import os
os.environ["OMP_NUM_THREADS"] = "1"
import torch
from collections import Counter
from utils.text_encoder import TokenTextEncoder
from data_gen.tts.emotion import inference as EmotionEncoder
from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance
from data_gen.tts.emotion.inference import preprocess_wav
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, is_sil_phoneme
from utils.hparams import hparams, set_hparams
import numpy as np
from utils.indexed_datasets import IndexedDatasetBuilder
from vocoders.base_vocoder import get_vocoder_cls
import pandas as pd
class BinarizationError(Exception):
pass
class EmotionBinarizer:
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.item2txt = {}
self.item2ph = {}
self.item2wavfn = {}
self.item2tgfn = {}
self.item2spk = {}
self.item2emo = {}
def load_meta_data(self):
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 tqdm(self.meta_df.iterrows(), desc='Loading meta data.'):
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] = r['wav_fn']
self.item2spk[item_name] = r.get('spk_name', 'SPK1') \
if self.binarization_args['with_spk_id'] else 'SPK1'
if len(self.processed_data_dirs) > 1:
self.item2spk[item_name] = f"ds{ds_id}_{self.item2spk[item_name]}"
self.item2tgfn[item_name] = f"{processed_data_dir}/mfa_outputs/{raw_item_name}.TextGrid"
self.item2emo[item_name] = r.get('others', '"Neutral"')
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']:]
@property
def valid_item_names(self):
return self.item_names[:hparams['test_num']]
@property
def test_item_names(self):
return self.valid_item_names
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)))}
print("| #Spk: ", len(spk_map))
assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map)
return spk_map
def build_emo_map(self):
emo_map = set()
for item_name in self.item_names:
emo_name = self.item2emo[item_name]
emo_map.add(emo_name)
emo_map = {x: i for i, x in enumerate(sorted(list(emo_map)))}
print("| #Emo: ", len(emo_map))
return emo_map
def item_name2spk_id(self, item_name):
return self.spk_map[self.item2spk[item_name]]
def item_name2emo_id(self, item_name):
return self.emo_map[self.item2emo[item_name]]
def _phone_encoder(self):
ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json"
ph_set = []
if self.binarization_args['reset_phone_dict'] or not os.path.exists(ph_set_fn):
for ph_sent in self.item2ph.values():
ph_set += ph_sent.split(' ')
ph_set = sorted(set(ph_set))
json.dump(ph_set, open(ph_set_fn, 'w'))
print("| Build phone set: ", ph_set)
else:
ph_set = json.load(open(ph_set_fn, 'r'))
print("| Load phone set: ", ph_set)
return build_phone_encoder(hparams['binary_data_dir'])
def _word_encoder(self):
fn = f"{hparams['binary_data_dir']}/word_set.json"
word_set = []
if self.binarization_args['reset_word_dict']:
for word_sent in self.item2txt.values():
word_set += [x for x in word_sent.split(' ') if x != '']
word_set = Counter(word_set)
total_words = sum(word_set.values())
word_set = word_set.most_common(hparams['word_size'])
num_unk_words = total_words - sum([x[1] for x in word_set])
word_set = [x[0] for x in word_set]
json.dump(word_set, open(fn, 'w'))
print(f"| Build word set. Size: {len(word_set)}, #total words: {total_words},"
f" #unk_words: {num_unk_words}, word_set[:10]:, {word_set[:10]}.")
else:
word_set = json.load(open(fn, 'r'))
print("| Load word set. Size: ", len(word_set), word_set[:10])
return TokenTextEncoder(None, vocab_list=word_set, replace_oov='<UNK>')
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)
emotion = self.item_name2emo_id(item_name)
yield item_name, ph, txt, tg_fn, wav_fn, spk_id, emotion
def process(self):
self.load_meta_data()
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.emo_map = self.build_emo_map()
print("| emo_map: ", self.emo_map)
emo_map_fn = f"{hparams['binary_data_dir']}/emo_map.json"
json.dump(self.emo_map, open(emo_map_fn, 'w'))
self.phone_encoder = self._phone_encoder()
self.word_encoder = None
EmotionEncoder.load_model(hparams['emotion_encoder_path'])
if self.binarization_args['with_word']:
self.word_encoder = self._word_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}')
ph_lengths = []
mel_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.word_encoder), self.binarization_args])
num_workers = self.num_workers
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
processed_wav = preprocess_wav(item['wav_fn'])
item['emo_embed'] = Embed_utterance(processed_wav)
if not self.binarization_args['with_wav'] and 'wav' in item:
del item['wav']
builder.add_item(item)
mel_lengths.append(item['len'])
if 'ph_len' in item:
ph_lengths.append(item['ph_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', mel_lengths)
if len(ph_lengths) > 0:
np.save(f'{data_dir}/{prefix}_ph_lengths.npy', ph_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, emotion, encoder, binarization_args):
res = {'item_name': item_name, 'txt': txt, 'ph': ph, 'wav_fn': wav_fn, 'spk_id': spk_id, 'emotion': emotion}
if binarization_args['with_linear']:
wav, mel, linear_stft = get_vocoder_cls(hparams).wav2spec(wav_fn) # , return_linear=True
res['linear'] = linear_stft
else:
wav, mel = get_vocoder_cls(hparams).wav2spec(wav_fn)
wav = wav.astype(np.float16)
res.update({'mel': mel, 'wav': wav,
'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]})
try:
if binarization_args['with_f0']:
cls.get_pitch(res)
if binarization_args['with_f0cwt']:
cls.get_f0cwt(res)
if binarization_args['with_txt']:
ph_encoder, word_encoder = encoder
try:
res['phone'] = ph_encoder.encode(ph)
res['ph_len'] = len(res['phone'])
except:
traceback.print_exc()
raise BinarizationError(f"Empty phoneme")
if binarization_args['with_align']:
cls.get_align(tg_fn, res)
if binarization_args['trim_eos_bos']:
bos_dur = res['dur'][0]
eos_dur = res['dur'][-1]
res['mel'] = mel[bos_dur:-eos_dur]
res['f0'] = res['f0'][bos_dur:-eos_dur]
res['pitch'] = res['pitch'][bos_dur:-eos_dur]
res['mel2ph'] = res['mel2ph'][bos_dur:-eos_dur]
res['wav'] = wav[bos_dur * hparams['hop_size']:-eos_dur * hparams['hop_size']]
res['dur'] = res['dur'][1:-1]
res['len'] = res['mel'].shape[0]
if binarization_args['with_word']:
cls.get_word(res, word_encoder)
except BinarizationError as e:
print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {wav_fn}")
return None
except Exception as e:
traceback.print_exc()
print(f"| Skip item. item_name: {item_name}, wav_fn: {wav_fn}")
return None
return res
@staticmethod
def get_align(tg_fn, res):
ph = res['ph']
mel = res['mel']
phone_encoded = res['phone']
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(res):
wav, mel = res['wav'], res['mel']
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(res):
from utils.cwt import get_cont_lf0, get_lf0_cwt
f0 = res['f0']
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
@staticmethod
def get_word(res, word_encoder):
ph_split = res['ph'].split(" ")
# ph side mapping to word
ph_words = [] # ['<BOS>', 'N_AW1_', ',', 'AE1_Z_|', 'AO1_L_|', 'B_UH1_K_S_|', 'N_AA1_T_|', ....]
ph2word = np.zeros([len(ph_split)], dtype=int)
last_ph_idx_for_word = [] # [2, 11, ...]
for i, ph in enumerate(ph_split):
if ph == '|':
last_ph_idx_for_word.append(i)
elif not ph[0].isalnum():
if ph not in ['<BOS>']:
last_ph_idx_for_word.append(i - 1)
last_ph_idx_for_word.append(i)
start_ph_idx_for_word = [0] + [i + 1 for i in last_ph_idx_for_word[:-1]]
for i, (s_w, e_w) in enumerate(zip(start_ph_idx_for_word, last_ph_idx_for_word)):
ph_words.append(ph_split[s_w:e_w + 1])
ph2word[s_w:e_w + 1] = i
ph2word = ph2word.tolist()
ph_words = ["_".join(w) for w in ph_words]
# mel side mapping to word
mel2word = []
dur_word = [0 for _ in range(len(ph_words))]
for i, m2p in enumerate(res['mel2ph']):
word_idx = ph2word[m2p - 1]
mel2word.append(ph2word[m2p - 1])
dur_word[word_idx] += 1
ph2word = [x + 1 for x in ph2word] # 0预留给padding
mel2word = [x + 1 for x in mel2word] # 0预留给padding
res['ph_words'] = ph_words # [T_word]
res['ph2word'] = ph2word # [T_ph]
res['mel2word'] = mel2word # [T_mel]
res['dur_word'] = dur_word # [T_word]
words = [x for x in res['txt'].split(" ") if x != '']
while len(words) > 0 and is_sil_phoneme(words[0]):
words = words[1:]
while len(words) > 0 and is_sil_phoneme(words[-1]):
words = words[:-1]
words = ['<BOS>'] + words + ['<EOS>']
word_tokens = word_encoder.encode(" ".join(words))
res['words'] = words
res['word_tokens'] = word_tokens
assert len(words) == len(ph_words), [words, ph_words]
@property
def num_workers(self):
return int(os.getenv('N_PROC', hparams.get('N_PROC', os.cpu_count())))
if __name__ == "__main__":
set_hparams()
EmotionBinarizer().process()