README.md

---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_tr_commonvoice_2
  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_tr_commonvoice_2

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5934

## Model description
```python
import torch
from transformers import pipeline
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. çok sıkıcı bir dersim var."
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 procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7533        | 1.2972 | 1000 | 0.6445          |
| 0.6745        | 2.5945 | 2000 | 0.6106          |
| 0.6535        | 3.8917 | 3000 | 0.5953          |
| 0.6593        | 5.1889 | 4000 | 0.5934          |


### Framework versions

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