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TTS
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Compute embeddings and find characters using config file

pull/581/head
Edresson 2021-06-18 14:04:49 -03:00
parent 14b209c7e9
commit b74b510d3c
3 changed files with 63 additions and 63 deletions
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67
TTS/bin/compute_embeddings.py
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@ -3,71 +3,44 @@ import glob
import os
import torch
import numpy as np
from tqdm import tqdm
from TTS.speaker_encoder.utils.generic_utils import setup_model
from TTS.tts.datasets.preprocess import load_meta_data
from TTS.tts.utils.speakers import SpeakerManager
from TTS.utils.audio import AudioProcessor
from TTS.config import load_config, BaseDatasetConfig
from TTS.config import load_config
parser = argparse.ArgumentParser(
description='Compute embedding vectors for each wav file in a dataset. If "target_dataset" is defined, it generates "speakers.json" necessary for training a multi-speaker model.'
description='Compute embedding vectors for each wav file in a dataset.'
)
parser.add_argument("model_path", type=str, help="Path to model outputs (checkpoint, tensorboard etc.).")
parser.add_argument("model_path", type=str, help="Path to model checkpoint file.")
parser.add_argument(
"config_path",
type=str,
help="Path to config file for training.",
help="Path to model config file.",
)
parser.add_argument("data_path", type=str, help="Data path for wav files - directory or CSV file")
parser.add_argument("output_path", type=str, help="path for output speakers.json.")
parser.add_argument(
"--target_dataset",
"config_dataset_path",
type=str,
default="",
help="Target dataset to pick a processor from TTS.tts.dataset.preprocess. Necessary to create a speakers.json file.",
help="Path to dataset config file.",
)
parser.add_argument("output_path", type=str, help="path for output speakers.json and/or speakers.npy.")
parser.add_argument("--use_cuda", type=bool, help="flag to set cuda.", default=True)
parser.add_argument("--separator", type=str, help="Separator used in file if CSV is passed for data_path", default="|")
parser.add_argument("--save_npy", type=bool, help="flag to set cuda.", default=False)
args = parser.parse_args()
c = load_config(args.config_path)
c_dataset = load_config(args.config_dataset_path)
ap = AudioProcessor(**c["audio"])
data_path = args.data_path
split_ext = os.path.splitext(data_path)
sep = args.separator
train_files, dev_files = load_meta_data(c_dataset.datasets, eval_split=True, ignore_generated_eval=True)
if args.target_dataset != "":
# if target dataset is defined
dataset_config = [
BaseDatasetConfig(name=args.target_dataset, path=args.data_path, meta_file_train=None, meta_file_val=None),
]
wav_files, _ = load_meta_data(dataset_config, eval_split=False)
else:
# if target dataset is not defined
if len(split_ext) > 0 and split_ext[1].lower() == ".csv":
# Parse CSV
print(f"CSV file: {data_path}")
with open(data_path) as f:
wav_path = os.path.join(os.path.dirname(data_path), "wavs")
wav_files = []
print(f"Separator is: {sep}")
for line in f:
components = line.split(sep)
if len(components) != 2:
print("Invalid line")
continue
wav_file = os.path.join(wav_path, components[0] + ".wav")
# print(f'wav_file: {wav_file}')
if os.path.exists(wav_file):
wav_files.append(wav_file)
print(f"Count of wavs imported: {len(wav_files)}")
else:
# Parse all wav files in data_path
wav_files = glob.glob(data_path + "/**/*.wav", recursive=True)
wav_files = train_files + dev_files
# define Encoder model
model = setup_model(c)
@ -100,11 +73,19 @@ for idx, wav_file in enumerate(tqdm(wav_files)):
if speaker_mapping:
# save speaker_mapping if target dataset is defined
if '.json' not in args.output_path:
if '.json' not in args.output_path and '.npy' not in args.output_path:
mapping_file_path = os.path.join(args.output_path, "speakers.json")
mapping_npy_file_path = os.path.join(args.output_path, "speakers.npy")
else:
mapping_file_path = args.output_path
mapping_file_path = args.output_path.replace(".npy", ".json")
mapping_npy_file_path = mapping_file_path.replace(".json", ".npy")
os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True)
if args.save_npy:
np.save(mapping_npy_file_path, speaker_mapping)
print("Speaker embeddings saved at:", mapping_npy_file_path)
speaker_manager = SpeakerManager()
# pylint: disable=W0212
speaker_manager._save_json(mapping_file_path, speaker_mapping)

25
TTS/bin/find_unique_chars.py
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@ -2,40 +2,41 @@
import argparse
import os
from argparse import RawTextHelpFormatter
from TTS.tts.datasets.preprocess import get_preprocessor_by_name
from TTS.tts.datasets.preprocess import load_meta_data
from TTS.config import load_config
def main():
# pylint: disable=bad-option-value
parser = argparse.ArgumentParser(
description="""Find all the unique characters or phonemes in a dataset.\n\n"""
"""Target dataset must be defined in TTS.tts.datasets.preprocess\n\n"""
"""\n\n"""
"""
Example runs:
python TTS/bin/find_unique_chars.py --dataset ljspeech --meta_file /path/to/LJSpeech/metadata.csv
python TTS/bin/find_unique_chars.py --config_path config.json
""",
formatter_class=RawTextHelpFormatter,
)
parser.add_argument(
"--dataset", type=str, default="", help="One of the target dataset names in TTS.tts.datasets.preprocess."
"--config_path", type=str, help="Path to dataset config file.", required=True
)
parser.add_argument("--meta_file", type=str, default=None, help="Path to the transcriptions file of the dataset.")
args = parser.parse_args()
preprocessor = get_preprocessor_by_name(args.dataset)
items = preprocessor(os.path.dirname(args.meta_file), os.path.basename(args.meta_file))
c = load_config(args.config_path)
# load all datasets
train_items, dev_items = load_meta_data(c.datasets, eval_split=True, ignore_generated_eval=True)
items = train_items + dev_items
texts = "".join(item[0] for item in items)
chars = set(texts)
lower_chars = filter(lambda c: c.islower(), chars)
chars_force_lower = set([c.lower() for c in chars])
print(f" > Number of unique characters: {len(chars)}")
print(f" > Unique characters: {''.join(sorted(chars))}")
print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
if __name__ == "__main__":
main()

34
TTS/tts/datasets/preprocess.py
View File

@ -37,7 +37,7 @@ def split_dataset(items):
return items[:eval_split_size], items[eval_split_size:]
def load_meta_data(datasets, eval_split=True):
def load_meta_data(datasets, eval_split=True, ignore_generated_eval=False):
meta_data_train_all = []
meta_data_eval_all = [] if eval_split else None
for dataset in datasets:
@ -54,9 +54,11 @@ def load_meta_data(datasets, eval_split=True):
if eval_split:
if meta_file_val:
meta_data_eval = preprocessor(root_path, meta_file_val)
else:
meta_data_eval_all += meta_data_eval
elif not ignore_generated_eval:
meta_data_eval, meta_data_train = split_dataset(meta_data_train)
meta_data_eval_all += meta_data_eval
meta_data_eval_all += meta_data_eval
meta_data_train_all += meta_data_train
# load attention masks for duration predictor training
if dataset.meta_file_attn_mask:
@ -270,16 +272,20 @@ def libri_tts(root_path, meta_files=None):
items = []
if meta_files is None:
meta_files = glob(f"{root_path}/**/*trans.tsv", recursive=True)
else:
if isinstance(meta_files, str):
meta_files = [os.path.join(root_path, meta_files)]
for meta_file in meta_files:
_meta_file = os.path.basename(meta_file).split(".")[0]
speaker_name = _meta_file.split("_")[0]
chapter_id = _meta_file.split("_")[1]
_root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
with open(meta_file, "r") as ttf:
for line in ttf:
cols = line.split("\t")
wav_file = os.path.join(_root_path, cols[0] + ".wav")
text = cols[1]
file_name = cols[0]
speaker_name, chapter_id, *_ = cols[0].split("_")
_root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
wav_file = os.path.join(_root_path, file_name + ".wav")
text = cols[2]
items.append([text, wav_file, "LTTS_" + speaker_name])
for item in items:
assert os.path.exists(item[1]), f" [!] wav files don't exist - {item[1]}"
@ -355,6 +361,18 @@ def vctk_slim(root_path, meta_files=None, wavs_path="wav48"):
return items
def mls(root_path, meta_files=None):
"""http://www.openslr.org/94/"""
items = []
with open(os.path.join(root_path, meta_files), "r") as meta:
isTrain = "train" in meta_files
for line in meta:
file, text = line.split('\t')
text = text[:-1]
speaker, book, no = file.split('_')
wav_file = os.path.join(root_path, "train" if isTrain else "dev", 'audio', speaker, book, file + ".wav")
items.append([text, wav_file, "MLS_" + speaker])
return items
# ======================================== VOX CELEB ===========================================
def voxceleb2(root_path, meta_file=None):