from io import BytesIO import requests from os.path import exists, join from TTS.utils.synthesizer import Synthesizer from enum import Enum from .formatter import preprocess_text from torch import no_grad class Voices(Enum): """List of available voices for the model.""" Olena = "olena" Mykyta = "mykyta" Lada = "lada" Dmytro = "dmytro" Olga = "olga" class StressOption(Enum): """Options how to stress sentence. - `dictionary` - performs lookup in dictionary, taking into account grammatical case of a word and its' neighbors - `model` - stress using transformer model""" Dictionary = "dictionary" Model = "model" class TTS: """ """ def __init__(self, cache_folder=None, use_cuda=False) -> None: """ Class to setup a text-to-speech engine, from download to model creation. \n Downloads or uses files from `cache_folder` directory. \n By default stores in current directory.""" self.__setup_cache(cache_folder, use_cuda=use_cuda) def tts(self, text: str, voice: str, stress: str, output_fp=BytesIO()): """ Run a Text-to-Speech engine and output to `output_fp` BytesIO-like object. - `text` - your model input text. - `voice` - one of predefined voices from `Voices` enum. - `stress` - stress method options, predefined in `StressOption` enum. - `output_fp` - file-like object output. Stores in RAM by default. """ autostress_with_model = ( True if stress == StressOption.Model.value else False ) if voice not in [option.value for option in Voices]: raise ValueError(f"Invalid value for voice selected! Please use one of the following values: {', '.join([option.value for option in Voices])}.") text = preprocess_text(text, autostress_with_model) with no_grad(): wavs = self.synthesizer.tts(text, speaker_name=voice) self.synthesizer.save_wav(wavs, output_fp) output_fp.seek(0) return output_fp, text def __setup_cache(self, cache_folder=None, use_cuda=False): """Downloads models and stores them into `cache_folder`. By default stores in current directory.""" print("downloading uk/mykyta/vits-tts") release_number = "v3.0.0" 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" if cache_folder is None: cache_folder = "." model_path = join(cache_folder, "model.pth") config_path = join(cache_folder, "config.json") speakers_path = join(cache_folder, "speakers.pth") self.__download(model_link, model_path) self.__download(config_link, config_path) self.__download(speakers_link, speakers_path) self.synthesizer = Synthesizer( model_path, config_path, speakers_path, None, None, use_cuda=use_cuda ) if self.synthesizer is None: raise NameError("Model not found") def __download(self, url, file_name): """Downloads file from `url` into local `file_name` file.""" 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...")