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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# MIT_LICENSE file in the root directory of this source tree.
from typing import Any, Dict, List, Literal, Optional, Tuple
import torch
import torch.nn.functional as F
from fairseq2.nn.embedding import Embedding, StandardEmbedding
from fairseq2.nn.padding import PaddingMask
from fairseq2.nn.position_encoder import PositionEncoder
from fairseq2.nn.projection import Projection
from fairseq2.typing import DataType, Device
from torch.nn import (
ELU,
BatchNorm1d,
Conv1d,
ConvTranspose1d,
Dropout,
Module,
ModuleList,
Parameter,
Sequential,
Tanh,
init,
)
from torch.nn.utils.weight_norm import remove_weight_norm, weight_norm
from seamless_communication.models.generator.ecapa_tdnn import ECAPA_TDNN
from seamless_communication.models.unity.fft_decoder import FeedForwardTransformer
from seamless_communication.models.unity.length_regulator import VarianceAdaptor
from seamless_communication.models.vocoder.hifigan import (
LRELU_SLOPE,
ResBlock,
init_weights,
)
from .streamable import (
StreamableConv1d,
StreamableConvTranspose1d,
StreamableLSTM,
StreamableResnetBlock,
)
ELU_PARAMS: Dict[str, Any] = {"alpha": 1.0}
class PretsselEncoderFrontend(Module):
"""
Represent Encoder frontend, including the prosody encoder and language embedding
"""
prosody_encoder: ECAPA_TDNN
embed_tokens: Embedding
embed_positions: PositionEncoder
pos_emb_alpha: Parameter
embed_lang: Embedding
dropout: Dropout
def __init__(
self,
prosody_encoder: ECAPA_TDNN,
embed_tokens: Embedding,
embed_positions: PositionEncoder,
lang_to_index: Dict[str, int],
lang_embed_dim: Optional[int],
dropout_p: float,
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
):
super().__init__()
self.prosody_encoder = prosody_encoder
self.embed_tokens = embed_tokens
self.embed_positions = embed_positions
self.pos_emb_alpha = Parameter(torch.ones(1, device=device, dtype=dtype))
self.lang_to_index = lang_to_index
if lang_embed_dim is not None:
self.embed_lang = StandardEmbedding(
len(lang_to_index), lang_embed_dim, device=device, dtype=dtype
)
else:
self.register_module("embed_lang", None)
self.dropout = Dropout(dropout_p)
self.device = device
self.dtype = dtype
def forward(
self,
seqs: torch.Tensor,
padding_mask: Optional[PaddingMask],
prosody_input_seqs: torch.Tensor,
prosody_padding_mask: Optional[PaddingMask],
tgt_lang: str,
) -> Tuple[torch.Tensor, torch.Tensor]:
prosody_embs = self.prosody_encoder(
prosody_input_seqs,
prosody_padding_mask,
).unsqueeze(1)
if self.embed_lang is not None:
lang_index = self.lang_to_index[tgt_lang]
lang_index_tensor = (
torch.Tensor([lang_index]).to(seqs).repeat(seqs.size(0), 1)
)
lang_embeds = self.embed_lang(lang_index_tensor)
prosody_embs = torch.cat([prosody_embs, lang_embeds], dim=-1)
seqs = self.embed_tokens(seqs)
seqs += self.pos_emb_alpha * (self.embed_positions(seqs, padding_mask) - seqs)
seqs = self.dropout(seqs)
return seqs, prosody_embs
class PretsselDecoderFrontend(Module):
"""Represent Decoder frontend, including VarianceAdaptor & Positional embedding"""
variance_adaptor: VarianceAdaptor
embed_positions: PositionEncoder
pos_emb_alpha: Parameter
def __init__(
self,
variance_adaptor: VarianceAdaptor,
embed_positions: PositionEncoder,
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
):
super().__init__()
self.variance_adaptor = variance_adaptor
self.embed_positions = embed_positions
self.pos_emb_alpha = Parameter(torch.ones(1, device=device, dtype=dtype))
self.device = device
self.dtype = dtype
def forward(
self,
seqs: torch.Tensor,
padding_mask: PaddingMask,
durations: Optional[torch.Tensor] = None,
duration_factor: float = 1.0,
min_duration: int = 0,
film_cond_emb: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, PaddingMask]:
seqs, padding_mask, _ = self.variance_adaptor(
seqs, padding_mask, durations, duration_factor, min_duration, film_cond_emb
)
seqs += self.pos_emb_alpha * (self.embed_positions(seqs, padding_mask) - seqs)
return seqs, padding_mask
class PretsselVocoder(Module):
"""The expressivity-preserving vocoder"""
encoder_frontend: PretsselEncoderFrontend
encoder: FeedForwardTransformer
decoder_frontend: PretsselDecoderFrontend
decoder: FeedForwardTransformer
final_proj: Projection
def __init__( # type: ignore[no-untyped-def]
self,
encoder_frontend: PretsselEncoderFrontend,
encoder: FeedForwardTransformer,
decoder_frontend: PretsselDecoderFrontend,
decoder: FeedForwardTransformer,
final_proj: Projection,
pn_n_channels: int,
pn_kernel_size: int,
pn_layers: int,
pn_dropout: float,
upsample_rates: List[int],
upsample_kernel_sizes: List[int],
upsample_initial_channel: int,
resblock_kernel_sizes: List[int],
resblock_dilation_sizes: List[List[int]],
mel_dim: int = 80,
add_ups_out_pad: bool = True,
channels: int = 1,
dimension: int = 128,
n_filters: int = 32,
ratios: List[int] = [8, 5, 4, 2],
norm: Literal[
"none", "weight_norm", "spectral_norm", "time_group_norm"
] = "none",
norm_params: Dict[str, Any] = {},
kernel_size: int = 7,
last_kernel_size: int = 7,
residual_kernel_size: int = 3,
causal: bool = False,
pad_mode: str = "constant",
true_skip: bool = True,
compress: int = 2,
lstm: int = 0,
disable_norm_outer_blocks: int = 0,
trim_right_ratio: float = 1.0,
gcmvn_mean: Optional[List[float]] = None,
gcmvn_std: Optional[List[float]] = None,
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
):
super().__init__()
self.encoder_frontend = encoder_frontend
self.encoder = encoder
self.decoder_frontend = decoder_frontend
self.decoder = decoder
self.final_proj = final_proj
mult = 1
stream_layers: List[Module] = [
StreamableConv1d(
channels,
mult * n_filters,
kernel_size,
norm="none" if disable_norm_outer_blocks >= 1 else norm,
norm_kwargs=norm_params,
causal=causal,
pad_mode=pad_mode,
activation=Tanh(),
device=device,
dtype=dtype,
)
]
# Downsample to from audio scale
for i, ratio in enumerate(list(reversed(ratios))):
block_norm = "none" if disable_norm_outer_blocks >= i + 2 else norm
stream_layers.append(
StreamableResnetBlock(
mult * n_filters,
kernel_sizes=[residual_kernel_size, 1],
dilations=[1, 1],
norm=block_norm,
norm_params=norm_params,
causal=causal,
pad_mode=pad_mode,
compress=compress,
true_skip=true_skip,
device=device,
dtype=dtype,
)
)
stream_layers.append(ELU(**ELU_PARAMS))
stream_layers.append(
StreamableConv1d(
mult * n_filters,
mult * n_filters * 2,
kernel_size=ratio * 2,
stride=ratio,
norm=block_norm,
norm_kwargs=norm_params,
causal=causal,
pad_mode=pad_mode,
device=device,
dtype=dtype,
)
)
mult *= 2
stream_layers.append(StreamableLSTM(mult * n_filters, num_layers=lstm))
stream_layers.append(ELU(**ELU_PARAMS))
n_blocks = len(ratios) + 2
stream_layers.append(
StreamableConv1d(
mult * n_filters,
dimension,
last_kernel_size,
norm="none" if disable_norm_outer_blocks == n_blocks else norm,
norm_kwargs=norm_params,
causal=causal,
pad_mode=pad_mode,
device=device,
dtype=dtype,
)
)
stream_layers.append(
StreamableConv1d(
dimension,
mult * n_filters,
kernel_size,
norm="none" if disable_norm_outer_blocks == n_blocks else norm,
norm_kwargs=norm_params,
causal=causal,
pad_mode=pad_mode,
device=device,
dtype=dtype,
)
)
stream_layers.append(
StreamableLSTM(
mult * n_filters, num_layers=lstm, device=device, dtype=dtype
)
)
# resample back to raw audio scale
for i, ratio in enumerate(ratios):
block_norm = (
"none" if disable_norm_outer_blocks >= n_blocks - (i + 1) else norm
)
stream_layers.append(ELU(**ELU_PARAMS))
stream_layers.append(
StreamableConvTranspose1d(
mult * n_filters,
mult * n_filters // 2,
kernel_size=ratio * 2,
stride=ratio,
norm=block_norm,
norm_kwargs=norm_params,
causal=causal,
trim_right_ratio=trim_right_ratio,
device=device,
dtype=dtype,
)
)
stream_layers.append(
StreamableResnetBlock(
mult * n_filters // 2,
kernel_sizes=[residual_kernel_size, 1],
dilations=[1, 1],
norm=block_norm,
norm_params=norm_params,
activation_params=ELU_PARAMS,
causal=causal,
pad_mode=pad_mode,
compress=compress,
true_skip=true_skip,
device=device,
dtype=dtype,
)
)
mult //= 2
stream_layers.append(ELU(**ELU_PARAMS))
stream_layers.append(
StreamableConv1d(
n_filters,
channels,
last_kernel_size,
norm="none" if disable_norm_outer_blocks >= 1 else norm,
norm_kwargs=norm_params,
causal=causal,
pad_mode=pad_mode,
device=device,
dtype=dtype,
)
)
self.n_streams = len(stream_layers)
chunk_size = self.n_streams // 4
stream_idx = 0
self.pn_layers = pn_layers
self.layers = ModuleList()
assert pn_kernel_size % 2 == 1
for i in range(pn_layers):
cur_layers = (
[
Conv1d(
mel_dim if i == 0 else pn_n_channels,
pn_n_channels if i < pn_layers - 1 else mel_dim,
kernel_size=pn_kernel_size,
padding="same",
device=device,
dtype=dtype,
),
BatchNorm1d(
pn_n_channels if i < pn_layers - 1 else mel_dim,
device=device,
dtype=dtype,
),
]
+ ([Tanh()] if i < pn_layers - 1 else [])
+ [Dropout(pn_dropout)]
)
self.layers.append(Sequential(*cur_layers))
self.reset_parameters()
self.layers.extend(stream_layers[:chunk_size])
stream_idx += chunk_size
self.layers.append(
weight_norm(
Conv1d(
mel_dim if mel_dim is not None else 80,
upsample_initial_channel,
7,
1,
padding="same",
device=device,
dtype=dtype,
)
)
)
self.layers.extend(stream_layers[stream_idx : stream_idx + chunk_size]) # noqa
stream_idx += chunk_size
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
ups = ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
out_pad = u % 2 if add_ups_out_pad else 0
ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2 + out_pad,
output_padding=out_pad,
device=device,
dtype=dtype,
)
)
)
ups.apply(init_weights)
self.layers.extend(ups)
self.layers.extend(stream_layers[stream_idx : stream_idx + chunk_size]) # noqa
stream_idx += chunk_size
for i in range(self.num_upsamples):
ch = upsample_initial_channel // (2 ** (i + 1))
for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):
self.layers.append(
ResBlock(
ch,
k,
d,
).to(device, dtype=dtype)
)
self.layers.extend(stream_layers[stream_idx:])
conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
conv_post.apply(init_weights)
self.layers.append(conv_post)
for u, k in zip(upsample_rates, upsample_kernel_sizes):
assert k == 2 * u, (k, u)
mean = torch.zeros((mel_dim,), dtype=torch.float)
scale = torch.zeros((mel_dim,), dtype=torch.float)
self.register_buffer("mean", mean)
self.register_buffer("scale", scale)
self.gcmvn_mean = torch.tensor(gcmvn_mean, device=device, dtype=dtype)
self.gcmvn_std = torch.tensor(gcmvn_std, device=device, dtype=dtype)
def reset_parameters(self) -> None:
for i in range(self.pn_layers):
init.xavier_uniform_(
self.layers[i][0].weight,
init.calculate_gain("tanh" if i < self.pn_layers - 1 else "linear"),
)
def gcmvn_denormalize(self, x: torch.Tensor) -> torch.Tensor:
if self.gcmvn_mean is None or self.gcmvn_std is None:
raise ValueError("gcmvn_mean is not set")
assert (
x.ndim == 3
and x.shape[2] == self.gcmvn_mean.shape[0]
and x.shape[2] == self.gcmvn_std.shape[0]
)
gcmvn_mean = self.gcmvn_mean.to(x)
gcmvn_std = self.gcmvn_std.to(x)
x = x * gcmvn_std.view(1, 1, -1).expand_as(x) # type: ignore[attr-defined]
return x + gcmvn_mean.view(1, 1, -1).expand_as(x) # type: ignore[attr-defined,no-any-return]
def forward(
self,
seqs: torch.Tensor,
tgt_lang: str,
prosody_input_seqs: torch.Tensor,
padding_mask: Optional[PaddingMask] = None,
prosody_padding_mask: Optional[PaddingMask] = None,
durations: Optional[torch.Tensor] = None,
duration_factor: float = 1.0,
min_duration: int = 0,
normalize_before: bool = True,
) -> List[torch.Tensor]:
# Here we are adding batch dimension for the pretssel
if seqs.ndim < 2:
seqs = seqs.unsqueeze(0)
if prosody_input_seqs.ndim < 3:
prosody_input_seqs = prosody_input_seqs.unsqueeze(0)
seqs, cond_embs = self.encoder_frontend(
seqs,
padding_mask,
prosody_input_seqs,
prosody_padding_mask,
tgt_lang,
)
seqs, padding_mask = self.encoder(seqs, padding_mask, cond_embs)
seqs, padding_mask = self.decoder_frontend(
seqs, padding_mask, durations, duration_factor, min_duration, cond_embs
)
seqs, padding_mask = self.decoder(seqs, padding_mask, cond_embs)
seqs = self.final_proj(seqs)
pn = seqs.transpose(1, 2) # B x T x C -> B x C x T
for i in range(self.pn_layers):
pn = self.layers[i](pn)
pn = pn.transpose(1, 2)
x = seqs + pn
x = self.gcmvn_denormalize(x)
wavs = []
for idx, _x in enumerate(x):
_x = _x[: durations[idx].sum()] # type: ignore[index]
if normalize_before:
_x = (_x - self.mean) / self.scale
_x = _x.transpose(1, 0).unsqueeze(0)
chunk_size = self.n_streams // 4
_x = self.layers[self.pn_layers + chunk_size](_x)
for i in range(self.num_upsamples):
_x = F.leaky_relu(_x, LRELU_SLOPE)
_x = self.layers[i + self.pn_layers + 1 + 2 * chunk_size](_x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.layers[
i * self.num_kernels
+ j
+ self.pn_layers
+ 3 * chunk_size
+ self.num_upsamples
+ 1
](_x)
else:
xs += self.layers[
i * self.num_kernels
+ j
+ self.pn_layers
+ 3 * chunk_size
+ self.num_upsamples
+ 1
](_x)
_x = xs / self.num_kernels # type: ignore
_x = F.leaky_relu(_x)
_x = self.layers[
self.pn_layers
+ self.n_streams
+ self.num_upsamples * (1 + self.num_kernels)
+ 1
](_x)
skip_output = _x
h = skip_output
for i1 in range(self.pn_layers, self.pn_layers + chunk_size):
h = self.layers[i1](h)
i1 += 2
for i2 in range(i1, i1 + chunk_size):
h = self.layers[i2](h)
i2 = i2 + self.num_upsamples + 1
for i3 in range(i2, i2 + chunk_size):
h = self.layers[i3](h)
i3 = i3 + (self.num_upsamples * self.num_kernels) + 1
for i4 in range(i3, i3 + chunk_size):
h = self.layers[i4](h)
h = h[:, :, : _x.size(-1)]
wavs.append(0.8 * h + torch.tanh(skip_output).squeeze(0))
return wavs
def remove_weight_norm(self) -> None:
i = self.pn_layers + 1
for j in range(self.num_upsamples):
remove_weight_norm(self.layers[i + j])
for k in range(self.num_upsamples * self.num_kernels):
self.layers[i + j + k + 1].remove_weight_norm()
remove_weight_norm(self.layers[self.pn_layers])
remove_weight_norm(
self.layers[
self.pn_layers + 1 + self.num_upsamples * (1 + self.num_kernels)
]
)
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