---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: Youtube3kTTSModel
  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. -->

# Youtube3kTTSModel

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4839

## 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: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6301        | 0.2222  | 100  | 0.5639          |
| 0.6013        | 0.4444  | 200  | 0.5471          |
| 0.5666        | 0.6667  | 300  | 0.5315          |
| 0.5632        | 0.8889  | 400  | 0.5254          |
| 0.5547        | 1.1111  | 500  | 0.5184          |
| 0.5583        | 1.3333  | 600  | 0.5211          |
| 0.5527        | 1.5556  | 700  | 0.5150          |
| 0.5508        | 1.7778  | 800  | 0.5123          |
| 0.5432        | 2.0     | 900  | 0.5135          |
| 0.5478        | 2.2222  | 1000 | 0.5077          |
| 0.5419        | 2.4444  | 1100 | 0.5073          |
| 0.5439        | 2.6667  | 1200 | 0.5083          |
| 0.5381        | 2.8889  | 1300 | 0.5108          |
| 0.5355        | 3.1111  | 1400 | 0.5075          |
| 0.5317        | 3.3333  | 1500 | 0.5053          |
| 0.5345        | 3.5556  | 1600 | 0.5022          |
| 0.5329        | 3.7778  | 1700 | 0.5006          |
| 0.53          | 4.0     | 1800 | 0.4965          |
| 0.5261        | 4.2222  | 1900 | 0.4971          |
| 0.5272        | 4.4444  | 2000 | 0.4976          |
| 0.5272        | 4.6667  | 2100 | 0.4943          |
| 0.5282        | 4.8889  | 2200 | 0.4938          |
| 0.5188        | 5.1111  | 2300 | 0.4980          |
| 0.523         | 5.3333  | 2400 | 0.4894          |
| 0.5225        | 5.5556  | 2500 | 0.4915          |
| 0.5178        | 5.7778  | 2600 | 0.4960          |
| 0.5165        | 6.0     | 2700 | 0.4893          |
| 0.5098        | 6.2222  | 2800 | 0.4892          |
| 0.512         | 6.4444  | 2900 | 0.4868          |
| 0.5177        | 6.6667  | 3000 | 0.4868          |
| 0.5128        | 6.8889  | 3100 | 0.4883          |
| 0.5062        | 7.1111  | 3200 | 0.4852          |
| 0.5104        | 7.3333  | 3300 | 0.4898          |
| 0.5126        | 7.5556  | 3400 | 0.4887          |
| 0.5093        | 7.7778  | 3500 | 0.4908          |
| 0.5075        | 8.0     | 3600 | 0.4828          |
| 0.5029        | 8.2222  | 3700 | 0.4842          |
| 0.5079        | 8.4444  | 3800 | 0.4850          |
| 0.5049        | 8.6667  | 3900 | 0.4853          |
| 0.5034        | 8.8889  | 4000 | 0.4849          |
| 0.4984        | 9.1111  | 4100 | 0.4833          |
| 0.5079        | 9.3333  | 4200 | 0.4863          |
| 0.5023        | 9.5556  | 4300 | 0.4830          |
| 0.5023        | 9.7778  | 4400 | 0.4833          |
| 0.5037        | 10.0    | 4500 | 0.4825          |
| 0.5035        | 10.2222 | 4600 | 0.4822          |
| 0.5011        | 10.4444 | 4700 | 0.4826          |
| 0.4969        | 10.6667 | 4800 | 0.4815          |
| 0.4958        | 10.8889 | 4900 | 0.4839          |
| 0.4972        | 11.1111 | 5000 | 0.4839          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1