MMS TTS THAI FEMALEV2

🚀 Thai Text-to-Speech Fine-tuning Project

This project focuses on fine - tuning a Thai text - to - speech model. It provides multiple ways to train and use the model, including on Google Colab and local computers.

🚀 Quick Start

• Fine - tuning on GitHub: You can access the fine - tuning code on GitHub at https://github.com/VYNCX/finetune-local-vits.
• Fine - tuning on Google Colab: Train the speech model by yourself on Google Colab using this Finetune - colab.
• Local Usage: Use the model on your local computer by referring to https://github.com/VYNCX/VachanaTTS.

💻 Usage Examples

Basic Usage

import torch
from transformers import VitsTokenizer, VitsModel, set_seed
import scipy

tokenizer = VitsTokenizer.from_pretrained("VIZINTZOR/VIZINTZOR/MMS-TTS-THAI-FEMALEV2",cache_dir="./mms")
model = VitsModel.from_pretrained("VIZINTZOR/VIZINTZOR/MMS-TTS-THAI-FEMALEV2",cache_dir="./mms")

inputs = tokenizer(text="สวัสดีค่ะ นี่คือเสียงพูดภาษาไทย", return_tensors="pt")

set_seed(456)  # make deterministic

with torch.no_grad():
   outputs = model(**inputs)

waveform = outputs.waveform[0]

# Convert PyTorch tensor to NumPy array
waveform_array = waveform.numpy()

scipy.io.wavfile.write("techno_output.wav", rate=model.config.sampling_rate, data=waveform_array)

Advanced Usage

There is no advanced usage example provided in the original document. If you have more complex scenarios, you can further explore the model's parameters and methods based on the basic usage.

📄 License

No license information is provided in the original document.