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"""
Notes
-----
This module contains the functions for audiobook_gen that handle text-to-speech generation.
The functions take in the preprocessed text and invoke the Silero package to generate audio tensors.
"""
import logging
import torch
from stqdm import stqdm
from src import output, config
def load_model():
"""
Load Silero package containg the model information
for the language and speaker set in config.py
and converts it to the set device.
Parameters
----------
None
Returns
-------
model : torch.package
"""
from silero import silero_tts
model, _ = silero_tts(language=config.LANGUAGE, speaker=config.MODEL_ID)
model.to(config.DEVICE)
return model
def generate_audio(corpus, title, model, speaker):
"""
For each section within the corpus, calls predict() function to generate audio tensors
and then calls write_audio() to output the tensors to audio files.
Parameters
----------
corpus : array_like
list of list of strings,
body of tokenized text from which audio is generated
title : str
title of document, used to name output files
model : torch.package
torch package containing model for language and speaker specified
speaker : str
identifier of selected speaker for audio generation
Returns
-------
None
"""
for section in stqdm(corpus, desc="Sections in document:"):
section_index = f'part{corpus.index(section):03}'
audio_list, sample_path = predict(section, section_index, title, model, speaker)
output.write_audio(audio_list, sample_path)
def predict(text_section, section_index, title, model, speaker):
"""
Applies Silero TTS engine for each token within the corpus section,
appending it to the output tensor array, and creates file path for output.
Parameters
----------
text_section : array_like
list of strings,
body of tokenized text from which audio is generated
section_index : int
index of current section within corpus
title : str
title of document, used to name output files
model : torch.package
torch package containing model for language and speaker specified
speaker : str
identifier of selected speaker for audio generation
Returns
-------
audio_list : torch.tensor
pytorch tensor containing generated audio
sample_path : str
file name and path for outputting tensor to audio file
"""
audio_list = []
for sentence in stqdm(text_section, desc="Sentences in section:"):
audio = model.apply_tts(text=sentence, speaker=speaker, sample_rate=config.SAMPLE_RATE)
if len(audio) > 0 and isinstance(audio, torch.Tensor):
audio_list.append(audio)
logging.info(f'Tensor generated for sentence: \n {sentence}')
else:
logging.info(f'Tensor for sentence is not valid: \n {sentence}')
sample_path = config.output_path / f'{title}_{section_index}.mp3'
return audio_list, sample_path
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