liangym
3 years ago
parent
ef7d15dc02
commit
c91dc02931
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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from typing import Any
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from typing import Dict
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from typing import Sequence
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from typing import Tuple
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import paddle
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from paddle import nn
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from typeguard import check_argument_types
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
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from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask
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from paddlespeech.t2s.modules.nets_utils import make_pad_mask
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from paddlespeech.t2s.modules.predictor.duration_predictor import DurationPredictorLoss
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class FastSpeech2MIDI(FastSpeech2):
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"""The Fastspeech2 module of DiffSinger.
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"""
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def __init__(
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self,
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# fastspeech2 network structure related
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idim: int,
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odim: int,
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fastspeech2_config: Dict[str, Any],
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# note emb
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note_num: int=300,
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# is_slur emb
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is_slur_num: int=2, ):
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"""Initialize FastSpeech2 module for svs.
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Args:
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fastspeech2_config (Dict):
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The config of FastSpeech2 module on DiffSinger model
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note_num (Optional[int]):
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Number of note. If not None, assume that the
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note_ids will be provided as the input and use note_embedding_table.
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is_slur_num (Optional[int]):
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Number of note. If not None, assume that the
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is_slur_ids will be provided as the input
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"""
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assert check_argument_types()
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super().__init__(idim=idim, odim=odim, **fastspeech2_config)
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self.note_embed_dim = self.is_slur_embed_dim = fastspeech2_config[
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"adim"]
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if note_num is not None:
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self.note_embedding_table = nn.Embedding(
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num_embeddings=note_num,
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embedding_dim=self.note_embed_dim,
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padding_idx=self.padding_idx)
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self.note_dur_layer = nn.Linear(1, self.note_embed_dim)
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if is_slur_num is not None:
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self.is_slur_embedding_table = nn.Embedding(
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num_embeddings=is_slur_num,
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embedding_dim=self.is_slur_embed_dim,
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padding_idx=self.padding_idx)
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def forward(
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self,
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text: paddle.Tensor,
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note: paddle.Tensor,
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note_dur: paddle.Tensor,
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is_slur: paddle.Tensor,
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text_lengths: paddle.Tensor,
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speech: paddle.Tensor,
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speech_lengths: paddle.Tensor,
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durations: paddle.Tensor,
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pitch: paddle.Tensor,
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energy: paddle.Tensor,
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spk_emb: paddle.Tensor=None,
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spk_id: paddle.Tensor=None,
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) -> Tuple[paddle.Tensor, Dict[str, paddle.Tensor], paddle.Tensor]:
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"""Calculate forward propagation.
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Args:
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text(Tensor(int64)):
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Batch of padded token (phone) ids (B, Tmax).
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note(Tensor(int64)):
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Batch of padded note (element in music score) ids (B, Tmax).
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note_dur(Tensor(float32)):
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Batch of padded note durations in seconds (element in music score) (B, Tmax).
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is_slur(Tensor(int64)):
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Batch of padded slur (element in music score) ids (B, Tmax).
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text_lengths(Tensor(int64)):
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Batch of phone lengths of each input (B,).
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speech(Tensor[float32]):
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Batch of padded target features (e.g. mel) (B, Lmax, odim).
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speech_lengths(Tensor(int64)):
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Batch of the lengths of each target features (B,).
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durations(Tensor(int64)):
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Batch of padded token durations in frame (B, Tmax).
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pitch(Tensor[float32]):
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Batch of padded frame-averaged pitch (B, Lmax, 1).
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energy(Tensor[float32]):
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Batch of padded frame-averaged energy (B, Lmax, 1).
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spk_emb(Tensor[float32], optional):
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Batch of speaker embeddings (B, spk_embed_dim).
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spk_id(Tnesor[int64], optional(int64)):
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Batch of speaker ids (B,)
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Returns:
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"""
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xs = paddle.cast(text, 'int64')
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note = paddle.cast(note, 'int64')
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note_dur = paddle.cast(note_dur, 'float32')
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is_slur = paddle.cast(is_slur, 'int64')
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ilens = paddle.cast(text_lengths, 'int64')
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olens = paddle.cast(speech_lengths, 'int64')
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ds = paddle.cast(durations, 'int64')
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ps = pitch
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es = energy
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ys = speech
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olens = speech_lengths
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if spk_id is not None:
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spk_id = paddle.cast(spk_id, 'int64')
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# forward propagation
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before_outs, after_outs, d_outs, p_outs, e_outs, spk_logits = self._forward(
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xs,
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note,
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note_dur,
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is_slur,
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ilens,
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olens,
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ds,
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ps,
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es,
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is_inference=False,
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spk_emb=spk_emb,
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spk_id=spk_id, )
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# modify mod part of groundtruth
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if self.reduction_factor > 1:
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olens = olens - olens % self.reduction_factor
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max_olen = max(olens)
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ys = ys[:, :max_olen]
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return before_outs, after_outs, d_outs, p_outs, e_outs, ys, olens, spk_logits
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def _forward(
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self,
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xs: paddle.Tensor,
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note: paddle.Tensor,
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note_dur: paddle.Tensor,
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is_slur: paddle.Tensor,
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ilens: paddle.Tensor,
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olens: paddle.Tensor=None,
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ds: paddle.Tensor=None,
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ps: paddle.Tensor=None,
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es: paddle.Tensor=None,
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is_inference: bool=False,
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is_train_diffusion: bool=False,
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return_after_enc=False,
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alpha: float=1.0,
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spk_emb=None,
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spk_id=None, ) -> Sequence[paddle.Tensor]:
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# forward encoder
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x_masks = self._source_mask(ilens)
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note_emb = self.note_embedding_table(note)
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note_dur_emb = self.note_dur_layer(paddle.unsqueeze(note_dur, axis=-1))
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is_slur_emb = self.is_slur_embedding_table(is_slur)
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# (B, Tmax, adim)
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hs, _ = self.encoder(
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xs,
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x_masks,
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note_emb,
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note_dur_emb,
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is_slur_emb, )
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if self.spk_num and self.enable_speaker_classifier and not is_inference:
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hs_for_spk_cls = self.grad_reverse(hs)
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spk_logits = self.speaker_classifier(hs_for_spk_cls, ilens)
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else:
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spk_logits = None
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# integrate speaker embedding
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if self.spk_embed_dim is not None:
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# spk_emb has a higher priority than spk_id
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if spk_emb is not None:
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hs = self._integrate_with_spk_embed(hs, spk_emb)
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elif spk_id is not None:
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spk_emb = self.spk_embedding_table(spk_id)
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hs = self._integrate_with_spk_embed(hs, spk_emb)
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# forward duration predictor and variance predictors
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d_masks = make_pad_mask(ilens)
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if olens is not None:
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pitch_masks = make_pad_mask(olens).unsqueeze(-1)
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else:
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pitch_masks = None
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# inference for decoder input for duffusion
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if is_train_diffusion:
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hs = self.length_regulator(hs, ds, is_inference=False)
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p_outs = self.pitch_predictor(hs.detach(), pitch_masks)
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e_outs = self.energy_predictor(hs.detach(), pitch_masks)
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p_embs = self.pitch_embed(p_outs.transpose((0, 2, 1))).transpose(
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(0, 2, 1))
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e_embs = self.energy_embed(e_outs.transpose((0, 2, 1))).transpose(
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(0, 2, 1))
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hs = hs + e_embs + p_embs
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elif is_inference:
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# (B, Tmax)
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if ds is not None:
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d_outs = ds
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else:
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d_outs = self.duration_predictor.inference(hs, d_masks)
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# (B, Lmax, adim)
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hs = self.length_regulator(hs, d_outs, alpha, is_inference=True)
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if ps is not None:
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p_outs = ps
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else:
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if self.stop_gradient_from_pitch_predictor:
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p_outs = self.pitch_predictor(hs.detach(), pitch_masks)
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else:
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p_outs = self.pitch_predictor(hs, pitch_masks)
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if es is not None:
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e_outs = es
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else:
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if self.stop_gradient_from_energy_predictor:
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e_outs = self.energy_predictor(hs.detach(), pitch_masks)
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else:
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e_outs = self.energy_predictor(hs, pitch_masks)
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p_embs = self.pitch_embed(p_outs.transpose((0, 2, 1))).transpose(
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(0, 2, 1))
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e_embs = self.energy_embed(e_outs.transpose((0, 2, 1))).transpose(
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(0, 2, 1))
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hs = hs + e_embs + p_embs
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# training
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else:
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d_outs = self.duration_predictor(hs, d_masks)
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# (B, Lmax, adim)
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hs = self.length_regulator(hs, ds, is_inference=False)
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if self.stop_gradient_from_pitch_predictor:
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p_outs = self.pitch_predictor(hs.detach(), pitch_masks)
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else:
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p_outs = self.pitch_predictor(hs, pitch_masks)
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if self.stop_gradient_from_energy_predictor:
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e_outs = self.energy_predictor(hs.detach(), pitch_masks)
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else:
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e_outs = self.energy_predictor(hs, pitch_masks)
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p_embs = self.pitch_embed(ps.transpose((0, 2, 1))).transpose(
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(0, 2, 1))
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e_embs = self.energy_embed(es.transpose((0, 2, 1))).transpose(
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(0, 2, 1))
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hs = hs + e_embs + p_embs
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# forward decoder
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if olens is not None and not is_inference:
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if self.reduction_factor > 1:
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olens_in = paddle.to_tensor(
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[olen // self.reduction_factor for olen in olens.numpy()])
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else:
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olens_in = olens
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# (B, 1, T)
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h_masks = self._source_mask(olens_in)
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else:
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h_masks = None
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if return_after_enc:
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return hs, h_masks
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if self.decoder_type == 'cnndecoder':
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# remove output masks for dygraph to static graph
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zs = self.decoder(hs, h_masks)
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before_outs = zs
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else:
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# (B, Lmax, adim)
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zs, _ = self.decoder(hs, h_masks)
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# (B, Lmax, odim)
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before_outs = self.feat_out(zs).reshape(
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(paddle.shape(zs)[0], -1, self.odim))
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# postnet -> (B, Lmax//r * r, odim)
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if self.postnet is None:
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after_outs = before_outs
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else:
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after_outs = before_outs + self.postnet(
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before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
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return before_outs, after_outs, d_outs, p_outs, e_outs, spk_logits
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def encoder_infer(
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self,
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text: paddle.Tensor,
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note: paddle.Tensor,
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note_dur: paddle.Tensor,
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is_slur: paddle.Tensor,
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alpha: float=1.0,
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spk_emb=None,
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spk_id=None,
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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xs = paddle.cast(text, 'int64').unsqueeze(0)
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note = paddle.cast(note, 'int64').unsqueeze(0)
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note_dur = paddle.cast(note_dur, 'float32').unsqueeze(0)
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is_slur = paddle.cast(is_slur, 'int64').unsqueeze(0)
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# setup batch axis
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ilens = paddle.shape(xs)[1]
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if spk_emb is not None:
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spk_emb = spk_emb.unsqueeze(0)
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# (1, L, odim)
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# use *_ to avoid bug in dygraph to static graph
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hs, _ = self._forward(
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xs,
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note,
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note_dur,
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is_slur,
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ilens,
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is_inference=True,
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return_after_enc=True,
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alpha=alpha,
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spk_emb=spk_emb,
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spk_id=spk_id, )
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return hs
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# get encoder output for diffusion training
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def encoder_infer_batch(
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self,
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text: paddle.Tensor,
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note: paddle.Tensor,
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note_dur: paddle.Tensor,
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is_slur: paddle.Tensor,
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text_lengths: paddle.Tensor,
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speech_lengths: paddle.Tensor,
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ds: paddle.Tensor=None,
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ps: paddle.Tensor=None,
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es: paddle.Tensor=None,
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alpha: float=1.0,
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spk_emb=None,
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spk_id=None, ) -> Tuple[paddle.Tensor, paddle.Tensor]:
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xs = paddle.cast(text, 'int64')
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note = paddle.cast(note, 'int64')
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note_dur = paddle.cast(note_dur, 'float32')
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is_slur = paddle.cast(is_slur, 'int64')
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ilens = paddle.cast(text_lengths, 'int64')
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olens = paddle.cast(speech_lengths, 'int64')
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if spk_emb is not None:
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spk_emb = spk_emb.unsqueeze(0)
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# (1, L, odim)
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# use *_ to avoid bug in dygraph to static graph
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hs, h_masks = self._forward(
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xs,
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note,
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note_dur,
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is_slur,
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ilens,
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olens,
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ds,
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ps,
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es,
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return_after_enc=True,
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is_train_diffusion=True,
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alpha=alpha,
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spk_emb=spk_emb,
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spk_id=spk_id, )
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return hs, h_masks
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def inference(
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self,
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text: paddle.Tensor,
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note: paddle.Tensor,
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note_dur: paddle.Tensor,
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is_slur: paddle.Tensor,
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durations: paddle.Tensor=None,
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pitch: paddle.Tensor=None,
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energy: paddle.Tensor=None,
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alpha: float=1.0,
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use_teacher_forcing: bool=False,
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spk_emb=None,
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spk_id=None,
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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"""Generate the sequence of features given the sequences of characters.
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Args:
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text(Tensor(int64)):
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Input sequence of characters (T,).
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note(Tensor(int64)):
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Input note (element in music score) ids (T,).
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note_dur(Tensor(float32)):
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Input note durations in seconds (element in music score) (T,).
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is_slur(Tensor(int64)):
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Input slur (element in music score) ids (T,).
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durations(Tensor, optional (int64)):
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Groundtruth of duration (T,).
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pitch(Tensor, optional):
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Groundtruth of token-averaged pitch (T, 1).
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energy(Tensor, optional):
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Groundtruth of token-averaged energy (T, 1).
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alpha(float, optional):
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Alpha to control the speed.
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use_teacher_forcing(bool, optional):
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Whether to use teacher forcing.
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If true, groundtruth of duration, pitch and energy will be used.
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spk_emb(Tensor, optional, optional):
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peaker embedding vector (spk_embed_dim,). (Default value = None)
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spk_id(Tensor, optional(int64), optional):
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spk ids (1,). (Default value = None)
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Returns:
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"""
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xs = paddle.cast(text, 'int64').unsqueeze(0)
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note = paddle.cast(note, 'int64').unsqueeze(0)
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note_dur = paddle.cast(note_dur, 'float32').unsqueeze(0)
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is_slur = paddle.cast(is_slur, 'int64').unsqueeze(0)
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d, p, e = durations, pitch, energy
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# setup batch axis
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ilens = paddle.shape(xs)[1]
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if spk_emb is not None:
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spk_emb = spk_emb.unsqueeze(0)
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if use_teacher_forcing:
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# use groundtruth of duration, pitch, and energy
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ds = d.unsqueeze(0) if d is not None else None
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ps = p.unsqueeze(0) if p is not None else None
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es = e.unsqueeze(0) if e is not None else None
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# (1, L, odim)
|
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_, outs, d_outs, p_outs, e_outs, _ = self._forward(
|
||||
xs,
|
||||
note,
|
||||
note_dur,
|
||||
is_slur,
|
||||
ilens,
|
||||
ds=ds,
|
||||
ps=ps,
|
||||
es=es,
|
||||
spk_emb=spk_emb,
|
||||
spk_id=spk_id,
|
||||
is_inference=True)
|
||||
else:
|
||||
# (1, L, odim)
|
||||
_, outs, d_outs, p_outs, e_outs, _ = self._forward(
|
||||
xs,
|
||||
note,
|
||||
note_dur,
|
||||
is_slur,
|
||||
ilens,
|
||||
is_inference=True,
|
||||
alpha=alpha,
|
||||
spk_emb=spk_emb,
|
||||
spk_id=spk_id, )
|
||||
|
||||
return outs[0], d_outs[0], p_outs[0], e_outs[0]
|
||||
|
||||
|
||||
class FastSpeech2MIDILoss(nn.Layer):
|
||||
"""Loss function module for DiffSinger."""
|
||||
|
||||
def __init__(self, use_masking: bool=True,
|
||||
use_weighted_masking: bool=False):
|
||||
"""Initialize feed-forward Transformer loss module.
|
||||
Args:
|
||||
use_masking (bool):
|
||||
Whether to apply masking for padded part in loss calculation.
|
||||
use_weighted_masking (bool):
|
||||
Whether to weighted masking in loss calculation.
|
||||
"""
|
||||
assert check_argument_types()
|
||||
super().__init__()
|
||||
|
||||
assert (use_masking != use_weighted_masking) or not use_masking
|
||||
self.use_masking = use_masking
|
||||
self.use_weighted_masking = use_weighted_masking
|
||||
|
||||
# define criterions
|
||||
reduction = "none" if self.use_weighted_masking else "mean"
|
||||
self.l1_criterion = nn.L1Loss(reduction=reduction)
|
||||
self.mse_criterion = nn.MSELoss(reduction=reduction)
|
||||
self.duration_criterion = DurationPredictorLoss(reduction=reduction)
|
||||
self.ce_criterion = nn.CrossEntropyLoss()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
after_outs: paddle.Tensor,
|
||||
before_outs: paddle.Tensor,
|
||||
d_outs: paddle.Tensor,
|
||||
p_outs: paddle.Tensor,
|
||||
e_outs: paddle.Tensor,
|
||||
ys: paddle.Tensor,
|
||||
ds: paddle.Tensor,
|
||||
ps: paddle.Tensor,
|
||||
es: paddle.Tensor,
|
||||
ilens: paddle.Tensor,
|
||||
olens: paddle.Tensor,
|
||||
spk_logits: paddle.Tensor=None,
|
||||
spk_ids: paddle.Tensor=None,
|
||||
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor,
|
||||
paddle.Tensor, ]:
|
||||
"""Calculate forward propagation.
|
||||
|
||||
Args:
|
||||
after_outs(Tensor):
|
||||
Batch of outputs after postnets (B, Lmax, odim).
|
||||
before_outs(Tensor):
|
||||
Batch of outputs before postnets (B, Lmax, odim).
|
||||
d_outs(Tensor):
|
||||
Batch of outputs of duration predictor (B, Tmax).
|
||||
p_outs(Tensor):
|
||||
Batch of outputs of pitch predictor (B, Lmax, 1).
|
||||
e_outs(Tensor):
|
||||
Batch of outputs of energy predictor (B, Lmax, 1).
|
||||
ys(Tensor):
|
||||
Batch of target features (B, Lmax, odim).
|
||||
ds(Tensor):
|
||||
Batch of durations (B, Tmax).
|
||||
ps(Tensor):
|
||||
Batch of target frame-averaged pitch (B, Lmax, 1).
|
||||
es(Tensor):
|
||||
Batch of target frame-averaged energy (B, Lmax, 1).
|
||||
ilens(Tensor):
|
||||
Batch of the lengths of each input (B,).
|
||||
olens(Tensor):
|
||||
Batch of the lengths of each target (B,).
|
||||
spk_logits(Option[Tensor]):
|
||||
Batch of outputs after speaker classifier (B, Lmax, num_spk)
|
||||
spk_ids(Option[Tensor]):
|
||||
Batch of target spk_id (B,)
|
||||
|
||||
|
||||
Returns:
|
||||
|
||||
|
||||
"""
|
||||
speaker_loss = 0.0
|
||||
# apply mask to remove padded part
|
||||
if self.use_masking:
|
||||
out_masks = make_non_pad_mask(olens).unsqueeze(-1)
|
||||
before_outs = before_outs.masked_select(
|
||||
out_masks.broadcast_to(before_outs.shape))
|
||||
if after_outs is not None:
|
||||
after_outs = after_outs.masked_select(
|
||||
out_masks.broadcast_to(after_outs.shape))
|
||||
ys = ys.masked_select(out_masks.broadcast_to(ys.shape))
|
||||
duration_masks = make_non_pad_mask(ilens)
|
||||
d_outs = d_outs.masked_select(
|
||||
duration_masks.broadcast_to(d_outs.shape))
|
||||
ds = ds.masked_select(duration_masks.broadcast_to(ds.shape))
|
||||
pitch_masks = out_masks
|
||||
p_outs = p_outs.masked_select(
|
||||
pitch_masks.broadcast_to(p_outs.shape))
|
||||
e_outs = e_outs.masked_select(
|
||||
pitch_masks.broadcast_to(e_outs.shape))
|
||||
ps = ps.masked_select(pitch_masks.broadcast_to(ps.shape))
|
||||
es = es.masked_select(pitch_masks.broadcast_to(es.shape))
|
||||
|
||||
if spk_logits is not None and spk_ids is not None:
|
||||
batch_size = spk_ids.shape[0]
|
||||
spk_ids = paddle.repeat_interleave(spk_ids, spk_logits.shape[1],
|
||||
None)
|
||||
spk_logits = paddle.reshape(spk_logits,
|
||||
[-1, spk_logits.shape[-1]])
|
||||
mask_index = spk_logits.abs().sum(axis=1) != 0
|
||||
spk_ids = spk_ids[mask_index]
|
||||
spk_logits = spk_logits[mask_index]
|
||||
|
||||
# calculate loss
|
||||
l1_loss = self.l1_criterion(before_outs, ys)
|
||||
if after_outs is not None:
|
||||
l1_loss += self.l1_criterion(after_outs, ys)
|
||||
duration_loss = self.duration_criterion(d_outs, ds)
|
||||
pitch_loss = self.mse_criterion(p_outs, ps)
|
||||
energy_loss = self.mse_criterion(e_outs, es)
|
||||
|
||||
if spk_logits is not None and spk_ids is not None:
|
||||
speaker_loss = self.ce_criterion(spk_logits, spk_ids) / batch_size
|
||||
|
||||
# make weighted mask and apply it
|
||||
if self.use_weighted_masking:
|
||||
out_masks = make_non_pad_mask(olens).unsqueeze(-1)
|
||||
out_weights = out_masks.cast(dtype=paddle.float32) / out_masks.cast(
|
||||
dtype=paddle.float32).sum(
|
||||
axis=1, keepdim=True)
|
||||
out_weights /= ys.shape[0] * ys.shape[2]
|
||||
duration_masks = make_non_pad_mask(ilens)
|
||||
duration_weights = (duration_masks.cast(dtype=paddle.float32) /
|
||||
duration_masks.cast(dtype=paddle.float32).sum(
|
||||
axis=1, keepdim=True))
|
||||
duration_weights /= ds.shape[0]
|
||||
|
||||
# apply weight
|
||||
l1_loss = l1_loss.multiply(out_weights)
|
||||
l1_loss = l1_loss.masked_select(
|
||||
out_masks.broadcast_to(l1_loss.shape)).sum()
|
||||
duration_loss = (duration_loss.multiply(duration_weights)
|
||||
.masked_select(duration_masks).sum())
|
||||
pitch_masks = out_masks
|
||||
pitch_weights = out_weights
|
||||
pitch_loss = pitch_loss.multiply(pitch_weights)
|
||||
pitch_loss = pitch_loss.masked_select(
|
||||
pitch_masks.broadcast_to(pitch_loss.shape)).sum()
|
||||
energy_loss = energy_loss.multiply(pitch_weights)
|
||||
energy_loss = energy_loss.masked_select(
|
||||
pitch_masks.broadcast_to(energy_loss.shape)).sum()
|
||||
|
||||
return l1_loss, duration_loss, pitch_loss, energy_loss, speaker_loss
|
||||
Loading…
Reference in new issue