import tempfile import gradio as gr from TTS.utils.synthesizer import Synthesizer import requests from os.path import exists from formatter import preprocess_text from datetime import datetime from enum import Enum import torch class StressOption(Enum): AutomaticStress = "Автоматичні наголоси" class VoiceOption(Enum): FemaleVoice = "Олена (жіночий)" MaleVoice = "Микита (чоловічий)" def download(url, file_name): if not exists(file_name): print(f"Downloading {file_name}") r = requests.get(url, allow_redirects=True) with open(file_name, "wb") as file: file.write(r.content) else: print(f"Found {file_name}. Skipping download...") print("downloading uk/mykyta/vits-tts") release_number = "v2.0.0" model_link = f"https://github.com/robinhad/ukrainian-tts/releases/download/{release_number}/model-inference.pth" config_link = f"https://github.com/robinhad/ukrainian-tts/releases/download/{release_number}/config.json" speakers_link = f"https://github.com/robinhad/ukrainian-tts/releases/download/{release_number}/speakers.pth" model_path = "model.pth" config_path = "config.json" speakers_path = "speakers.pth" download(model_link, model_path) download(config_link, config_path) download(speakers_link, speakers_path) badge = ( "https://visitor-badge-reloaded.herokuapp.com/badge?page_id=robinhad.ukrainian-tts" ) synthesizer = Synthesizer( model_path, config_path, speakers_path, None, None, ) if synthesizer is None: raise NameError("model not found") def tts(text: str, voice: str, stress: str): print("============================") print("Original text:", text) print("Voice", voice) print("Stress:", stress) print("Time:", datetime.utcnow()) autostress = True if stress == StressOption.AutomaticStress.value else False speaker_name = "male1" if voice == VoiceOption.MaleVoice.value else "female3" text = preprocess_text(text, autostress) text_limit = 1200 text = ( text if len(text) < text_limit else text[0:text_limit] ) # mitigate crashes on hf space print("Converted:", text) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: with torch.no_grad(): wavs = synthesizer.tts(text, speaker_name=speaker_name) synthesizer.save_wav(wavs, fp) return fp.name, text iface = gr.Interface( fn=tts, inputs=[ gr.inputs.Textbox( label="Input", default="Введіть, будь ласка, своє р+ечення.", ), gr.inputs.Radio( label="Голос", choices=[option.value for option in VoiceOption], default=VoiceOption.FemaleVoice.value ), gr.inputs.Radio( label="Наголоси", choices=[option.value for option in StressOption], ), ], outputs=[ gr.outputs.Audio(label="Output"), gr.outputs.Textbox(label="Наголошений текст"), ], title="🐸💬🇺🇦 - Coqui TTS", theme="huggingface", description="Україномовний🇺🇦 TTS за допомогою Coqui TTS (щоб вручну поставити наголос, використовуйте + перед голосною)", article="Якщо вам подобається, підтримайте за посиланням: [SUPPORT LINK](https://send.monobank.ua/jar/48iHq4xAXm), " + "Github: [https://github.com/robinhad/ukrainian-tts](https://github.com/robinhad/ukrainian-tts) \n" + "Model training - [Yurii Paniv @robinhad](https://github.com/robinhad) \n" + "Mykyta and Olena dataset - [Yehor Smoliakov @egorsmkv](https://github.com/egorsmkv) \n" + "Autostress using [ukrainian-word-stress](https://github.com/lang-uk/ukrainian-word-stress) - [Oleksiy Syvokon @asivokon](https://github.com/asivokon) \n" + f'