---
library_name: transformers
license: mit
base_model: Moustapha91/speecht5_finetuned_wo_v1
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 [Moustapha91/speecht5_finetuned_wo_v1](https://huggingface.co/Moustapha91/speecht5_finetuned_wo_v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2943

## 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 |
|:-------------:|:-------:|:----:|:---------------:|
| 0.9404        | 0.5952  | 50   | 0.4362          |
| 0.8342        | 1.1905  | 100  | 0.3784          |
| 0.7869        | 1.7857  | 150  | 0.3627          |
| 0.7841        | 2.3810  | 200  | 0.3546          |
| 0.762         | 2.9762  | 250  | 0.3489          |
| 0.7487        | 3.5714  | 300  | 0.3431          |
| 0.7423        | 4.1667  | 350  | 0.3392          |
| 0.7211        | 4.7619  | 400  | 0.3362          |
| 0.7147        | 5.3571  | 450  | 0.3304          |
| 0.7097        | 5.9524  | 500  | 0.3266          |
| 0.7058        | 6.5476  | 550  | 0.3223          |
| 0.6929        | 7.1429  | 600  | 0.3198          |
| 0.6887        | 7.7381  | 650  | 0.3152          |
| 0.664         | 8.3333  | 700  | 0.3131          |
| 0.6736        | 8.9286  | 750  | 0.3115          |
| 0.6767        | 9.5238  | 800  | 0.3105          |
| 0.6722        | 10.1190 | 850  | 0.3095          |
| 0.6702        | 10.7143 | 900  | 0.3075          |
| 0.6615        | 11.3095 | 950  | 0.3058          |
| 0.6654        | 11.9048 | 1000 | 0.3063          |
| 0.6682        | 12.5    | 1050 | 0.3083          |
| 0.6607        | 13.0952 | 1100 | 0.3051          |
| 0.6514        | 13.6905 | 1150 | 0.3042          |
| 0.6605        | 14.2857 | 1200 | 0.3041          |
| 0.6509        | 14.8810 | 1250 | 0.3028          |
| 0.6556        | 15.4762 | 1300 | 0.3025          |
| 0.6477        | 16.0714 | 1350 | 0.3019          |
| 0.6489        | 16.6667 | 1400 | 0.3011          |
| 0.6567        | 17.2619 | 1450 | 0.3007          |
| 0.6533        | 17.8571 | 1500 | 0.3016          |
| 0.6489        | 18.4524 | 1550 | 0.3009          |
| 0.6454        | 19.0476 | 1600 | 0.3002          |
| 0.6354        | 19.6429 | 1650 | 0.2992          |
| 0.645         | 20.2381 | 1700 | 0.2996          |
| 0.6376        | 20.8333 | 1750 | 0.2993          |
| 0.6472        | 21.4286 | 1800 | 0.2991          |
| 0.6571        | 22.0238 | 1850 | 0.2995          |
| 0.6333        | 22.6190 | 1900 | 0.2986          |
| 0.6323        | 23.2143 | 1950 | 0.2973          |
| 0.6314        | 23.8095 | 2000 | 0.2980          |
| 0.6437        | 24.4048 | 2050 | 0.2980          |
| 0.6383        | 25.0    | 2100 | 0.2977          |
| 0.6314        | 25.5952 | 2150 | 0.2978          |
| 0.6309        | 26.1905 | 2200 | 0.2965          |
| 0.6365        | 26.7857 | 2250 | 0.2965          |
| 0.6406        | 27.3810 | 2300 | 0.2966          |
| 0.6286        | 27.9762 | 2350 | 0.2968          |
| 0.6279        | 28.5714 | 2400 | 0.2963          |
| 0.6304        | 29.1667 | 2450 | 0.2967          |
| 0.6457        | 29.7619 | 2500 | 0.2960          |
| 0.6372        | 30.3571 | 2550 | 0.2958          |
| 0.6338        | 30.9524 | 2600 | 0.2952          |
| 0.6325        | 31.5476 | 2650 | 0.2956          |
| 0.6313        | 32.1429 | 2700 | 0.2951          |
| 0.6345        | 32.7381 | 2750 | 0.2956          |
| 0.6289        | 33.3333 | 2800 | 0.2949          |
| 0.6264        | 33.9286 | 2850 | 0.2947          |
| 0.6302        | 34.5238 | 2900 | 0.2952          |
| 0.6248        | 35.1190 | 2950 | 0.2945          |
| 0.626         | 35.7143 | 3000 | 0.2945          |
| 0.6248        | 36.3095 | 3050 | 0.2947          |
| 0.6306        | 36.9048 | 3100 | 0.2943          |
| 0.6258        | 37.5    | 3150 | 0.2944          |
| 0.6318        | 38.0952 | 3200 | 0.2947          |
| 0.6279        | 38.6905 | 3250 | 0.2947          |
| 0.628         | 39.2857 | 3300 | 0.2940          |
| 0.632         | 39.8810 | 3350 | 0.2947          |
| 0.6259        | 40.4762 | 3400 | 0.2939          |
| 0.6305        | 41.0714 | 3450 | 0.2943          |
| 0.6381        | 41.6667 | 3500 | 0.2939          |
| 0.6341        | 42.2619 | 3550 | 0.2942          |
| 0.6163        | 42.8571 | 3600 | 0.2937          |
| 0.6256        | 43.4524 | 3650 | 0.2934          |
| 0.628         | 44.0476 | 3700 | 0.2934          |
| 0.6371        | 44.6429 | 3750 | 0.2945          |
| 0.6209        | 45.2381 | 3800 | 0.2930          |
| 0.6285        | 45.8333 | 3850 | 0.2939          |
| 0.6309        | 46.4286 | 3900 | 0.2938          |
| 0.6216        | 47.0238 | 3950 | 0.2935          |
| 0.6352        | 47.6190 | 4000 | 0.2943          |


### Framework versions

- Transformers 4.47.0.dev0
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0