Spaces:
Build error
Build error
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', '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) | |
def train_item_names(self): | |
return self.item_names[hparams['test_num']:] | |
def valid_item_names(self): | |
return self.item_names[:hparams['test_num']] | |
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") | |
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 | |
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 | |
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 | |
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 | |
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] | |
def num_workers(self): | |
return int(os.getenv('N_PROC', hparams.get('N_PROC', os.cpu_count()))) | |
if __name__ == "__main__": | |
set_hparams() | |
EmotionBinarizer().process() | |