Yurii Paniv
Fix preprocessing
58ef124
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...")