---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: bhojpuri_tts
  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. -->

# bhojpuri_tts

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4576

## 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 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: 100
- training_steps: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6508        | 2.5   | 200  | 0.5943          |
| 0.5656        | 5.0   | 400  | 0.5341          |
| 0.523         | 7.5   | 600  | 0.5141          |
| 0.512         | 10.0  | 800  | 0.4860          |
| 0.4966        | 12.5  | 1000 | 0.4803          |
| 0.522         | 15.0  | 1200 | 0.4921          |
| 0.4775        | 17.5  | 1400 | 0.4678          |
| 0.4726        | 20.0  | 1600 | 0.5031          |
| 0.4623        | 22.5  | 1800 | 0.4611          |
| 0.4612        | 25.0  | 2000 | 0.4593          |
| 0.4526        | 27.5  | 2200 | 0.4753          |
| 0.4558        | 30.0  | 2400 | 0.4578          |
| 0.4468        | 32.5  | 2600 | 0.4620          |
| 0.4474        | 35.0  | 2800 | 0.4618          |
| 0.4394        | 37.5  | 3000 | 0.4589          |
| 0.4332        | 40.0  | 3200 | 0.4463          |
| 0.4382        | 42.5  | 3400 | 0.4456          |
| 0.4382        | 45.0  | 3600 | 0.4481          |
| 0.4283        | 47.5  | 3800 | 0.4435          |
| 0.4278        | 50.0  | 4000 | 0.4470          |
| 0.4281        | 52.5  | 4200 | 0.4484          |
| 0.4236        | 55.0  | 4400 | 0.4482          |
| 0.422         | 57.5  | 4600 | 0.4480          |
| 0.4271        | 60.0  | 4800 | 0.4477          |
| 0.4105        | 62.5  | 5000 | 0.4475          |
| 0.4121        | 65.0  | 5200 | 0.4502          |
| 0.4115        | 67.5  | 5400 | 0.4522          |
| 0.4081        | 70.0  | 5600 | 0.4561          |
| 0.4059        | 72.5  | 5800 | 0.4610          |
| 0.4048        | 75.0  | 6000 | 0.4576          |


### Framework versions

- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1