---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
model-index:
- name: Hindi_SpeechT5_finetuned
  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. -->

# Hindi_SpeechT5_finetuned

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: 0.4484

## 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: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6781        | 0.3442 | 100  | 0.5528          |
| 0.5834        | 0.6885 | 200  | 0.5470          |
| 0.554         | 1.0327 | 300  | 0.5090          |
| 0.5397        | 1.3769 | 400  | 0.5025          |
| 0.526         | 1.7212 | 500  | 0.4872          |
| 0.5224        | 2.0654 | 600  | 0.4846          |
| 0.515         | 2.4096 | 700  | 0.4754          |
| 0.5047        | 2.7539 | 800  | 0.4703          |
| 0.5014        | 3.0981 | 900  | 0.4689          |
| 0.4946        | 3.4423 | 1000 | 0.4601          |
| 0.4912        | 3.7866 | 1100 | 0.4584          |
| 0.4847        | 4.1308 | 1200 | 0.4554          |
| 0.4824        | 4.4750 | 1300 | 0.4514          |
| 0.4831        | 4.8193 | 1400 | 0.4501          |
| 0.4723        | 5.1635 | 1500 | 0.4484          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1