from io import BytesIO import requests from os.path import exists, join from espnet2.bin.tts_inference import Text2Speech from enum import Enum from .formatter import preprocess_text from .stress import sentence_to_stress, stress_dict, stress_with_model from torch import no_grad import numpy as np import time import soundfile as sf class Voices(Enum): """List of available voices for the model.""" Olena = 4 Mykyta = 3 Lada = 2 Dmytro = 1 Olga = 5 class Stress(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, device="cpu") -> 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.device = device self.__setup_cache(cache_folder) def tts(self, text: str, voice: int, stress: str, output_fp=BytesIO(), speed=1.0): """ 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 `Stress` enum. - `output_fp` - file-like object output. Stores in RAM by default. """ if stress not in [option.value for option in Stress]: raise ValueError( f"Invalid value for stress option selected! Please use one of the following values: {', '.join([option.value for option in Stress])}." ) if stress == Stress.Model.value: stress = True else: stress = 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) text = sentence_to_stress(text, stress_with_model if stress else stress_dict) # synthesis with no_grad(): start = time.time() wav = self.synthesizer( text, sids=np.array(voice), decode_conf={"alpha": 1 / speed} )["wav"] rtf = (time.time() - start) / (len(wav) / self.synthesizer.fs) print(f"RTF = {rtf:5f}") sf.write( output_fp, wav.view(-1).cpu().numpy(), self.synthesizer.fs, "PCM_16", format="wav", ) output_fp.seek(0) return output_fp, text 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 = "v4.0.0" model_link = f"https://github.com/robinhad/ukrainian-tts/releases/download/{release_number}/model.pth" config_link = f"https://github.com/robinhad/ukrainian-tts/releases/download/{release_number}/config.yaml" if cache_folder is None: cache_folder = "." model_path = join(cache_folder, "model.pth") config_path = join(cache_folder, "config.yaml") self.__download(model_link, model_path) self.__download(config_link, config_path) self.synthesizer = Text2Speech( train_config="config.yaml", model_file="model.pth", device=self.device, # Only for VITS noise_scale=0.333, noise_scale_dur=0.333, ) 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...")