---
library_name: transformers
language:
- tr
license: mit
base_model: microsoft/speecht5_tts
tags:
- text-to-speech
- generated_from_trainer
datasets:
- erenfazlioglu/turkishvoicedataset
model-index:
- name: SpeechT5 TTS Turkish
  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 TTS Turkish

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the turkishvoicedataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3079

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- training_steps: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.4436        | 1.8484  | 1000 | 0.3752          |
| 0.3822        | 3.6969  | 2000 | 0.3403          |
| 0.3729        | 5.5453  | 3000 | 0.3233          |
| 0.3451        | 7.3937  | 4000 | 0.3153          |
| 0.3315        | 9.2421  | 5000 | 0.3099          |
| 0.3492        | 11.0906 | 6000 | 0.3079          |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1

### Usage
installs
```bash
!pip install datasets soundfile speechbrain
```

inference
```python
from transformers import pipeline
from datasets import load_dataset
import soundfile as sf
import torch
from IPython.display import Audio

synthesiser = pipeline("text-to-speech", "umarigan/speecht5_tts_tr_v1.0")

embeddings_dataset = load_dataset("umarigan/turkish_voice_dataset_embedded", split="train")
speaker_embedding = torch.tensor(embeddings_dataset[736]["speaker_embeddings"]).unsqueeze(0)

# Synthesize speech using the embedding
speech = synthesiser("Bir berber bir berbere gel beraber bir berber kuralım demiş", forward_params={"speaker_embeddings": speaker_embedding})

# Save the generated audio to a file
sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])

# Play the audio in the notebook
Audio("speech.wav")

```