---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_2500
  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_finetuned_2500

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: 0.7095

## 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: 3500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch     | Step | Validation Loss |
|:-------------:|:---------:|:----:|:---------------:|
| 0.584         | 88.8889   | 100  | 0.6268          |
| 0.4839        | 177.7778  | 200  | 0.5803          |
| 0.4304        | 266.6667  | 300  | 0.6589          |
| 0.3976        | 355.5556  | 400  | 0.6201          |
| 0.3594        | 444.4444  | 500  | 0.6107          |
| 0.3468        | 533.3333  | 600  | 0.6492          |
| 0.3353        | 622.2222  | 700  | 0.6362          |
| 0.3194        | 711.1111  | 800  | 0.6373          |
| 0.3065        | 800.0     | 900  | 0.6431          |
| 0.2985        | 888.8889  | 1000 | 0.6736          |
| 0.289         | 977.7778  | 1100 | 0.6697          |
| 0.2833        | 1066.6667 | 1200 | 0.7000          |
| 0.2798        | 1155.5556 | 1300 | 0.6875          |
| 0.2775        | 1244.4444 | 1400 | 0.7149          |
| 0.275         | 1333.3333 | 1500 | 0.6630          |
| 0.2722        | 1422.2222 | 1600 | 0.7117          |
| 0.2622        | 1511.1111 | 1700 | 0.6647          |
| 0.2481        | 1600.0    | 1800 | 0.6985          |
| 0.2474        | 1688.8889 | 1900 | 0.7020          |
| 0.2511        | 1777.7778 | 2000 | 0.6847          |
| 0.2359        | 1866.6667 | 2100 | 0.6940          |
| 0.2336        | 1955.5556 | 2200 | 0.6996          |
| 0.2338        | 2044.4444 | 2300 | 0.7134          |
| 0.23          | 2133.3333 | 2400 | 0.7031          |
| 0.2305        | 2222.2222 | 2500 | 0.6993          |
| 0.2273        | 2311.1111 | 2600 | 0.7099          |
| 0.2229        | 2400.0    | 2700 | 0.6907          |
| 0.2151        | 2488.8889 | 2800 | 0.6985          |
| 0.2232        | 2577.7778 | 2900 | 0.6972          |
| 0.2234        | 2666.6667 | 3000 | 0.7091          |
| 0.2165        | 2755.5556 | 3100 | 0.7103          |
| 0.2143        | 2844.4444 | 3200 | 0.7168          |
| 0.22          | 2933.3333 | 3300 | 0.6877          |
| 0.2225        | 3022.2222 | 3400 | 0.7050          |
| 0.2192        | 3111.1111 | 3500 | 0.7095          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1