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

# CollectedDataModel

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: 0.4390

## 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.5567        | 0.9913  | 100  | 0.4913          |
| 0.5127        | 1.9827  | 200  | 0.4692          |
| 0.4915        | 2.9740  | 300  | 0.4562          |
| 0.4862        | 3.9653  | 400  | 0.4524          |
| 0.4745        | 4.9566  | 500  | 0.4483          |
| 0.4735        | 5.9480  | 600  | 0.4458          |
| 0.4681        | 6.9393  | 700  | 0.4397          |
| 0.4656        | 7.9306  | 800  | 0.4408          |
| 0.4576        | 8.9219  | 900  | 0.4336          |
| 0.4571        | 9.9133  | 1000 | 0.4343          |
| 0.451         | 10.9046 | 1100 | 0.4339          |
| 0.4517        | 11.8959 | 1200 | 0.4316          |
| 0.4432        | 12.8872 | 1300 | 0.4315          |
| 0.4448        | 13.8786 | 1400 | 0.4357          |
| 0.4455        | 14.8699 | 1500 | 0.4296          |
| 0.4387        | 15.8612 | 1600 | 0.4331          |
| 0.4334        | 16.8525 | 1700 | 0.4359          |
| 0.4373        | 17.8439 | 1800 | 0.4290          |
| 0.4304        | 18.8352 | 1900 | 0.4318          |
| 0.4279        | 19.8265 | 2000 | 0.4305          |
| 0.4294        | 20.8178 | 2100 | 0.4327          |
| 0.4269        | 21.8092 | 2200 | 0.4327          |
| 0.4248        | 22.8005 | 2300 | 0.4309          |
| 0.4255        | 23.7918 | 2400 | 0.4275          |
| 0.43          | 24.7831 | 2500 | 0.4315          |
| 0.4214        | 25.7745 | 2600 | 0.4345          |
| 0.4166        | 26.7658 | 2700 | 0.4362          |
| 0.4173        | 27.7571 | 2800 | 0.4343          |
| 0.4172        | 28.7485 | 2900 | 0.4325          |
| 0.4142        | 29.7398 | 3000 | 0.4329          |
| 0.4134        | 30.7311 | 3100 | 0.4327          |
| 0.4121        | 31.7224 | 3200 | 0.4388          |
| 0.4085        | 32.7138 | 3300 | 0.4352          |
| 0.4095        | 33.7051 | 3400 | 0.4388          |
| 0.4112        | 34.6964 | 3500 | 0.4372          |
| 0.4106        | 35.6877 | 3600 | 0.4388          |
| 0.4054        | 36.6791 | 3700 | 0.4392          |
| 0.4075        | 37.6704 | 3800 | 0.4395          |
| 0.4086        | 38.6617 | 3900 | 0.4393          |
| 0.4125        | 39.6530 | 4000 | 0.4390          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1