---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_tts
  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

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5139

## 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: 2
- eval_batch_size: 2
- 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        | 1.92  | 250   | 0.6150          |
| 0.7483        | 3.85  | 500   | 0.5494          |
| 0.7483        | 5.77  | 750   | 0.5001          |
| 0.5482        | 7.69  | 1000  | 0.4861          |
| 0.5482        | 9.62  | 1250  | 0.4792          |
| 0.502         | 11.54 | 1500  | 0.4786          |
| 0.502         | 13.46 | 1750  | 0.4804          |
| 0.4794        | 15.38 | 2000  | 0.4803          |
| 0.4794        | 17.31 | 2250  | 0.4724          |
| 0.4685        | 19.23 | 2500  | 0.4801          |
| 0.4685        | 21.15 | 2750  | 0.4740          |
| 0.4553        | 23.08 | 3000  | 0.4840          |
| 0.4553        | 25.0  | 3250  | 0.4857          |
| 0.4567        | 26.92 | 3500  | 0.4792          |
| 0.4567        | 28.85 | 3750  | 0.4831          |
| 0.445         | 30.77 | 4000  | 0.4884          |
| 0.445         | 32.69 | 4250  | 0.4845          |
| 0.4412        | 34.62 | 4500  | 0.4944          |
| 0.4412        | 36.54 | 4750  | 0.4940          |
| 0.4373        | 38.46 | 5000  | 0.4863          |
| 0.4373        | 40.38 | 5250  | 0.4899          |
| 0.4353        | 42.31 | 5500  | 0.4954          |
| 0.4353        | 44.23 | 5750  | 0.5005          |
| 0.4265        | 46.15 | 6000  | 0.4994          |
| 0.4265        | 48.08 | 6250  | 0.4918          |
| 0.4285        | 50.0  | 6500  | 0.5022          |
| 0.4285        | 51.92 | 6750  | 0.4939          |
| 0.4209        | 53.85 | 7000  | 0.4989          |
| 0.4209        | 55.77 | 7250  | 0.4959          |
| 0.4206        | 57.69 | 7500  | 0.5013          |
| 0.4206        | 59.62 | 7750  | 0.5061          |
| 0.4189        | 61.54 | 8000  | 0.5092          |
| 0.4189        | 63.46 | 8250  | 0.5084          |
| 0.422         | 65.38 | 8500  | 0.5116          |
| 0.422         | 67.31 | 8750  | 0.5115          |
| 0.415         | 69.23 | 9000  | 0.5100          |
| 0.415         | 71.15 | 9250  | 0.5121          |
| 0.4179        | 73.08 | 9500  | 0.5112          |
| 0.4179        | 75.0  | 9750  | 0.5115          |
| 0.4139        | 76.92 | 10000 | 0.5139          |


### Framework versions

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