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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import torch
from torch import nn
from fairseq.models import (FairseqEncoder, FairseqEncoderModel, register_model,
register_model_architecture)
from fairseq.modules import (
LayerNorm, PositionalEmbedding, FairseqDropout, MultiheadAttention
)
from fairseq import utils
from fairseq.data.data_utils import lengths_to_padding_mask
logger = logging.getLogger(__name__)
def model_init(m):
if isinstance(m, nn.Conv1d):
nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu"))
def Embedding(num_embeddings, embedding_dim, padding_idx=None):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
return m
class PositionwiseFeedForward(nn.Module):
def __init__(self, in_dim, hidden_dim, kernel_size, dropout):
super().__init__()
self.ffn = nn.Sequential(
nn.Conv1d(in_dim, hidden_dim, kernel_size=kernel_size,
padding=(kernel_size - 1) // 2),
nn.ReLU(),
nn.Conv1d(hidden_dim, in_dim, kernel_size=kernel_size,
padding=(kernel_size - 1) // 2)
)
self.layer_norm = LayerNorm(in_dim)
self.dropout = self.dropout_module = FairseqDropout(
p=dropout, module_name=self.__class__.__name__
)
def forward(self, x):
# B x T x C
residual = x
x = self.ffn(x.transpose(1, 2)).transpose(1, 2)
x = self.dropout(x)
return self.layer_norm(x + residual)
class FFTLayer(torch.nn.Module):
def __init__(
self, embed_dim, n_heads, hidden_dim, kernel_size, dropout,
attention_dropout
):
super().__init__()
self.self_attn = MultiheadAttention(
embed_dim, n_heads, dropout=attention_dropout, self_attention=True
)
self.layer_norm = LayerNorm(embed_dim)
self.ffn = PositionwiseFeedForward(
embed_dim, hidden_dim, kernel_size, dropout=dropout
)
def forward(self, x, padding_mask=None):
# B x T x C
residual = x
x = x.transpose(0, 1)
x, _ = self.self_attn(
query=x, key=x, value=x, key_padding_mask=padding_mask,
need_weights=False
)
x = x.transpose(0, 1)
x = self.layer_norm(x + residual)
return self.ffn(x)
class LengthRegulator(nn.Module):
def forward(self, x, durations):
# x: B x T x C
out_lens = durations.sum(dim=1)
max_len = out_lens.max()
bsz, seq_len, dim = x.size()
out = x.new_zeros((bsz, max_len, dim))
for b in range(bsz):
indices = []
for t in range(seq_len):
indices.extend([t] * utils.item(durations[b, t]))
indices = torch.tensor(indices, dtype=torch.long).to(x.device)
out_len = utils.item(out_lens[b])
out[b, :out_len] = x[b].index_select(0, indices)
return out, out_lens
class VariancePredictor(nn.Module):
def __init__(self, args):
super().__init__()
self.conv1 = nn.Sequential(
nn.Conv1d(
args.encoder_embed_dim, args.var_pred_hidden_dim,
kernel_size=args.var_pred_kernel_size,
padding=(args.var_pred_kernel_size - 1) // 2
),
nn.ReLU()
)
self.ln1 = nn.LayerNorm(args.var_pred_hidden_dim)
self.dropout_module = FairseqDropout(
p=args.var_pred_dropout, module_name=self.__class__.__name__
)
self.conv2 = nn.Sequential(
nn.Conv1d(
args.var_pred_hidden_dim, args.var_pred_hidden_dim,
kernel_size=args.var_pred_kernel_size, padding=1
),
nn.ReLU()
)
self.ln2 = nn.LayerNorm(args.var_pred_hidden_dim)
self.proj = nn.Linear(args.var_pred_hidden_dim, 1)
def forward(self, x):
# Input: B x T x C; Output: B x T
x = self.conv1(x.transpose(1, 2)).transpose(1, 2)
x = self.dropout_module(self.ln1(x))
x = self.conv2(x.transpose(1, 2)).transpose(1, 2)
x = self.dropout_module(self.ln2(x))
return self.proj(x).squeeze(dim=2)
class VarianceAdaptor(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.length_regulator = LengthRegulator()
self.duration_predictor = VariancePredictor(args)
self.pitch_predictor = VariancePredictor(args)
self.energy_predictor = VariancePredictor(args)
n_bins, steps = self.args.var_pred_n_bins, self.args.var_pred_n_bins - 1
self.pitch_bins = torch.linspace(args.pitch_min, args.pitch_max, steps)
self.embed_pitch = Embedding(n_bins, args.encoder_embed_dim)
self.energy_bins = torch.linspace(args.energy_min, args.energy_max, steps)
self.embed_energy = Embedding(n_bins, args.encoder_embed_dim)
def get_pitch_emb(self, x, tgt=None, factor=1.0):
out = self.pitch_predictor(x)
bins = self.pitch_bins.to(x.device)
if tgt is None:
out = out * factor
emb = self.embed_pitch(torch.bucketize(out, bins))
else:
emb = self.embed_pitch(torch.bucketize(tgt, bins))
return out, emb
def get_energy_emb(self, x, tgt=None, factor=1.0):
out = self.energy_predictor(x)
bins = self.energy_bins.to(x.device)
if tgt is None:
out = out * factor
emb = self.embed_energy(torch.bucketize(out, bins))
else:
emb = self.embed_energy(torch.bucketize(tgt, bins))
return out, emb
def forward(
self, x, padding_mask, durations=None, pitches=None, energies=None,
d_factor=1.0, p_factor=1.0, e_factor=1.0
):
# x: B x T x C
log_dur_out = self.duration_predictor(x)
dur_out = torch.clamp(
torch.round((torch.exp(log_dur_out) - 1) * d_factor).long(), min=0
)
dur_out.masked_fill_(padding_mask, 0)
pitch_out, pitch_emb = self.get_pitch_emb(x, pitches, p_factor)
x = x + pitch_emb
energy_out, energy_emb = self.get_energy_emb(x, energies, e_factor)
x = x + energy_emb
x, out_lens = self.length_regulator(
x, dur_out if durations is None else durations
)
return x, out_lens, log_dur_out, pitch_out, energy_out
class FastSpeech2Encoder(FairseqEncoder):
def __init__(self, args, src_dict, embed_speaker):
super().__init__(src_dict)
self.args = args
self.padding_idx = src_dict.pad()
self.n_frames_per_step = args.n_frames_per_step
self.out_dim = args.output_frame_dim * args.n_frames_per_step
self.embed_speaker = embed_speaker
self.spk_emb_proj = None
if embed_speaker is not None:
self.spk_emb_proj = nn.Linear(
args.encoder_embed_dim + args.speaker_embed_dim,
args.encoder_embed_dim
)
self.dropout_module = FairseqDropout(
p=args.dropout, module_name=self.__class__.__name__
)
self.embed_tokens = Embedding(
len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx
)
self.embed_positions = PositionalEmbedding(
args.max_source_positions, args.encoder_embed_dim, self.padding_idx
)
self.pos_emb_alpha = nn.Parameter(torch.ones(1))
self.dec_pos_emb_alpha = nn.Parameter(torch.ones(1))
self.encoder_fft_layers = nn.ModuleList(
FFTLayer(
args.encoder_embed_dim, args.encoder_attention_heads,
args.fft_hidden_dim, args.fft_kernel_size,
dropout=args.dropout, attention_dropout=args.attention_dropout
)
for _ in range(args.encoder_layers)
)
self.var_adaptor = VarianceAdaptor(args)
self.decoder_fft_layers = nn.ModuleList(
FFTLayer(
args.decoder_embed_dim, args.decoder_attention_heads,
args.fft_hidden_dim, args.fft_kernel_size,
dropout=args.dropout, attention_dropout=args.attention_dropout
)
for _ in range(args.decoder_layers)
)
self.out_proj = nn.Linear(args.decoder_embed_dim, self.out_dim)
self.apply(model_init)
def forward(self, src_tokens, src_lengths=None, speaker=None,
durations=None, pitches=None, energies=None, **kwargs):
x = self.embed_tokens(src_tokens)
enc_padding_mask = src_tokens.eq(self.padding_idx)
x += self.pos_emb_alpha * self.embed_positions(enc_padding_mask)
x = self.dropout_module(x)
for layer in self.encoder_fft_layers:
x = layer(x, enc_padding_mask)
if self.embed_speaker is not None:
bsz, seq_len, _ = x.size()
emb = self.embed_speaker(speaker).expand(bsz, seq_len, -1)
x = self.spk_emb_proj(torch.cat([x, emb], dim=2))
x, out_lens, log_dur_out, pitch_out, energy_out = \
self.var_adaptor(x, enc_padding_mask, durations, pitches, energies)
dec_padding_mask = lengths_to_padding_mask(out_lens)
x += self.dec_pos_emb_alpha * self.embed_positions(dec_padding_mask)
for layer in self.decoder_fft_layers:
x = layer(x, dec_padding_mask)
x = self.out_proj(x)
return x, out_lens, log_dur_out, pitch_out, energy_out
@register_model("fastspeech2")
class FastSpeech2Model(FairseqEncoderModel):
"""
Implementation for https://arxiv.org/abs/2006.04558
"""
NON_AUTOREGRESSIVE = True
@staticmethod
def add_args(parser):
parser.add_argument("--dropout", type=float)
parser.add_argument("--output-frame-dim", type=int)
parser.add_argument("--speaker-embed-dim", type=int)
# FFT blocks
parser.add_argument("--fft-hidden-dim", type=int)
parser.add_argument("--fft-kernel-size", type=int)
parser.add_argument("--attention-dropout", type=float)
parser.add_argument("--encoder-layers", type=int)
parser.add_argument("--encoder-embed-dim", type=int)
parser.add_argument("--encoder-attention-heads", type=int)
parser.add_argument("--decoder-layers", type=int)
parser.add_argument("--decoder-embed-dim", type=int)
parser.add_argument("--decoder-attention-heads", type=int)
# variance predictor
parser.add_argument("--var-pred-n-bins", type=int)
parser.add_argument("--var-pred-hidden-dim", type=int)
parser.add_argument("--var-pred-kernel-size", type=int)
parser.add_argument("--var-pred-dropout", type=float)
def __init__(self, encoder, args, src_dict):
super().__init__(encoder)
self._num_updates = 0
out_dim = args.output_frame_dim * args.n_frames_per_step
self.ctc_proj = None
if getattr(args, "ctc_weight", 0.) > 0.:
self.ctc_proj = nn.Linear(out_dim, len(src_dict))
@classmethod
def build_model(cls, args, task):
embed_speaker = task.get_speaker_embeddings(args)
encoder = FastSpeech2Encoder(args, task.src_dict, embed_speaker)
return cls(encoder, args, task.src_dict)
def set_num_updates(self, num_updates):
super().set_num_updates(num_updates)
self._num_updates = num_updates
def get_normalized_probs(self, net_output, log_probs, sample=None):
logits = self.ctc_proj(net_output[0])
if log_probs:
return utils.log_softmax(logits.float(), dim=-1)
else:
return utils.softmax(logits.float(), dim=-1)
@register_model_architecture("fastspeech2", "fastspeech2")
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.2)
args.output_frame_dim = getattr(args, "output_frame_dim", 80)
args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64)
# FFT blocks
args.fft_hidden_dim = getattr(args, "fft_hidden_dim", 1024)
args.fft_kernel_size = getattr(args, "fft_kernel_size", 9)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.encoder_layers = getattr(args, "encoder_layers", 4)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2)
args.decoder_layers = getattr(args, "decoder_layers", 4)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2)
# variance predictor
args.var_pred_n_bins = getattr(args, "var_pred_n_bins", 256)
args.var_pred_hidden_dim = getattr(args, "var_pred_hidden_dim", 256)
args.var_pred_kernel_size = getattr(args, "var_pred_kernel_size", 3)
args.var_pred_dropout = getattr(args, "var_pred_dropout", 0.5)
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