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---
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
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