---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
model-index:
- name: SpeechT5-Hausa-9
  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-9

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: 0.6525

## 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: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6188        | 1.6598  | 100  | 0.6391          |
| 0.5578        | 3.3195  | 200  | 0.6273          |
| 0.5346        | 4.9793  | 300  | 0.6454          |
| 0.5193        | 6.6390  | 400  | 0.6131          |
| 0.5011        | 8.2988  | 500  | 0.6113          |
| 0.5069        | 9.9585  | 600  | 0.6259          |
| 0.495         | 11.6183 | 700  | 0.6292          |
| 0.4835        | 13.2780 | 800  | 0.6238          |
| 0.4795        | 14.9378 | 900  | 0.6300          |
| 0.4747        | 16.5975 | 1000 | 0.6222          |
| 0.4746        | 18.2573 | 1100 | 0.6387          |
| 0.4683        | 19.9170 | 1200 | 0.6220          |
| 0.4591        | 21.5768 | 1300 | 0.6474          |
| 0.4593        | 23.2365 | 1400 | 0.6548          |
| 0.4567        | 24.8963 | 1500 | 0.6322          |
| 0.4529        | 26.5560 | 1600 | 0.6476          |
| 0.4495        | 28.2158 | 1700 | 0.6517          |
| 0.4477        | 29.8755 | 1800 | 0.6397          |
| 0.442         | 31.5353 | 1900 | 0.6557          |
| 0.4412        | 33.1950 | 2000 | 0.6525          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1