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---
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
|