import os
os.environ["OMP_NUM_THREADS"] = "1"
from utils.multiprocess_utils import chunked_multiprocess_run
import random
import traceback
import json
from resemblyzer import VoiceEncoder
from tqdm import tqdm
from data_gen.tts.data_gen_utils import get_mel2ph, get_pitch, build_phone_encoder
from utils.hparams import set_hparams, hparams
import numpy as np
from utils.indexed_datasets import IndexedDatasetBuilder
from vocoders.base_vocoder import VOCODERS
import pandas as pd
class BinarizationError(Exception):
pass
class BaseBinarizer:
def __init__(self, processed_data_dir=None):
if processed_data_dir is None:
processed_data_dir = hparams['processed_data_dir']
self.processed_data_dirs = processed_data_dir.split(",")
self.binarization_args = hparams['binarization_args']
self.pre_align_args = hparams['pre_align_args']
self.forced_align = self.pre_align_args['forced_align']
tg_dir = None
if self.forced_align == 'mfa':
tg_dir = 'mfa_outputs'
if self.forced_align == 'kaldi':
tg_dir = 'kaldi_outputs'
self.item2txt = {}
self.item2ph = {}
self.item2wavfn = {}
self.item2tgfn = {}
self.item2spk = {}
for ds_id, processed_data_dir in enumerate(self.processed_data_dirs):
self.meta_df = pd.read_csv(f"{processed_data_dir}/metadata_phone.csv", dtype=str)
for r_idx, r in self.meta_df.iterrows():
item_name = raw_item_name = r['item_name']
if len(self.processed_data_dirs) > 1:
item_name = f'ds{ds_id}_{item_name}'
self.item2txt[item_name] = r['txt']
self.item2ph[item_name] = r['ph']
self.item2wavfn[item_name] = os.path.join(hparams['raw_data_dir'], 'wavs', os.path.basename(r['wav_fn']).split('_')[1])
self.item2spk[item_name] = r.get('spk', 'SPK1')
if len(self.processed_data_dirs) > 1:
self.item2spk[item_name] = f"ds{ds_id}_{self.item2spk[item_name]}"
if tg_dir is not None:
self.item2tgfn[item_name] = f"{processed_data_dir}/{tg_dir}/{raw_item_name}.TextGrid"
self.item_names = sorted(list(self.item2txt.keys()))
if self.binarization_args['shuffle']:
random.seed(1234)
random.shuffle(self.item_names)
@property
def train_item_names(self):
return self.item_names[hparams['test_num']+hparams['valid_num']:]
@property
def valid_item_names(self):
return self.item_names[0: hparams['test_num']+hparams['valid_num']] #
@property
def test_item_names(self):
return self.item_names[0: hparams['test_num']] # Audios for MOS testing are in 'test_ids'
def build_spk_map(self):
spk_map = set()
for item_name in self.item_names:
spk_name = self.item2spk[item_name]
spk_map.add(spk_name)
spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))}
assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map)
return spk_map
def item_name2spk_id(self, item_name):
return self.spk_map[self.item2spk[item_name]]
def _phone_encoder(self):
ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json"
ph_set = []
if hparams['reset_phone_dict'] or not os.path.exists(ph_set_fn):
for processed_data_dir in self.processed_data_dirs:
ph_set += [x.split(' ')[0] for x in open(f'{processed_data_dir}/dict.txt').readlines()]
ph_set = sorted(set(ph_set))
json.dump(ph_set, open(ph_set_fn, 'w'))
else:
ph_set = json.load(open(ph_set_fn, 'r'))
print("| phone set: ", ph_set)
return build_phone_encoder(hparams['binary_data_dir'])
def meta_data(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:
ph = self.item2ph[item_name]
txt = self.item2txt[item_name]
tg_fn = self.item2tgfn.get(item_name)
wav_fn = self.item2wavfn[item_name]
spk_id = self.item_name2spk_id(item_name)
yield item_name, ph, txt, tg_fn, wav_fn, spk_id
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'))
self.phone_encoder = self._phone_encoder()
self.process_data('valid')
self.process_data('test')
self.process_data('train')
def process_data(self, prefix):
data_dir = hparams['binary_data_dir']
args = []
builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
lengths = []
f0s = []
total_sec = 0
if self.binarization_args['with_spk_embed']:
voice_encoder = VoiceEncoder().cuda()
meta_data = list(self.meta_data(prefix))
for m in meta_data:
args.append(list(m) + [self.phone_encoder, self.binarization_args])
num_workers = int(os.getenv('N_PROC', os.cpu_count() // 3))
for f_id, (_, item) in enumerate(
zip(tqdm(meta_data), chunked_multiprocess_run(self.process_item, args, num_workers=num_workers))):
if item is None:
continue
item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \
if self.binarization_args['with_spk_embed'] else None
if not self.binarization_args['with_wav'] and 'wav' in item:
print("del wav")
del item['wav']
builder.add_item(item)
lengths.append(item['len'])
total_sec += item['sec']
if item.get('f0') is not None:
f0s.append(item['f0'])
builder.finalize()
np.save(f'{data_dir}/{prefix}_lengths.npy', lengths)
if len(f0s) > 0:
f0s = np.concatenate(f0s, 0)
f0s = f0s[f0s != 0]
np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()])
print(f"| {prefix} total duration: {total_sec:.3f}s")
@classmethod
def process_item(cls, item_name, ph, txt, tg_fn, wav_fn, spk_id, encoder, binarization_args):
if hparams['vocoder'] in VOCODERS:
wav, mel = VOCODERS[hparams['vocoder']].wav2spec(wav_fn)
else:
wav, mel = VOCODERS[hparams['vocoder'].split('.')[-1]].wav2spec(wav_fn)
res = {
'item_name': item_name, 'txt': txt, 'ph': ph, 'mel': mel, 'wav': wav, 'wav_fn': wav_fn,
'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0], 'spk_id': spk_id
}
try:
if binarization_args['with_f0']:
cls.get_pitch(wav, mel, res)
if binarization_args['with_f0cwt']:
cls.get_f0cwt(res['f0'], res)
if binarization_args['with_txt']:
try:
phone_encoded = res['phone'] = encoder.encode(ph)
except:
traceback.print_exc()
raise BinarizationError(f"Empty phoneme")
if binarization_args['with_align']:
cls.get_align(tg_fn, ph, mel, phone_encoded, res)
except BinarizationError as e:
print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {wav_fn}")
return None
return res
@staticmethod
def get_align(tg_fn, ph, mel, phone_encoded, res):
if tg_fn is not None and os.path.exists(tg_fn):
mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams)
else:
raise BinarizationError(f"Align not found")
if mel2ph.max() - 1 >= len(phone_encoded):
raise BinarizationError(
f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}")
res['mel2ph'] = mel2ph
res['dur'] = dur
@staticmethod
def get_pitch(wav, mel, res):
f0, pitch_coarse = get_pitch(wav, mel, hparams)
if sum(f0) == 0:
raise BinarizationError("Empty f0")
res['f0'] = f0
res['pitch'] = pitch_coarse
@staticmethod
def get_f0cwt(f0, res):
from utils.cwt import get_cont_lf0, get_lf0_cwt
uv, cont_lf0_lpf = get_cont_lf0(f0)
logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf)
cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org
Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)
if np.any(np.isnan(Wavelet_lf0)):
raise BinarizationError("NaN CWT")
res['cwt_spec'] = Wavelet_lf0
res['cwt_scales'] = scales
res['f0_mean'] = logf0s_mean_org
res['f0_std'] = logf0s_std_org
if __name__ == "__main__":
set_hparams()
BaseBinarizer().process()