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translator_preprocess_embeds.py
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translator_preprocess_embeds.py
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from data_objects.preprocess_translator import create_dvec_embeddings
from utils.argutils import print_args
from pathlib import Path
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Creates embeddings for the synthesizer from the LibriSpeech utterances.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("synthesizer_root", type=Path, help=\
"Path to the synthesizer training data that contains the audios and the train.txt file. "
"If you let everything as default, it should be <datasets_root>/SV2TTS/translator/.")
parser.add_argument("-e", "--encoder_model_fpath", type=Path,
default="/home/grads/q/quamer.waris/projects/Accentron/pretrained_model/pretrained/encoder/saved_models/pretrained.pt", help= \
"Path your trained encoder model.")
parser.add_argument("-ea", "--encoder_accent_model_fpath", type=Path,
default="/home/grads/q/quamer.waris/projects/Accentron/pretrained_model/pretrained/encoder/saved_models/encoder_accent.pt", help= \
"Path your trained encoder model.")
parser.add_argument("-n", "--n_processes", type=int, default=4, help= \
"Number of parallel processes. An encoder is created for each, so you may need to lower "
"this value on GPUs with low memory. Set it to 1 if CUDA is unhappy.")
parser.add_argument("-emb", "--embedding_type", type=str, default="accent", help= \
"Type of embedding to generate"
"options: both, speaker, accent")
args = parser.parse_args()
# Preprocess the dataset
print_args(args, parser)
create_dvec_embeddings(**vars(args))