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| 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) -> 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) | |
| 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 | |
| def __setup_cache(self, cache_folder=None): | |
| """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, | |
| ) | |
| 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...") | |