---
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: 0.5093

## 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: 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: 15000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| No log        | 11.36  | 250   | 0.5773          |
| 0.6817        | 22.73  | 500   | 0.4226          |
| 0.6817        | 34.09  | 750   | 0.4172          |
| 0.4227        | 45.45  | 1000  | 0.4403          |
| 0.4227        | 56.82  | 1250  | 0.4363          |
| 0.3798        | 68.18  | 1500  | 0.4678          |
| 0.3798        | 79.55  | 1750  | 0.4609          |
| 0.3629        | 90.91  | 2000  | 0.4706          |
| 0.3629        | 102.27 | 2250  | 0.4558          |
| 0.3534        | 113.64 | 2500  | 0.4697          |
| 0.3534        | 125.0  | 2750  | 0.4641          |
| 0.3429        | 136.36 | 3000  | 0.4813          |
| 0.3429        | 147.73 | 3250  | 0.4933          |
| 0.3354        | 159.09 | 3500  | 0.5028          |
| 0.3354        | 170.45 | 3750  | 0.4860          |
| 0.3247        | 181.82 | 4000  | 0.4945          |
| 0.3247        | 193.18 | 4250  | 0.5021          |
| 0.3227        | 204.55 | 4500  | 0.4802          |
| 0.3227        | 215.91 | 4750  | 0.4874          |
| 0.3173        | 227.27 | 5000  | 0.4917          |
| 0.3173        | 238.64 | 5250  | 0.4913          |
| 0.3124        | 250.0  | 5500  | 0.5010          |
| 0.3124        | 261.36 | 5750  | 0.4846          |
| 0.3044        | 272.73 | 6000  | 0.5064          |
| 0.3044        | 284.09 | 6250  | 0.5071          |
| 0.304         | 295.45 | 6500  | 0.5009          |
| 0.304         | 306.82 | 6750  | 0.4901          |
| 0.2991        | 318.18 | 7000  | 0.4887          |
| 0.2991        | 329.55 | 7250  | 0.4908          |
| 0.2981        | 340.91 | 7500  | 0.4866          |
| 0.2981        | 352.27 | 7750  | 0.4972          |
| 0.296         | 363.64 | 8000  | 0.5037          |
| 0.296         | 375.0  | 8250  | 0.5099          |
| 0.2956        | 386.36 | 8500  | 0.4939          |
| 0.2956        | 397.73 | 8750  | 0.5055          |
| 0.2895        | 409.09 | 9000  | 0.5012          |
| 0.2895        | 420.45 | 9250  | 0.5231          |
| 0.2918        | 431.82 | 9500  | 0.5082          |
| 0.2918        | 443.18 | 9750  | 0.5120          |
| 0.289         | 454.55 | 10000 | 0.5067          |
| 0.289         | 465.91 | 10250 | 0.5097          |
| 0.287         | 477.27 | 10500 | 0.5244          |
| 0.287         | 488.64 | 10750 | 0.5116          |
| 0.2836        | 500.0  | 11000 | 0.5073          |
| 0.2836        | 511.36 | 11250 | 0.5089          |
| 0.2864        | 522.73 | 11500 | 0.5145          |
| 0.2864        | 534.09 | 11750 | 0.5077          |
| 0.2831        | 545.45 | 12000 | 0.5006          |
| 0.2831        | 556.82 | 12250 | 0.5145          |
| 0.2824        | 568.18 | 12500 | 0.5124          |
| 0.2824        | 579.55 | 12750 | 0.5166          |
| 0.2836        | 590.91 | 13000 | 0.5174          |
| 0.2836        | 602.27 | 13250 | 0.5082          |
| 0.2814        | 613.64 | 13500 | 0.5157          |
| 0.2814        | 625.0  | 13750 | 0.5210          |
| 0.2813        | 636.36 | 14000 | 0.5161          |
| 0.2813        | 647.73 | 14250 | 0.5092          |
| 0.2804        | 659.09 | 14500 | 0.5131          |
| 0.2804        | 670.45 | 14750 | 0.5128          |
| 0.2796        | 681.82 | 15000 | 0.5093          |


### Framework versions

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