NeuralSeq/data_gen/tts/base_binarizer_emotion.py

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()