---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_interview
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# speecht5_finetuned_emirhan_tr

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: 0.4392

## Model description

The Speech T5 model is a text-to-speech (TTS) model based on the T5 architecture. It has been
pretrained on a large corpus of speech data, allowing it to understand and generate human-like
speech from input text. The model is capable of handling various speech synthesis tasks, making it
suitable for applications such as virtual assistants, audiobook production, and more


## Intended uses & limitations

More information needed

## Training and evaluation data

The model was trained using a custom-made dataset of 170 audio samples, containing commonly asked interview lines. Synthetic audio was generated using Amazon AWS Polly, which offered diverse voice options. The dataset was carefully curated to ensure a variety of speech styles, accents, and phonetic structures, enhancing the model's ability to generalize.



### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- training_steps: 250
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6078        | 2.2535  | 40   | 0.4783          |
| 0.5393        | 4.5070  | 80   | 0.4533          |
| 0.4864        | 6.7606  | 120  | 0.4480          |
| 0.4846        | 9.0141  | 160  | 0.4493          |
| 0.4628        | 11.2676 | 200  | 0.4383          |
| 0.4731        | 13.5211 | 240  | 0.4392          |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu118
- Datasets 3.0.0
- Tokenizers 0.20.0