---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_tr_commonvoice
  results: []
language:
- tr
base_model:
- microsoft/speecht5_tts
---

<!-- 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_finetuned_tr_commonvoice

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5179
- eval_runtime: 361.0936
- eval_samples_per_second: 32.161
- eval_steps_per_second: 16.082
- epoch: 1.6783
- step: 2000

## Model description

```python
import torch
from datasets import load_dataset
import soundfile as sf

embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)

from transformers import pipeline
pipe = pipeline("text-to-audio", model="Chan-Y/speecht5_finetuned_tr_commonvoice")
text = "bugün okula erken geldim, çalışmam lazım."
result = pipe(text, forward_params={"speaker_embeddings": speaker_embedding})

sf.write("speech.wav", result["audio"], samplerate=result["sampling_rate"])

from IPython.display import Audio
Audio("speech.wav")
```


## Training and evaluation data

I used [CommonVoice Turkish Corpus 19.0](https://commonvoice.mozilla.org/tr/datasets)


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3