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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...")
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