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Zero
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# Copyright 2025 ByteDance and/or its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn.functional as F
def map_phone_to_tokendict(item, pad_bos_eos=True):
# Merge Chinese phone and tone (Original dict ends at 173, i.e., ph_dict_size=173). 146~173 is punctuations.
phone = item['txt_token'].clone()
merged_phone = item['txt_token'].clone()
tone_tmp = item['tone'].clone()
# In tone_dict, tone_1 is 4, tone_2 is 11, tone_3 is 12, tone_4 is 13, tone_5 is 14, tone_6 is 15
tone_tmp[tone_tmp==4] = 1
tone_tmp[tone_tmp==11] = 2
tone_tmp[tone_tmp==12] = 3
tone_tmp[tone_tmp==13] = 4
tone_tmp[tone_tmp==14] = 5
tone_tmp[tone_tmp==15] = 6
# Chinese phones lie in 3~100 in the phone_dict, we map them to 200~788
ch_phone_idx = (phone >= 3) & (phone <= 100)
merged_phone[ch_phone_idx] = (merged_phone[ch_phone_idx] - 3) * 6 + 200 + tone_tmp[ch_phone_idx]
if pad_bos_eos:
merged_phone = F.pad(merged_phone, (1, 0), mode='constant', value=798)
merged_phone = F.pad(merged_phone, (0, 1), mode='constant', value=799)
return merged_phone
def split_ph_timestamp(ph_timestamp):
''' Input: ph_timestamp, shape [T] '''
# Map the timestamp of each phone back to its original frame-level lengths
ph_timestamp[ph_timestamp >= 800] -= 800
ph_list = []
tone_list = []
dur_list = []
cur_timestamp = 0
for idx, item in enumerate(ph_timestamp):
if idx % 2 == 0:
# Map Chinese phones back to its original phone_dict
if (200 <= item <= 788):
ph = (item - 200 - 1) // 6 + 3
tone = (item - 200 - 1) % 6 + 1
if tone == 1:
tone = 4
else:
tone = tone + 9
# Set English tone to '3'
else:
ph = item
tone = 3
ph_list.append(ph)
tone_list.append(tone)
else:
dur_list.append((item - cur_timestamp))
cur_timestamp = item
assert len(ph_list) == len(dur_list), f"{len(ph_list)}, {len(dur_list)}"
ph_seq, tone_seq, dur_seq = torch.LongTensor(ph_list), torch.LongTensor(tone_list), torch.LongTensor(dur_list)
return ph_seq, tone_seq, dur_seq, ph_timestamp[-1]
def split_ph(ph_seq):
''' Input: ph_timestamp, shape [T] '''
ph_list = []
tone_list = []
for idx, item in enumerate(ph_seq):
# Map Chinese phones back to its original phone_dict
if (200 <= item <= 788):
ph = (item - 200 - 1) // 6 + 3
tone = (item - 200 - 1) % 6 + 1
if tone == 1:
tone = 4
else:
tone = tone + 9
# Set English tone to '3'
else:
ph = item
tone = 3
ph_list.append(ph)
tone_list.append(tone)
assert len(ph_list) == len(tone_list)
ph_seq, tone_seq = torch.LongTensor(ph_list), torch.LongTensor(tone_list)
return ph_seq, tone_seq |