import json
import logging
import os
import random
from copy import deepcopy
import numpy as np
import yaml
from resemblyzer import VoiceEncoder
from tqdm import tqdm
from infer_tools.f0_static import static_f0_time
from modules.vocoders.nsf_hifigan import NsfHifiGAN
from preprocessing.hubertinfer import HubertEncoder
from preprocessing.process_pipeline import File2Batch
from preprocessing.process_pipeline import get_pitch_parselmouth, get_pitch_crepe
from utils.hparams import hparams
from utils.hparams import set_hparams
from utils.indexed_datasets import IndexedDatasetBuilder
os.environ["OMP_NUM_THREADS"] = "1"
BASE_ITEM_ATTRIBUTES = ['wav_fn', 'spk_id']
class SvcBinarizer:
'''
Base class for data processing.
1. *process* and *process_data_split*:
process entire data, generate the train-test split (support parallel processing);
2. *process_item*:
process singe piece of data;
3. *get_pitch*:
infer the pitch using some algorithm;
4. *get_align*:
get the alignment using 'mel2ph' format (see https://arxiv.org/abs/1905.09263).
5. phoneme encoder, voice encoder, etc.
Subclasses should define:
1. *load_metadata*:
how to read multiple datasets from files;
2. *train_item_names*, *valid_item_names*, *test_item_names*:
how to split the dataset;
3. load_ph_set:
the phoneme set.
'''
def __init__(self, data_dir=None, item_attributes=None):
self.spk_map = None
self.vocoder = NsfHifiGAN()
self.phone_encoder = HubertEncoder(pt_path=hparams['hubert_path'])
if item_attributes is None:
item_attributes = BASE_ITEM_ATTRIBUTES
if data_dir is None:
data_dir = hparams['raw_data_dir']
if 'speakers' not in hparams:
speakers = hparams['datasets']
hparams['speakers'] = hparams['datasets']
else:
speakers = hparams['speakers']
assert isinstance(speakers, list), 'Speakers must be a list'
assert len(speakers) == len(set(speakers)), 'Speakers cannot contain duplicate names'
self.raw_data_dirs = data_dir if isinstance(data_dir, list) else [data_dir]
assert len(speakers) == len(self.raw_data_dirs), \
'Number of raw data dirs must equal number of speaker names!'
self.speakers = speakers
self.binarization_args = hparams['binarization_args']
self.items = {}
# every item in self.items has some attributes
self.item_attributes = item_attributes
# load each dataset
for ds_id, data_dir in enumerate(self.raw_data_dirs):
self.load_meta_data(data_dir, ds_id)
if ds_id == 0:
# check program correctness
assert all([attr in self.item_attributes for attr in list(self.items.values())[0].keys()])
self.item_names = sorted(list(self.items.keys()))
if self.binarization_args['shuffle']:
random.seed(hparams['seed'])
random.shuffle(self.item_names)
# set default get_pitch algorithm
if hparams['use_crepe']:
self.get_pitch_algorithm = get_pitch_crepe
else:
self.get_pitch_algorithm = get_pitch_parselmouth
print('spkers: ', set(self.speakers))
self._train_item_names, self._test_item_names = self.split_train_test_set(self.item_names)
@staticmethod
def split_train_test_set(item_names):
auto_test = item_names[-5:]
item_names = set(deepcopy(item_names))
if hparams['choose_test_manually']:
prefixes = set([str(pr) for pr in hparams['test_prefixes']])
test_item_names = set()
# Add prefixes that specified speaker index and matches exactly item name to test set
for prefix in deepcopy(prefixes):
if prefix in item_names:
test_item_names.add(prefix)
prefixes.remove(prefix)
# Add prefixes that exactly matches item name without speaker id to test set
for prefix in deepcopy(prefixes):
for name in item_names:
if name.split(':')[-1] == prefix:
test_item_names.add(name)
prefixes.remove(prefix)
# Add names with one of the remaining prefixes to test set
for prefix in deepcopy(prefixes):
for name in item_names:
if name.startswith(prefix):
test_item_names.add(name)
prefixes.remove(prefix)
for prefix in prefixes:
for name in item_names:
if name.split(':')[-1].startswith(prefix):
test_item_names.add(name)
test_item_names = sorted(list(test_item_names))
else:
test_item_names = auto_test
train_item_names = [x for x in item_names if x not in set(test_item_names)]
logging.info("train {}".format(len(train_item_names)))
logging.info("test {}".format(len(test_item_names)))
return train_item_names, test_item_names
@property
def train_item_names(self):
return self._train_item_names
@property
def valid_item_names(self):
return self._test_item_names
@property
def test_item_names(self):
return self._test_item_names
def load_meta_data(self, raw_data_dir, ds_id):
self.items.update(File2Batch.file2temporary_dict(raw_data_dir, ds_id))
@staticmethod
def build_spk_map():
spk_map = {x: i for i, x in enumerate(hparams['speakers'])}
assert len(spk_map) <= hparams['num_spk'], 'Actual number of speakers should be smaller than num_spk!'
return spk_map
def item_name2spk_id(self, item_name):
return self.spk_map[self.items[item_name]['spk_id']]
def meta_data_iterator(self, prefix):
if prefix == 'valid':
item_names = self.valid_item_names
elif prefix == 'test':
item_names = self.test_item_names
else:
item_names = self.train_item_names
for item_name in item_names:
meta_data = self.items[item_name]
yield item_name, meta_data
def process(self):
os.makedirs(hparams['binary_data_dir'], exist_ok=True)
self.spk_map = self.build_spk_map()
print("| spk_map: ", self.spk_map)
spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json"
json.dump(self.spk_map, open(spk_map_fn, 'w', encoding='utf-8'))
self.process_data_split('valid')
self.process_data_split('test')
self.process_data_split('train')
def process_data_split(self, prefix):
data_dir = hparams['binary_data_dir']
args = []
builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
lengths = []
total_sec = 0
if self.binarization_args['with_spk_embed']:
voice_encoder = VoiceEncoder().cuda()
for item_name, meta_data in self.meta_data_iterator(prefix):
args.append([item_name, meta_data, self.binarization_args])
spec_min = []
spec_max = []
f0_dict = {}
# code for single cpu processing
for i in tqdm(reversed(range(len(args))), total=len(args)):
a = args[i]
item = self.process_item(*a)
if item is None:
continue
item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \
if self.binarization_args['with_spk_embed'] else None
spec_min.append(item['spec_min'])
spec_max.append(item['spec_max'])
f0_dict[item['wav_fn']] = item['f0']
builder.add_item(item)
lengths.append(item['len'])
total_sec += item['sec']
if prefix == 'train':
spec_max = np.max(spec_max, 0)
spec_min = np.min(spec_min, 0)
pitch_time = static_f0_time(f0_dict)
with open(hparams['config_path'], encoding='utf-8') as f:
_hparams = yaml.safe_load(f)
_hparams['spec_max'] = spec_max.tolist()
_hparams['spec_min'] = spec_min.tolist()
if self.speakers == 1:
_hparams['f0_static'] = json.dumps(pitch_time)
with open(hparams['config_path'], 'w', encoding='utf-8') as f:
yaml.safe_dump(_hparams, f)
builder.finalize()
np.save(f'{data_dir}/{prefix}_lengths.npy', lengths)
print(f"| {prefix} total duration: {total_sec:.3f}s")
def process_item(self, item_name, meta_data, binarization_args):
from preprocessing.process_pipeline import File2Batch
return File2Batch.temporary_dict2processed_input(item_name, meta_data, self.phone_encoder)
if __name__ == "__main__":
set_hparams()
SvcBinarizer().process()