---
library_name: transformers
language:
- yo
license: mit
base_model: microsoft/speecht5_tts
tags:
- Nigeria
- generated_from_trainer
datasets:
- ccibeekeoc42/all_tts_v2_processed_with_speaker_embeddings
model-index:
- name: SpeechT5 TTS Igbo Yoruba
  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 Igbo Yoruba

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the all_tts_v2_processed_with_speaker_embeddings dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4111

## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.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: 18000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.6671        | 0.0526 | 250   | 0.5571          |
| 0.5767        | 0.1052 | 500   | 0.4814          |
| 0.5233        | 0.1577 | 750   | 0.4562          |
| 0.5045        | 0.2103 | 1000  | 0.4461          |
| 0.4917        | 0.2629 | 1250  | 0.4440          |
| 0.4908        | 0.3155 | 1500  | 0.4398          |
| 0.4881        | 0.3680 | 1750  | 0.4346          |
| 0.4855        | 0.4206 | 2000  | 0.4361          |
| 0.4785        | 0.4732 | 2250  | 0.4343          |
| 0.4753        | 0.5258 | 2500  | 0.4310          |
| 0.4767        | 0.5783 | 2750  | 0.4309          |
| 0.4707        | 0.6309 | 3000  | 0.4280          |
| 0.4724        | 0.6835 | 3250  | 0.4278          |
| 0.4694        | 0.7361 | 3500  | 0.4264          |
| 0.4674        | 0.7886 | 3750  | 0.4259          |
| 0.4659        | 0.8412 | 4000  | 0.4263          |
| 0.4631        | 0.8938 | 4250  | 0.4243          |
| 0.4644        | 0.9464 | 4500  | 0.4232          |
| 0.4619        | 0.9989 | 4750  | 0.4221          |
| 0.4662        | 1.0515 | 5000  | 0.4244          |
| 0.4602        | 1.1041 | 5250  | 0.4217          |
| 0.4616        | 1.1567 | 5500  | 0.4211          |
| 0.461         | 1.2093 | 5750  | 0.4201          |
| 0.4576        | 1.2618 | 6000  | 0.4212          |
| 0.4573        | 1.3144 | 6250  | 0.4187          |
| 0.4598        | 1.3670 | 6500  | 0.4186          |
| 0.4551        | 1.4196 | 6750  | 0.4200          |
| 0.4599        | 1.4721 | 7000  | 0.4175          |
| 0.4576        | 1.5247 | 7250  | 0.4169          |
| 0.4569        | 1.5773 | 7500  | 0.4180          |
| 0.4539        | 1.6299 | 7750  | 0.4175          |
| 0.4552        | 1.6824 | 8000  | 0.4158          |
| 0.4554        | 1.7350 | 8250  | 0.4163          |
| 0.451         | 1.7876 | 8500  | 0.4171          |
| 0.4558        | 1.8402 | 8750  | 0.4163          |
| 0.4539        | 1.8927 | 9000  | 0.4153          |
| 0.4537        | 1.9453 | 9250  | 0.4160          |
| 0.453         | 1.9979 | 9500  | 0.4164          |
| 0.4539        | 2.0505 | 9750  | 0.4157          |
| 0.4561        | 2.1030 | 10000 | 0.4143          |
| 0.4513        | 2.1556 | 10250 | 0.4144          |
| 0.4525        | 2.2082 | 10500 | 0.4145          |
| 0.4532        | 2.2608 | 10750 | 0.4149          |
| 0.4483        | 2.3134 | 11000 | 0.4140          |
| 0.4496        | 2.3659 | 11250 | 0.4142          |
| 0.4513        | 2.4185 | 11500 | 0.4131          |
| 0.4492        | 2.4711 | 11750 | 0.4134          |
| 0.4504        | 2.5237 | 12000 | 0.4130          |
| 0.4484        | 2.5762 | 12250 | 0.4131          |
| 0.4522        | 2.6288 | 12500 | 0.4132          |
| 0.4467        | 2.6814 | 12750 | 0.4124          |
| 0.4487        | 2.7340 | 13000 | 0.4125          |
| 0.4462        | 2.7865 | 13250 | 0.4117          |
| 0.4459        | 2.8391 | 13500 | 0.4119          |
| 0.4485        | 2.8917 | 13750 | 0.4121          |
| 0.4467        | 2.9443 | 14000 | 0.4121          |
| 0.4495        | 2.9968 | 14250 | 0.4124          |
| 0.4473        | 3.0494 | 14500 | 0.4111          |
| 0.4462        | 3.1020 | 14750 | 0.4112          |
| 0.445         | 3.1546 | 15000 | 0.4119          |
| 0.4497        | 3.2072 | 15250 | 0.4133          |
| 0.4488        | 3.2597 | 15500 | 0.4116          |
| 0.4451        | 3.3123 | 15750 | 0.4115          |
| 0.4473        | 3.3649 | 16000 | 0.4115          |
| 0.4416        | 3.4175 | 16250 | 0.4116          |
| 0.4454        | 3.4700 | 16500 | 0.4106          |
| 0.4491        | 3.5226 | 16750 | 0.4112          |
| 0.4502        | 3.5752 | 17000 | 0.4108          |
| 0.4488        | 3.6278 | 17250 | 0.4111          |
| 0.4474        | 3.6803 | 17500 | 0.4109          |
| 0.4478        | 3.7329 | 17750 | 0.4110          |
| 0.4468        | 3.7855 | 18000 | 0.4111          |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0