---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
model-index:
- name: SpeechT5-Hausa-5
  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-Hausa-5

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4702

## 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: 200
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6245        | 1.8476  | 100  | 0.5508          |
| 0.5758        | 3.6952  | 200  | 0.5566          |
| 0.5463        | 5.5427  | 300  | 0.5015          |
| 0.5299        | 7.3903  | 400  | 0.4968          |
| 0.5139        | 9.2379  | 500  | 0.4792          |
| 0.5132        | 11.0855 | 600  | 0.4823          |
| 0.4982        | 12.9330 | 700  | 0.4640          |
| 0.4889        | 14.7806 | 800  | 0.4649          |
| 0.4841        | 16.6282 | 900  | 0.4601          |
| 0.4795        | 18.4758 | 1000 | 0.4631          |
| 0.4779        | 20.3233 | 1100 | 0.4592          |
| 0.4642        | 22.1709 | 1200 | 0.4651          |
| 0.4618        | 24.0185 | 1300 | 0.4599          |
| 0.4583        | 25.8661 | 1400 | 0.4634          |
| 0.4584        | 27.7136 | 1500 | 0.4592          |
| 0.4539        | 29.5612 | 1600 | 0.4604          |
| 0.4498        | 31.4088 | 1700 | 0.4642          |
| 0.4428        | 33.2564 | 1800 | 0.4677          |
| 0.4517        | 35.1039 | 1900 | 0.4705          |
| 0.4371        | 36.9515 | 2000 | 0.4702          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1