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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: 0.7806

## 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: 30000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 0.53  | 250   | 0.8506          |
| 1.0736        | 1.06  | 500   | 0.8219          |
| 1.0736        | 1.6   | 750   | 0.7713          |
| 0.8607        | 2.13  | 1000  | 0.7947          |
| 0.8607        | 2.66  | 1250  | 0.7537          |
| 0.802         | 3.19  | 1500  | 0.7304          |
| 0.802         | 3.72  | 1750  | 0.7409          |
| 0.7627        | 4.26  | 2000  | 0.7282          |
| 0.7627        | 4.79  | 2250  | 0.7224          |
| 0.7442        | 5.32  | 2500  | 0.7132          |
| 0.7442        | 5.85  | 2750  | 0.7718          |
| 0.736         | 6.38  | 3000  | 0.7362          |
| 0.736         | 6.91  | 3250  | 0.7283          |
| 0.7234        | 7.45  | 3500  | 0.7377          |
| 0.7234        | 7.98  | 3750  | 0.7226          |
| 0.6968        | 8.51  | 4000  | 0.7285          |
| 0.6968        | 9.04  | 4250  | 0.7395          |
| 0.692         | 9.57  | 4500  | 0.7306          |
| 0.692         | 10.11 | 4750  | 0.7221          |
| 0.6807        | 10.64 | 5000  | 0.7349          |
| 0.6807        | 11.17 | 5250  | 0.7310          |
| 0.6702        | 11.7  | 5500  | 0.7391          |
| 0.6702        | 12.23 | 5750  | 0.7299          |
| 0.6559        | 12.77 | 6000  | 0.7277          |
| 0.6559        | 13.3  | 6250  | 0.7453          |
| 0.6511        | 13.83 | 6500  | 0.7303          |
| 0.6511        | 14.36 | 6750  | 0.7451          |
| 0.6335        | 14.89 | 7000  | 0.7209          |
| 0.6335        | 15.43 | 7250  | 0.7421          |
| 0.6282        | 15.96 | 7500  | 0.7277          |
| 0.6282        | 16.49 | 7750  | 0.7426          |
| 0.6286        | 17.02 | 8000  | 0.7724          |
| 0.6286        | 17.55 | 8250  | 0.7310          |
| 0.6164        | 18.09 | 8500  | 0.7414          |
| 0.6164        | 18.62 | 8750  | 0.7411          |
| 0.6029        | 19.15 | 9000  | 0.7466          |
| 0.6029        | 19.68 | 9250  | 0.7267          |
| 0.5986        | 20.21 | 9500  | 0.7593          |
| 0.5986        | 20.74 | 9750  | 0.7544          |
| 0.595         | 21.28 | 10000 | 0.7441          |
| 0.595         | 21.81 | 10250 | 0.7422          |
| 0.5905        | 22.34 | 10500 | 0.7399          |
| 0.5905        | 22.87 | 10750 | 0.7494          |
| 0.5792        | 23.4  | 11000 | 0.7311          |
| 0.5792        | 23.94 | 11250 | 0.7479          |
| 0.5774        | 24.47 | 11500 | 0.7615          |
| 0.5774        | 25.0  | 11750 | 0.7578          |
| 0.5684        | 25.53 | 12000 | 0.7603          |
| 0.5684        | 26.06 | 12250 | 0.7300          |
| 0.5621        | 26.6  | 12500 | 0.7385          |
| 0.5621        | 27.13 | 12750 | 0.7447          |
| 0.5666        | 27.66 | 13000 | 0.7400          |
| 0.5666        | 28.19 | 13250 | 0.7518          |
| 0.5525        | 28.72 | 13500 | 0.7462          |
| 0.5525        | 29.26 | 13750 | 0.7351          |
| 0.5471        | 29.79 | 14000 | 0.7673          |
| 0.5471        | 30.32 | 14250 | 0.7325          |
| 0.5449        | 30.85 | 14500 | 0.7455          |
| 0.5449        | 31.38 | 14750 | 0.7473          |
| 0.5349        | 31.91 | 15000 | 0.7549          |
| 0.5349        | 32.45 | 15250 | 0.7513          |
| 0.5345        | 32.98 | 15500 | 0.7472          |
| 0.5345        | 33.51 | 15750 | 0.7542          |
| 0.5285        | 34.04 | 16000 | 0.7513          |
| 0.5285        | 34.57 | 16250 | 0.7466          |
| 0.522         | 35.11 | 16500 | 0.7627          |
| 0.522         | 35.64 | 16750 | 0.7609          |
| 0.5209        | 36.17 | 17000 | 0.7616          |
| 0.5209        | 36.7  | 17250 | 0.7612          |
| 0.5151        | 37.23 | 17500 | 0.7601          |
| 0.5151        | 37.77 | 17750 | 0.7590          |
| 0.5088        | 38.3  | 18000 | 0.7568          |
| 0.5088        | 38.83 | 18250 | 0.7551          |
| 0.5105        | 39.36 | 18500 | 0.7688          |
| 0.5105        | 39.89 | 18750 | 0.7631          |
| 0.5046        | 40.43 | 19000 | 0.7654          |
| 0.5046        | 40.96 | 19250 | 0.7749          |
| 0.5029        | 41.49 | 19500 | 0.7617          |
| 0.5029        | 42.02 | 19750 | 0.7735          |
| 0.4969        | 42.55 | 20000 | 0.7763          |
| 0.4969        | 43.09 | 20250 | 0.7484          |
| 0.497         | 43.62 | 20500 | 0.7606          |
| 0.497         | 44.15 | 20750 | 0.7726          |
| 0.4889        | 44.68 | 21000 | 0.7564          |
| 0.4889        | 45.21 | 21250 | 0.7694          |
| 0.4842        | 45.74 | 21500 | 0.7639          |
| 0.4842        | 46.28 | 21750 | 0.7784          |
| 0.4829        | 46.81 | 22000 | 0.7817          |
| 0.4829        | 47.34 | 22250 | 0.7727          |
| 0.4772        | 47.87 | 22500 | 0.7661          |
| 0.4772        | 48.4  | 22750 | 0.7630          |
| 0.477         | 48.94 | 23000 | 0.7640          |
| 0.477         | 49.47 | 23250 | 0.7730          |
| 0.4766        | 50.0  | 23500 | 0.7708          |
| 0.4766        | 50.53 | 23750 | 0.7716          |
| 0.4717        | 51.06 | 24000 | 0.7670          |
| 0.4717        | 51.6  | 24250 | 0.7671          |
| 0.4686        | 52.13 | 24500 | 0.7711          |
| 0.4686        | 52.66 | 24750 | 0.7704          |
| 0.4685        | 53.19 | 25000 | 0.7775          |
| 0.4685        | 53.72 | 25250 | 0.7690          |
| 0.4635        | 54.26 | 25500 | 0.7839          |
| 0.4635        | 54.79 | 25750 | 0.7746          |
| 0.4617        | 55.32 | 26000 | 0.7738          |
| 0.4617        | 55.85 | 26250 | 0.7753          |
| 0.4549        | 56.38 | 26500 | 0.7830          |
| 0.4549        | 56.91 | 26750 | 0.7777          |
| 0.4564        | 57.45 | 27000 | 0.7758          |
| 0.4564        | 57.98 | 27250 | 0.7728          |
| 0.4546        | 58.51 | 27500 | 0.7772          |
| 0.4546        | 59.04 | 27750 | 0.7795          |
| 0.4511        | 59.57 | 28000 | 0.7754          |
| 0.4511        | 60.11 | 28250 | 0.7867          |
| 0.4467        | 60.64 | 28500 | 0.7838          |
| 0.4467        | 61.17 | 28750 | 0.7858          |
| 0.4512        | 61.7  | 29000 | 0.7758          |
| 0.4512        | 62.23 | 29250 | 0.7819          |
| 0.4497        | 62.77 | 29500 | 0.7871          |
| 0.4497        | 63.3  | 29750 | 0.7817          |
| 0.4463        | 63.83 | 30000 | 0.7806          |


### Framework versions

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