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| from io import BytesIO | |
| import requests | |
| from os.path import exists, join, dirname | |
| 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 | |
| from kaldiio import load_ark | |
| class Voices(Enum): | |
| """List of available voices for the model.""" | |
| Tetiana = "tetiana" | |
| Mykyta = "mykyta" | |
| Lada = "lada" | |
| Dmytro = "dmytro" | |
| 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: str, 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]: | |
| if voice not in self.xvectors.keys(): | |
| 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, spembs=self.xvectors[voice][0], 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.""" | |
| release_number = "v5.0.0" | |
| print( | |
| f"downloading https://github.com/robinhad/ukrainian-tts/releases/download/{release_number}" | |
| ) | |
| 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" | |
| speakers_link = f"https://github.com/robinhad/ukrainian-tts/releases/download/{release_number}/spk_xvector.ark" | |
| if cache_folder is None: | |
| cache_folder = "." | |
| model_path = join(cache_folder, "model.pth") | |
| config_path = join(cache_folder, "config.yaml") | |
| speakers_path = join(cache_folder, "spk_xvector.ark") | |
| self.__download(model_link, model_path) | |
| self.__download(config_link, config_path) | |
| self.__download(speakers_link, speakers_path) | |
| print("downloaded.") | |
| self.synthesizer = Text2Speech( | |
| train_config=config_path, | |
| model_file=model_path, | |
| device=self.device, | |
| # Only for VITS | |
| noise_scale=0.333, | |
| noise_scale_dur=0.333, | |
| ) | |
| self.xvectors = {k: v for k, v in load_ark(speakers_path)} | |
| def __download(self, url, file_name): | |
| """Downloads file from `url` into local `file_name` file.""" | |
| if not exists(file_name): | |
| if not exists(dirname(file_name)): | |
| raise ValueError(f'Directory "{dirname(file_name)}" doesn\'t exist!') | |
| 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...") | |