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