Text-to-Speech
Transformers
PyTorch
Turkish
speecht5
text-to-audio
turkish
speech-generation
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("deryauysal/speechT5_tts_youtube_tr")
model = AutoModelForTextToSpectrogram.from_pretrained("deryauysal/speechT5_tts_youtube_tr", device_map="auto")Quick Links
SpeechT5 TTS Turkish with Youtube
This model is a fine-tuned version of microsoft/speecht5_tts on the test_audio_2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4289
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: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4584 | 76.92 | 1000 | 0.4368 |
| 0.4099 | 153.85 | 2000 | 0.4261 |
| 0.4123 | 230.77 | 3000 | 0.4264 |
| 0.4182 | 307.69 | 4000 | 0.4289 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for deryauysal/speechT5_tts_youtube_tr
Base model
microsoft/speecht5_tts
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="deryauysal/speechT5_tts_youtube_tr")