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