---
library_name: transformers
language:
- bn
license: mit
base_model: microsoft/speecht5_tts
tags:
- Bengali
- generated_from_trainer
datasets:
- ucalyptus/train-bn
model-index:
- name: SpeechT5-tuned-bn
  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-tuned-bn

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the train-bn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5622

## 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: 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: 100
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.2372        | 0.1803 | 100  | 0.7144          |
| 5.5988        | 0.3607 | 200  | 0.6772          |
| 5.4093        | 0.5410 | 300  | 0.6321          |
| 5.3172        | 0.7214 | 400  | 0.6306          |
| 5.1628        | 0.9017 | 500  | 0.6069          |
| 5.1058        | 1.0821 | 600  | 0.6035          |
| 5.0202        | 1.2624 | 700  | 0.5955          |
| 5.0445        | 1.4427 | 800  | 0.5878          |
| 4.9277        | 1.6231 | 900  | 0.5814          |
| 4.9124        | 1.8034 | 1000 | 0.5767          |
| 4.877         | 1.9838 | 1100 | 0.5764          |
| 4.8186        | 2.1641 | 1200 | 0.5672          |
| 4.7883        | 2.3445 | 1300 | 0.5692          |
| 4.7329        | 2.5248 | 1400 | 0.5635          |
| 4.8234        | 2.7051 | 1500 | 0.5598          |
| 4.7006        | 2.8855 | 1600 | 0.5622          |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1