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()