metadata
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: []
SpeechT5 TTS Turkish
This model is a fine-tuned version of 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
!pip install datasets soundfile speechbrain
inference
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")