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import argparse
import importlib
import os
from argparse import RawTextHelpFormatter
import numpy as np
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from TTS.config import load_config
from TTS.tts.datasets.TTSDataset import TTSDataset
from TTS.tts.models import setup_model
from TTS.tts.utils.text.characters import make_symbols, phonemes, symbols
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_checkpoint
if __name__ == "__main__":
# pylint: disable=bad-option-value
parser = argparse.ArgumentParser(
description="""Extract attention masks from trained Tacotron/Tacotron2 models.
These masks can be used for different purposes including training a TTS model with a Duration Predictor.\n\n"""
"""Each attention mask is written to the same path as the input wav file with ".npy" file extension.
(e.g. path/bla.wav (wav file) --> path/bla.npy (attention mask))\n"""
"""
Example run:
CUDA_VISIBLE_DEVICE="0" python TTS/bin/compute_attention_masks.py
--model_path /data/rw/home/Models/ljspeech-dcattn-December-14-2020_11+10AM-9d0e8c7/checkpoint_200000.pth
--config_path /data/rw/home/Models/ljspeech-dcattn-December-14-2020_11+10AM-9d0e8c7/config.json
--dataset_metafile metadata.csv
--data_path /root/LJSpeech-1.1/
--batch_size 32
--dataset ljspeech
--use_cuda True
""",
formatter_class=RawTextHelpFormatter,
)
parser.add_argument("--model_path", type=str, required=True, help="Path to Tacotron/Tacotron2 model file ")
parser.add_argument(
"--config_path",
type=str,
required=True,
help="Path to Tacotron/Tacotron2 config file.",
)
parser.add_argument(
"--dataset",
type=str,
default="",
required=True,
help="Target dataset processor name from TTS.tts.dataset.preprocess.",
)
parser.add_argument(
"--dataset_metafile",
type=str,
default="",
required=True,
help="Dataset metafile inclusing file paths with transcripts.",
)
parser.add_argument("--data_path", type=str, default="", help="Defines the data path. It overwrites config.json.")
parser.add_argument("--use_cuda", type=bool, default=False, help="enable/disable cuda.")
parser.add_argument(
"--batch_size", default=16, type=int, help="Batch size for the model. Use batch_size=1 if you have no CUDA."
)
args = parser.parse_args()
C = load_config(args.config_path)
ap = AudioProcessor(**C.audio)
# if the vocabulary was passed, replace the default
if "characters" in C.keys():
symbols, phonemes = make_symbols(**C.characters)
# load the model
num_chars = len(phonemes) if C.use_phonemes else len(symbols)
# TODO: handle multi-speaker
model = setup_model(C)
model, _ = load_checkpoint(model, args.model_path, args.use_cuda, True)
# data loader
preprocessor = importlib.import_module("TTS.tts.datasets.formatters")
preprocessor = getattr(preprocessor, args.dataset)
meta_data = preprocessor(args.data_path, args.dataset_metafile)
dataset = TTSDataset(
model.decoder.r,
C.text_cleaner,
compute_linear_spec=False,
ap=ap,
meta_data=meta_data,
characters=C.characters if "characters" in C.keys() else None,
add_blank=C["add_blank"] if "add_blank" in C.keys() else False,
use_phonemes=C.use_phonemes,
phoneme_cache_path=C.phoneme_cache_path,
phoneme_language=C.phoneme_language,
enable_eos_bos=C.enable_eos_bos_chars,
)
dataset.sort_and_filter_items(C.get("sort_by_audio_len", default=False))
loader = DataLoader(
dataset,
batch_size=args.batch_size,
num_workers=4,
collate_fn=dataset.collate_fn,
shuffle=False,
drop_last=False,
)
# compute attentions
file_paths = []
with torch.no_grad():
for data in tqdm(loader):
# setup input data
text_input = data[0]
text_lengths = data[1]
linear_input = data[3]
mel_input = data[4]
mel_lengths = data[5]
stop_targets = data[6]
item_idxs = data[7]
# dispatch data to GPU
if args.use_cuda:
text_input = text_input.cuda()
text_lengths = text_lengths.cuda()
mel_input = mel_input.cuda()
mel_lengths = mel_lengths.cuda()
model_outputs = model.forward(text_input, text_lengths, mel_input)
alignments = model_outputs["alignments"].detach()
for idx, alignment in enumerate(alignments):
item_idx = item_idxs[idx]
# interpolate if r > 1
alignment = (
torch.nn.functional.interpolate(
alignment.transpose(0, 1).unsqueeze(0),
size=None,
scale_factor=model.decoder.r,
mode="nearest",
align_corners=None,
recompute_scale_factor=None,
)
.squeeze(0)
.transpose(0, 1)
)
# remove paddings
alignment = alignment[: mel_lengths[idx], : text_lengths[idx]].cpu().numpy()
# set file paths
wav_file_name = os.path.basename(item_idx)
align_file_name = os.path.splitext(wav_file_name)[0] + "_attn.npy"
file_path = item_idx.replace(wav_file_name, align_file_name)
# save output
wav_file_abs_path = os.path.abspath(item_idx)
file_abs_path = os.path.abspath(file_path)
file_paths.append([wav_file_abs_path, file_abs_path])
np.save(file_path, alignment)
# ourput metafile
metafile = os.path.join(args.data_path, "metadata_attn_mask.txt")
with open(metafile, "w", encoding="utf-8") as f:
for p in file_paths:
f.write(f"{p[0]}|{p[1]}\n")
print(f" >> Metafile created: {metafile}")
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