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PaddleSpeech/paddlespeech/t2s/datasets/am_batch_fn.py

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 numpy as np
import paddle
from paddlespeech.t2s.datasets.batch import batch_sequences
from paddlespeech.t2s.modules.nets_utils import get_seg_pos
from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask
from paddlespeech.t2s.modules.nets_utils import phones_masking
from paddlespeech.t2s.modules.nets_utils import phones_text_masking
# 因为要传参数，所以需要额外构建
def build_erniesat_collate_fn(mlm_prob: float=0.8,
mean_phn_span: int=8,
seg_emb: bool=False,
text_masking: bool=False):
return ErnieSATCollateFn(
mlm_prob=mlm_prob,
mean_phn_span=mean_phn_span,
seg_emb=seg_emb,
text_masking=text_masking)
class ErnieSATCollateFn:
"""Functor class of common_collate_fn()"""
def __init__(self,
mlm_prob: float=0.8,
mean_phn_span: int=8,
seg_emb: bool=False,
text_masking: bool=False):
self.mlm_prob = mlm_prob
self.mean_phn_span = mean_phn_span
self.seg_emb = seg_emb
self.text_masking = text_masking
def __call__(self, exmaples):
return erniesat_batch_fn(
exmaples,
mlm_prob=self.mlm_prob,
mean_phn_span=self.mean_phn_span,
seg_emb=self.seg_emb,
text_masking=self.text_masking)
def erniesat_batch_fn(examples,
mlm_prob: float=0.8,
mean_phn_span: int=8,
seg_emb: bool=False,
text_masking: bool=False):
# fields = ["text", "text_lengths", "speech", "speech_lengths", "align_start", "align_end"]
text = [np.array(item["text"], dtype=np.int64) for item in examples]
speech = [np.array(item["speech"], dtype=np.float32) for item in examples]
text_lengths = [
np.array(item["text_lengths"], dtype=np.int64) for item in examples
]
speech_lengths = [
np.array(item["speech_lengths"], dtype=np.int64) for item in examples
]
align_start = [
np.array(item["align_start"], dtype=np.int64) for item in examples
]
align_end = [
np.array(item["align_end"], dtype=np.int64) for item in examples
]
align_start_lengths = [
np.array(len(item["align_start"]), dtype=np.int64) for item in examples
]
# add_pad
text = batch_sequences(text)
speech = batch_sequences(speech)
align_start = batch_sequences(align_start)
align_end = batch_sequences(align_end)
# convert each batch to paddle.Tensor
text = paddle.to_tensor(text)
speech = paddle.to_tensor(speech)
text_lengths = paddle.to_tensor(text_lengths)
speech_lengths = paddle.to_tensor(speech_lengths)
align_start_lengths = paddle.to_tensor(align_start_lengths)
speech_pad = speech
text_pad = text
text_mask = make_non_pad_mask(
text_lengths, text_pad, length_dim=1).unsqueeze(-2)
speech_mask = make_non_pad_mask(
speech_lengths, speech_pad[:, :, 0], length_dim=1).unsqueeze(-2)
# for training
span_bdy = None
# for inference
if 'span_bdy' in examples[0].keys():
span_bdy = [
np.array(item["span_bdy"], dtype=np.int64) for item in examples
]
span_bdy = paddle.to_tensor(span_bdy)
# dual_mask 的是混合中英时候同时 mask 语音和文本
# ernie sat 在实现跨语言的时候都 mask 了
if text_masking:
masked_pos, text_masked_pos = phones_text_masking(
xs_pad=speech_pad,
src_mask=speech_mask,
text_pad=text_pad,
text_mask=text_mask,
align_start=align_start,
align_end=align_end,
align_start_lens=align_start_lengths,
mlm_prob=mlm_prob,
mean_phn_span=mean_phn_span,
span_bdy=span_bdy)
# 训练纯中文和纯英文的 -> a3t 没有对 phoneme 做 mask, 只对语音 mask 了
# a3t 和 ernie sat 的区别主要在于做 mask 的时候
else:
masked_pos = phones_masking(
xs_pad=speech_pad,
src_mask=speech_mask,
align_start=align_start,
align_end=align_end,
align_start_lens=align_start_lengths,
mlm_prob=mlm_prob,
mean_phn_span=mean_phn_span,
span_bdy=span_bdy)
text_masked_pos = paddle.zeros(paddle.shape(text_pad))
speech_seg_pos, text_seg_pos = get_seg_pos(
speech_pad=speech_pad,
text_pad=text_pad,
align_start=align_start,
align_end=align_end,
align_start_lens=align_start_lengths,
seg_emb=seg_emb)
batch = {
"text": text,
"speech": speech,
# need to generate
"masked_pos": masked_pos,
"speech_mask": speech_mask,
"text_mask": text_mask,
"speech_seg_pos": speech_seg_pos,
"text_seg_pos": text_seg_pos,
"text_masked_pos": text_masked_pos
}
return batch
def tacotron2_single_spk_batch_fn(examples):
# fields = ["text", "text_lengths", "speech", "speech_lengths"]
text = [np.array(item["text"], dtype=np.int64) for item in examples]
speech = [np.array(item["speech"], dtype=np.float32) for item in examples]
text_lengths = [
np.array(item["text_lengths"], dtype=np.int64) for item in examples
]
speech_lengths = [
np.array(item["speech_lengths"], dtype=np.int64) for item in examples
]
text = batch_sequences(text)
speech = batch_sequences(speech)
# convert each batch to paddle.Tensor
text = paddle.to_tensor(text)
speech = paddle.to_tensor(speech)
text_lengths = paddle.to_tensor(text_lengths)
speech_lengths = paddle.to_tensor(speech_lengths)
batch = {
"text": text,
"text_lengths": text_lengths,
"speech": speech,
"speech_lengths": speech_lengths,
}
return batch
def tacotron2_multi_spk_batch_fn(examples):
# fields = ["text", "text_lengths", "speech", "speech_lengths"]
text = [np.array(item["text"], dtype=np.int64) for item in examples]
speech = [np.array(item["speech"], dtype=np.float32) for item in examples]
text_lengths = [
np.array(item["text_lengths"], dtype=np.int64) for item in examples
]
speech_lengths = [
np.array(item["speech_lengths"], dtype=np.int64) for item in examples
]
text = batch_sequences(text)
speech = batch_sequences(speech)
# convert each batch to paddle.Tensor
text = paddle.to_tensor(text)
speech = paddle.to_tensor(speech)
text_lengths = paddle.to_tensor(text_lengths)
speech_lengths = paddle.to_tensor(speech_lengths)
batch = {
"text": text,
"text_lengths": text_lengths,
"speech": speech,
"speech_lengths": speech_lengths,
}
# spk_emb has a higher priority than spk_id
if "spk_emb" in examples[0]:
spk_emb = [
np.array(item["spk_emb"], dtype=np.float32) for item in examples
]
spk_emb = batch_sequences(spk_emb)
spk_emb = paddle.to_tensor(spk_emb)
batch["spk_emb"] = spk_emb
elif "spk_id" in examples[0]:
spk_id = [np.array(item["spk_id"], dtype=np.int64) for item in examples]
spk_id = paddle.to_tensor(spk_id)
batch["spk_id"] = spk_id
return batch
def speedyspeech_single_spk_batch_fn(examples):
# fields = ["phones", "tones", "num_phones", "num_frames", "feats", "durations"]
phones = [np.array(item["phones"], dtype=np.int64) for item in examples]
tones = [np.array(item["tones"], dtype=np.int64) for item in examples]
feats = [np.array(item["feats"], dtype=np.float32) for item in examples]
durations = [
np.array(item["durations"], dtype=np.int64) for item in examples
]
num_phones = [
np.array(item["num_phones"], dtype=np.int64) for item in examples
]
num_frames = [
np.array(item["num_frames"], dtype=np.int64) for item in examples
]
phones = batch_sequences(phones)
tones = batch_sequences(tones)
feats = batch_sequences(feats)
durations = batch_sequences(durations)
# convert each batch to paddle.Tensor
phones = paddle.to_tensor(phones)
tones = paddle.to_tensor(tones)
feats = paddle.to_tensor(feats)
durations = paddle.to_tensor(durations)
num_phones = paddle.to_tensor(num_phones)
num_frames = paddle.to_tensor(num_frames)
batch = {
"phones": phones,
"tones": tones,
"num_phones": num_phones,
"num_frames": num_frames,
"feats": feats,
"durations": durations,
}
return batch
def speedyspeech_multi_spk_batch_fn(examples):
# fields = ["phones", "tones", "num_phones", "num_frames", "feats", "durations", "spk_id"]
phones = [np.array(item["phones"], dtype=np.int64) for item in examples]
tones = [np.array(item["tones"], dtype=np.int64) for item in examples]
feats = [np.array(item["feats"], dtype=np.float32) for item in examples]
durations = [
np.array(item["durations"], dtype=np.int64) for item in examples
]
num_phones = [
np.array(item["num_phones"], dtype=np.int64) for item in examples
]
num_frames = [
np.array(item["num_frames"], dtype=np.int64) for item in examples
]
phones = batch_sequences(phones)
tones = batch_sequences(tones)
feats = batch_sequences(feats)
durations = batch_sequences(durations)
# convert each batch to paddle.Tensor
phones = paddle.to_tensor(phones)
tones = paddle.to_tensor(tones)
feats = paddle.to_tensor(feats)
durations = paddle.to_tensor(durations)
num_phones = paddle.to_tensor(num_phones)
num_frames = paddle.to_tensor(num_frames)
batch = {
"phones": phones,
"tones": tones,
"num_phones": num_phones,
"num_frames": num_frames,
"feats": feats,
"durations": durations,
}
if "spk_id" in examples[0]:
spk_id = [np.array(item["spk_id"], dtype=np.int64) for item in examples]
spk_id = paddle.to_tensor(spk_id)
batch["spk_id"] = spk_id
return batch
def fastspeech2_single_spk_batch_fn(examples):
# fields = ["text", "text_lengths", "speech", "speech_lengths", "durations", "pitch", "energy"]
text = [np.array(item["text"], dtype=np.int64) for item in examples]
speech = [np.array(item["speech"], dtype=np.float32) for item in examples]
pitch = [np.array(item["pitch"], dtype=np.float32) for item in examples]
energy = [np.array(item["energy"], dtype=np.float32) for item in examples]
durations = [
np.array(item["durations"], dtype=np.int64) for item in examples
]
text_lengths = [
np.array(item["text_lengths"], dtype=np.int64) for item in examples
]
speech_lengths = [
np.array(item["speech_lengths"], dtype=np.int64) for item in examples
]
text = batch_sequences(text)
pitch = batch_sequences(pitch)
speech = batch_sequences(speech)
durations = batch_sequences(durations)
energy = batch_sequences(energy)
# convert each batch to paddle.Tensor
text = paddle.to_tensor(text)
pitch = paddle.to_tensor(pitch)
speech = paddle.to_tensor(speech)
durations = paddle.to_tensor(durations)
energy = paddle.to_tensor(energy)
text_lengths = paddle.to_tensor(text_lengths)
speech_lengths = paddle.to_tensor(speech_lengths)
batch = {
"text": text,
"text_lengths": text_lengths,
"durations": durations,
"speech": speech,
"speech_lengths": speech_lengths,
"pitch": pitch,
"energy": energy
}
return batch
def fastspeech2_multi_spk_batch_fn(examples):
# fields = ["text", "text_lengths", "speech", "speech_lengths", "durations", "pitch", "energy", "spk_id"/"spk_emb"]
text = [np.array(item["text"], dtype=np.int64) for item in examples]
speech = [np.array(item["speech"], dtype=np.float32) for item in examples]
pitch = [np.array(item["pitch"], dtype=np.float32) for item in examples]
energy = [np.array(item["energy"], dtype=np.float32) for item in examples]
durations = [
np.array(item["durations"], dtype=np.int64) for item in examples
]
text_lengths = [
np.array(item["text_lengths"], dtype=np.int64) for item in examples
]
speech_lengths = [
np.array(item["speech_lengths"], dtype=np.int64) for item in examples
]
text = batch_sequences(text)
pitch = batch_sequences(pitch)
speech = batch_sequences(speech)
durations = batch_sequences(durations)
energy = batch_sequences(energy)
# convert each batch to paddle.Tensor
text = paddle.to_tensor(text)
pitch = paddle.to_tensor(pitch)
speech = paddle.to_tensor(speech)
durations = paddle.to_tensor(durations)
energy = paddle.to_tensor(energy)
text_lengths = paddle.to_tensor(text_lengths)
speech_lengths = paddle.to_tensor(speech_lengths)
batch = {
"text": text,
"text_lengths": text_lengths,
"durations": durations,
"speech": speech,
"speech_lengths": speech_lengths,
"pitch": pitch,
"energy": energy
}
# spk_emb has a higher priority than spk_id
if "spk_emb" in examples[0]:
spk_emb = [
np.array(item["spk_emb"], dtype=np.float32) for item in examples
]
spk_emb = batch_sequences(spk_emb)
spk_emb = paddle.to_tensor(spk_emb)
batch["spk_emb"] = spk_emb
elif "spk_id" in examples[0]:
spk_id = [np.array(item["spk_id"], dtype=np.int64) for item in examples]
spk_id = paddle.to_tensor(spk_id)
batch["spk_id"] = spk_id
return batch
def transformer_single_spk_batch_fn(examples):
# fields = ["text", "text_lengths", "speech", "speech_lengths"]
text = [np.array(item["text"], dtype=np.int64) for item in examples]
speech = [np.array(item["speech"], dtype=np.float32) for item in examples]
text_lengths = [
np.array(item["text_lengths"], dtype=np.int64) for item in examples
]
speech_lengths = [
np.array(item["speech_lengths"], dtype=np.int64) for item in examples
]
text = batch_sequences(text)
speech = batch_sequences(speech)
# convert each batch to paddle.Tensor
text = paddle.to_tensor(text)
speech = paddle.to_tensor(speech)
text_lengths = paddle.to_tensor(text_lengths)
speech_lengths = paddle.to_tensor(speech_lengths)
batch = {
"text": text,
"text_lengths": text_lengths,
"speech": speech,
"speech_lengths": speech_lengths,
}
return batch
def vits_single_spk_batch_fn(examples):
"""
Returns:
Dict[str, Any]:
- text (Tensor): Text index tensor (B, T_text).
- text_lengths (Tensor): Text length tensor (B,).
- feats (Tensor): Feature tensor (B, T_feats, aux_channels).
- feats_lengths (Tensor): Feature length tensor (B,).
- speech (Tensor): Speech waveform tensor (B, T_wav).
"""
# fields = ["text", "text_lengths", "feats", "feats_lengths", "speech"]
text = [np.array(item["text"], dtype=np.int64) for item in examples]
feats = [np.array(item["feats"], dtype=np.float32) for item in examples]
speech = [np.array(item["wave"], dtype=np.float32) for item in examples]
text_lengths = [
np.array(item["text_lengths"], dtype=np.int64) for item in examples
]
feats_lengths = [
np.array(item["feats_lengths"], dtype=np.int64) for item in examples
]
text = batch_sequences(text)
feats = batch_sequences(feats)
speech = batch_sequences(speech)
# convert each batch to paddle.Tensor
text = paddle.to_tensor(text)
feats = paddle.to_tensor(feats)
text_lengths = paddle.to_tensor(text_lengths)
feats_lengths = paddle.to_tensor(feats_lengths)
batch = {
"text": text,
"text_lengths": text_lengths,
"feats": feats,
"feats_lengths": feats_lengths,
"speech": speech
}
return batch
def vits_multi_spk_batch_fn(examples):
"""
Returns:
Dict[str, Any]:
- text (Tensor): Text index tensor (B, T_text).
- text_lengths (Tensor): Text length tensor (B,).
- feats (Tensor): Feature tensor (B, T_feats, aux_channels).
- feats_lengths (Tensor): Feature length tensor (B,).
- speech (Tensor): Speech waveform tensor (B, T_wav).
- spk_id (Optional[Tensor]): Speaker index tensor (B,) or (B, 1).
- spk_emb (Optional[Tensor]): Speaker embedding tensor (B, spk_embed_dim).
"""
# fields = ["text", "text_lengths", "feats", "feats_lengths", "speech", "spk_id"/"spk_emb"]
text = [np.array(item["text"], dtype=np.int64) for item in examples]
feats = [np.array(item["feats"], dtype=np.float32) for item in examples]
speech = [np.array(item["wave"], dtype=np.float32) for item in examples]
text_lengths = [
np.array(item["text_lengths"], dtype=np.int64) for item in examples
]
feats_lengths = [
np.array(item["feats_lengths"], dtype=np.int64) for item in examples
]
text = batch_sequences(text)
feats = batch_sequences(feats)
speech = batch_sequences(speech)
# convert each batch to paddle.Tensor
text = paddle.to_tensor(text)
feats = paddle.to_tensor(feats)
text_lengths = paddle.to_tensor(text_lengths)
feats_lengths = paddle.to_tensor(feats_lengths)
batch = {
"text": text,
"text_lengths": text_lengths,
"feats": feats,
"feats_lengths": feats_lengths,
"speech": speech
}
# spk_emb has a higher priority than spk_id
if "spk_emb" in examples[0]:
spk_emb = [
np.array(item["spk_emb"], dtype=np.float32) for item in examples
]
spk_emb = batch_sequences(spk_emb)
spk_emb = paddle.to_tensor(spk_emb)
batch["spk_emb"] = spk_emb
elif "spk_id" in examples[0]:
spk_id = [np.array(item["spk_id"], dtype=np.int64) for item in examples]
spk_id = paddle.to_tensor(spk_id)
batch["spk_id"] = spk_id
return batch
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