---
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: 0.7779

## 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        | 31.25   | 250   | 0.8090          |
| 0.7576        | 62.5    | 500   | 0.6545          |
| 0.7576        | 93.75   | 750   | 0.6266          |
| 0.4719        | 125.0   | 1000  | 0.6372          |
| 0.4719        | 156.25  | 1250  | 0.6317          |
| 0.403         | 187.5   | 1500  | 0.6457          |
| 0.403         | 218.75  | 1750  | 0.6571          |
| 0.374         | 250.0   | 2000  | 0.6805          |
| 0.374         | 281.25  | 2250  | 0.6814          |
| 0.3564        | 312.5   | 2500  | 0.6814          |
| 0.3564        | 343.75  | 2750  | 0.6956          |
| 0.342         | 375.0   | 3000  | 0.6769          |
| 0.342         | 406.25  | 3250  | 0.7000          |
| 0.3339        | 437.5   | 3500  | 0.7052          |
| 0.3339        | 468.75  | 3750  | 0.7104          |
| 0.3205        | 500.0   | 4000  | 0.7095          |
| 0.3205        | 531.25  | 4250  | 0.7120          |
| 0.3112        | 562.5   | 4500  | 0.7194          |
| 0.3112        | 593.75  | 4750  | 0.7363          |
| 0.3045        | 625.0   | 5000  | 0.7252          |
| 0.3045        | 656.25  | 5250  | 0.7003          |
| 0.3067        | 687.5   | 5500  | 0.7176          |
| 0.3067        | 718.75  | 5750  | 0.7513          |
| 0.2938        | 750.0   | 6000  | 0.7403          |
| 0.2938        | 781.25  | 6250  | 0.7180          |
| 0.2894        | 812.5   | 6500  | 0.7569          |
| 0.2894        | 843.75  | 6750  | 0.7398          |
| 0.2886        | 875.0   | 7000  | 0.7384          |
| 0.2886        | 906.25  | 7250  | 0.7363          |
| 0.2877        | 937.5   | 7500  | 0.7550          |
| 0.2877        | 968.75  | 7750  | 0.7350          |
| 0.2822        | 1000.0  | 8000  | 0.8012          |
| 0.2822        | 1031.25 | 8250  | 0.7509          |
| 0.2808        | 1062.5  | 8500  | 0.7544          |
| 0.2808        | 1093.75 | 8750  | 0.7851          |
| 0.2746        | 1125.0  | 9000  | 0.7283          |
| 0.2746        | 1156.25 | 9250  | 0.7559          |
| 0.27          | 1187.5  | 9500  | 0.7449          |
| 0.27          | 1218.75 | 9750  | 0.7496          |
| 0.2693        | 1250.0  | 10000 | 0.7375          |
| 0.2693        | 1281.25 | 10250 | 0.7723          |
| 0.2674        | 1312.5  | 10500 | 0.7632          |
| 0.2674        | 1343.75 | 10750 | 0.7825          |
| 0.2678        | 1375.0  | 11000 | 0.7645          |
| 0.2678        | 1406.25 | 11250 | 0.7502          |
| 0.269         | 1437.5  | 11500 | 0.7762          |
| 0.269         | 1468.75 | 11750 | 0.7500          |
| 0.2642        | 1500.0  | 12000 | 0.7503          |
| 0.2642        | 1531.25 | 12250 | 0.7460          |
| 0.2618        | 1562.5  | 12500 | 0.7512          |
| 0.2618        | 1593.75 | 12750 | 0.7889          |
| 0.2642        | 1625.0  | 13000 | 0.7578          |
| 0.2642        | 1656.25 | 13250 | 0.7678          |
| 0.2628        | 1687.5  | 13500 | 0.7638          |
| 0.2628        | 1718.75 | 13750 | 0.7721          |
| 0.2612        | 1750.0  | 14000 | 0.7531          |
| 0.2612        | 1781.25 | 14250 | 0.7924          |
| 0.2555        | 1812.5  | 14500 | 0.7882          |
| 0.2555        | 1843.75 | 14750 | 0.7841          |
| 0.2596        | 1875.0  | 15000 | 0.7779          |


### Framework versions

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