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import re
import torch
import numpy as np
from textgrid import TextGrid
from text_to_speech.utils.text.text_encoder import is_sil_phoneme
def get_mel2ph(tg_fn, ph, mel, hop_size, audio_sample_rate, min_sil_duration=0):
ph_list = ph.split(" ")
itvs = TextGrid.fromFile(tg_fn)[1]
itvs_ = []
for i in range(len(itvs)):
if itvs[i].maxTime - itvs[i].minTime < min_sil_duration and i > 0 and is_sil_phoneme(itvs[i].mark):
itvs_[-1].maxTime = itvs[i].maxTime
else:
itvs_.append(itvs[i])
itvs.intervals = itvs_
itv_marks = [itv.mark for itv in itvs]
tg_len = len([x for x in itvs if not is_sil_phoneme(x.mark)])
ph_len = len([x for x in ph_list if not is_sil_phoneme(x)])
assert tg_len == ph_len, (tg_len, ph_len, itv_marks, ph_list, tg_fn)
mel2ph = np.zeros([mel.shape[0]], int)
# to process aishell3_no_tone
# for _ in range(10):
# if itvs[-2].mark == '':
# start_time = itvs[-2].minTime
# end_time = itvs[-1].maxTime
# mark = itvs[-1].mark
# itvs[-2].maxTime = end_time
# itvs[-2].mark = mark
# itvs_ = []
# for i in range(len(itvs)-1):
# itvs_.append(itvs[i])
# itvs.intervals = itvs_
# if itvs[-1].mark == '':
# start_time = itvs[-2].minTime
# end_time = itvs[-1].maxTime
# itvs[-2].maxTime = end_time
# itvs_ = []
# for i in range(len(itvs)-1):
# itvs_.append(itvs[i])
# itvs.intervals = itvs_
i_itv = 0
i_ph = 0
while i_itv < len(itvs):
itv = itvs[i_itv]
ph = ph_list[i_ph]
itv_ph = itv.mark
start_frame = int(itv.minTime * audio_sample_rate / hop_size + 0.5)
end_frame = int(itv.maxTime * audio_sample_rate / hop_size + 0.5)
if is_sil_phoneme(itv_ph) and not is_sil_phoneme(ph):
mel2ph[start_frame:end_frame] = i_ph
i_itv += 1
elif not is_sil_phoneme(itv_ph) and is_sil_phoneme(ph):
i_ph += 1
else:
if not ((is_sil_phoneme(itv_ph) and is_sil_phoneme(ph)) \
or re.sub(r'\d+', '', itv_ph.lower()) == re.sub(r'\d+', '', ph.lower())):
print(f"| WARN: {tg_fn} phs are not same: ", itv_ph, ph, itv_marks, ph_list)
mel2ph[start_frame:end_frame] = i_ph + 1
i_ph += 1
i_itv += 1
mel2ph[-1] = mel2ph[-2]
assert not np.any(mel2ph == 0)
T_t = len(ph_list)
dur = mel2token_to_dur(mel2ph, T_t)
return mel2ph.tolist(), dur.tolist()
def split_audio_by_mel2ph(audio, mel2ph, hop_size, audio_num_mel_bins):
if isinstance(audio, torch.Tensor):
audio = audio.numpy()
if isinstance(mel2ph, torch.Tensor):
mel2ph = mel2ph.numpy()
assert len(audio.shape) == 1, len(mel2ph.shape) == 1
split_locs = []
for i in range(1, len(mel2ph)):
if mel2ph[i] != mel2ph[i - 1]:
split_loc = i * hop_size
split_locs.append(split_loc)
new_audio = []
for i in range(len(split_locs) - 1):
new_audio.append(audio[split_locs[i]:split_locs[i + 1]])
new_audio.append(np.zeros([0.5 * audio_num_mel_bins]))
return np.concatenate(new_audio)
def mel2token_to_dur(mel2token, T_txt=None, max_dur=None):
is_torch = isinstance(mel2token, torch.Tensor)
has_batch_dim = True
if not is_torch:
mel2token = torch.LongTensor(mel2token)
if T_txt is None:
T_txt = mel2token.max()
if len(mel2token.shape) == 1:
mel2token = mel2token[None, ...]
has_batch_dim = False
B, _ = mel2token.shape
dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token))
dur = dur[:, 1:]
if max_dur is not None:
dur = dur.clamp(max=max_dur)
if not is_torch:
dur = dur.numpy()
if not has_batch_dim:
dur = dur[0]
return dur
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