---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_tts_wolof
  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_wolof

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: 0.4769

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1638        | 0.0963 | 50   | 1.0066          |
| 2.0092        | 0.1927 | 100  | 0.9555          |
| 1.7858        | 0.2890 | 150  | 0.9100          |
| 1.8592        | 0.3854 | 200  | 0.8772          |
| 1.778         | 0.4817 | 250  | 0.8337          |
| 1.6383        | 0.5780 | 300  | 0.7854          |
| 1.4971        | 0.6744 | 350  | 0.6216          |
| 1.2996        | 0.7707 | 400  | 0.5678          |
| 1.2728        | 0.8671 | 450  | 0.5555          |
| 1.2295        | 0.9634 | 500  | 0.5388          |
| 1.1974        | 1.0597 | 550  | 0.5332          |
| 1.1524        | 1.1561 | 600  | 0.5261          |
| 1.2034        | 1.2524 | 650  | 0.5243          |
| 1.1313        | 1.3487 | 700  | 0.5137          |
| 1.1011        | 1.4451 | 750  | 0.5121          |
| 1.1376        | 1.5414 | 800  | 0.5112          |
| 1.142         | 1.6378 | 850  | 0.5097          |
| 1.1685        | 1.7341 | 900  | 0.5035          |
| 1.122         | 1.8304 | 950  | 0.5030          |
| 1.117         | 1.9268 | 1000 | 0.5075          |
| 1.1164        | 2.0231 | 1050 | 0.5011          |
| 1.1186        | 2.1195 | 1100 | 0.4997          |
| 1.102         | 2.2158 | 1150 | 0.4999          |
| 1.0753        | 2.3121 | 1200 | 0.4995          |
| 1.0459        | 2.4085 | 1250 | 0.4950          |
| 1.1395        | 2.5048 | 1300 | 0.4967          |
| 1.086         | 2.6012 | 1350 | 0.4914          |
| 1.0647        | 2.6975 | 1400 | 0.4922          |
| 1.0779        | 2.7938 | 1450 | 0.4925          |
| 1.0814        | 2.8902 | 1500 | 0.4885          |
| 1.092         | 2.9865 | 1550 | 0.4909          |
| 1.0609        | 3.0829 | 1600 | 0.4883          |
| 1.0517        | 3.1792 | 1650 | 0.4903          |
| 1.0954        | 3.2755 | 1700 | 0.4886          |
| 1.0431        | 3.3719 | 1750 | 0.4903          |
| 1.0756        | 3.4682 | 1800 | 0.4914          |
| 1.0806        | 3.5645 | 1850 | 0.4835          |
| 1.0592        | 3.6609 | 1900 | 0.4891          |
| 1.054         | 3.7572 | 1950 | 0.4906          |
| 1.0297        | 3.8536 | 2000 | 0.4863          |
| 1.0461        | 3.9499 | 2050 | 0.4833          |
| 1.0244        | 4.0462 | 2100 | 0.4836          |
| 1.0341        | 4.1426 | 2150 | 0.4826          |
| 1.0287        | 4.2389 | 2200 | 0.4806          |
| 1.026         | 4.3353 | 2250 | 0.4831          |
| 1.0354        | 4.4316 | 2300 | 0.4850          |
| 1.0665        | 4.5279 | 2350 | 0.4824          |
| 1.0291        | 4.6243 | 2400 | 0.4800          |
| 1.0425        | 4.7206 | 2450 | 0.4791          |
| 1.0367        | 4.8170 | 2500 | 0.4796          |
| 1.0393        | 4.9133 | 2550 | 0.4814          |
| 1.0301        | 5.0096 | 2600 | 0.4799          |
| 1.0431        | 5.1060 | 2650 | 0.4790          |
| 1.0261        | 5.2023 | 2700 | 0.4791          |
| 1.05          | 5.2987 | 2750 | 0.4804          |
| 1.0296        | 5.3950 | 2800 | 0.4794          |
| 1.0296        | 5.4913 | 2850 | 0.4805          |
| 1.0214        | 5.5877 | 2900 | 0.4792          |
| 1.0277        | 5.6840 | 2950 | 0.4781          |
| 1.0355        | 5.7803 | 3000 | 0.4764          |
| 1.0325        | 5.8767 | 3050 | 0.4779          |
| 1.0206        | 5.9730 | 3100 | 0.4768          |
| 1.0316        | 6.0694 | 3150 | 0.4793          |
| 1.0426        | 6.1657 | 3200 | 0.4771          |
| 1.0562        | 6.2620 | 3250 | 0.4786          |
| 1.0242        | 6.3584 | 3300 | 0.4792          |
| 1.0144        | 6.4547 | 3350 | 0.4788          |
| 1.0335        | 6.5511 | 3400 | 0.4761          |
| 0.9995        | 6.6474 | 3450 | 0.4757          |
| 1.0119        | 6.7437 | 3500 | 0.4772          |
| 1.0355        | 6.8401 | 3550 | 0.4789          |
| 0.9858        | 6.9364 | 3600 | 0.4767          |
| 1.0327        | 7.0328 | 3650 | 0.4757          |
| 1.0355        | 7.1291 | 3700 | 0.4746          |
| 0.9974        | 7.2254 | 3750 | 0.4757          |
| 1.0296        | 7.3218 | 3800 | 0.4761          |
| 1.0033        | 7.4181 | 3850 | 0.4759          |
| 1.0013        | 7.5145 | 3900 | 0.4751          |
| 1.0086        | 7.6108 | 3950 | 0.4756          |
| 1.0191        | 7.7071 | 4000 | 0.4769          |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0