364         | 53.67 | 9500  | 0.5854          |
| 0.364         | 55.08 | 9750  | 0.5806          |
| 0.36          | 56.5  | 10000 | 0.5854          |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.14.1