| import tempfile ,os | |
| import gradio as gr | |
| from transformers import VitsModel, AutoTokenizer,pipeline | |
| import torch | |
| import numpy as np | |
| import torchaudio | |
| def TTS(text): | |
| model = VitsModel.from_pretrained("SeyedAli/Persian-Speech-synthesis") | |
| tokenizer = AutoTokenizer.from_pretrained("SeyedAli/Persian-Speech-synthesis") | |
| inputs = tokenizer(text, return_tensors="pt") | |
| pipe = pipeline("text-to-speech", model=model,tokenizer=tokenizer) | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: | |
| torchaudio.save(fp, pipe(text)['audio'], rate=pipe(text)['sampling_rate']) | |
| return fp.name | |
| iface = gr.Interface(fn=TTS, inputs="text", outputs="audio") | |
| iface.launch(share=False) |