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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 dataclasses import dataclass
from typing import Any, Dict, List, Literal, Optional, Tuple
from fairseq2.data import VocabularyInfo
from fairseq2.models.utils.arch_registry import ArchitectureRegistry
from fairseq2.nn.embedding import StandardEmbedding, init_scaled_embedding
from fairseq2.nn.position_encoder import SinusoidalPositionEncoder
from fairseq2.nn.projection import Linear
from fairseq2.nn.transformer import (
MultiheadAttention,
StandardMultiheadAttention,
TransformerNormOrder,
create_default_sdpa,
)
from fairseq2.typing import DataType, Device
from torch.nn import Conv1d
from seamless_communication.models.generator.ecapa_tdnn_builder import (
EcapaTDNNBuilder,
EcapaTDNNConfig,
ecapa_tdnn_archs,
)
from seamless_communication.models.generator.vocoder import (
PretsselDecoderFrontend,
PretsselEncoderFrontend,
PretsselVocoder,
)
from seamless_communication.models.unity.fft_decoder import FeedForwardTransformer
from seamless_communication.models.unity.fft_decoder_layer import (
Conv1dBlock,
FeedForwardTransformerLayer,
)
from seamless_communication.models.unity.length_regulator import (
VarianceAdaptor,
VariancePredictor,
)
from seamless_communication.models.unity.t2u_builder import VariancePredictorConfig
@dataclass
class PretsselEncoderFrontendConfig:
prosody_encoder_config: EcapaTDNNConfig
dropout: float
lang_embed_dim: Optional[int] = None
@dataclass
class FFTLayerConfig:
attention_heads: int
hidden_dim: int
kernel_size: int
dropout: float
conv1d_dropout: float
film_cond_dim: int
use_film: bool = False
@dataclass
class PretsselDecoderFrontendConfig:
upsampling_type: Literal["gaussian", "hard"]
variance_predictor_config: VariancePredictorConfig
add_variance_parallel: bool
@dataclass
class VocoderConfig:
"""Holds the configuration of a Vocoder model."""
encoder_frontend_config: PretsselEncoderFrontendConfig
fft_layer_config: FFTLayerConfig
decoder_frontend_config: PretsselDecoderFrontendConfig
pn_conv_dim: int
pn_layers: int
pn_conv_kernel_size: int
pn_dropout: float
vocab_info: VocabularyInfo
model_dim: int
max_seq_len: int
encoder_layers: int
decoder_layers: int
mel_dim: int
langs: List # type: ignore[type-arg]
upsample_rates: List[int]
upsample_kernel_sizes: List[int]
upsample_initial_channel: int
resblock_kernel_sizes: List[int]
resblock_dilation_sizes: List[List[int]]
channels: int
dimension: int
n_filters: int
ratios: List[int]
norm: Literal["none", "weight_norm", "spectral_norm", "time_group_norm"]
norm_params: Dict[str, Any]
kernel_size: int
last_kernel_size: int
residual_kernel_size: int
causal: bool
pad_mode: str
true_skip: bool
compress: int
lstm: int
disable_norm_outer_blocks: int
trim_right_ratio: float
gcmvn_stats: Dict[str, List] # type: ignore[type-arg]
vocoder_archs = ArchitectureRegistry[VocoderConfig]("vocoder_pretssel")
vocoder_arch = vocoder_archs.decorator
def pretssel_config() -> (
Tuple[PretsselEncoderFrontendConfig, FFTLayerConfig, PretsselDecoderFrontendConfig]
):
prosody_encoder_config = ecapa_tdnn_archs.get_config("base")
encoder_frontend_config = PretsselEncoderFrontendConfig(
prosody_encoder_config=prosody_encoder_config,
dropout=0.2,
lang_embed_dim=64,
)
fft_layer_config = FFTLayerConfig(
attention_heads=2,
hidden_dim=1024,
kernel_size=9,
dropout=0.0,
conv1d_dropout=0.2,
use_film=True,
film_cond_dim=576,
)
variance_predictor_config = VariancePredictorConfig(
var_pred_hidden_dim=512,
var_pred_kernel_size=5,
var_pred_dropout=0.5,
use_film=True,
film_cond_dim=576,
)
decoder_frontend_config = PretsselDecoderFrontendConfig(
upsampling_type="gaussian",
variance_predictor_config=variance_predictor_config,
add_variance_parallel=True,
)
return (
encoder_frontend_config,
fft_layer_config,
decoder_frontend_config,
)
@vocoder_arch("16khz")
def _16khz_vocoder() -> VocoderConfig:
(
encoder_frontend_config,
fft_layer_config,
decoder_frontend_config,
) = pretssel_config()
return VocoderConfig(
encoder_frontend_config=encoder_frontend_config,
fft_layer_config=fft_layer_config,
decoder_frontend_config=decoder_frontend_config,
pn_conv_dim=512,
pn_layers=5,
pn_conv_kernel_size=5,
pn_dropout=0.5,
vocab_info=VocabularyInfo(
size=10004, unk_idx=3, bos_idx=0, eos_idx=2, pad_idx=1
),
model_dim=256,
max_seq_len=10000,
encoder_layers=4,
decoder_layers=4,
mel_dim=80,
langs=[],
upsample_rates=[5, 4, 4, 2],
upsample_kernel_sizes=[10, 8, 8, 4],
upsample_initial_channel=512,
resblock_kernel_sizes=[3, 7, 11],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
channels=1,
dimension=128,
n_filters=32,
ratios=[8, 5, 4, 2],
norm="weight_norm",
norm_params={},
kernel_size=7,
last_kernel_size=7,
residual_kernel_size=3,
causal=False,
pad_mode="constant",
true_skip=True,
compress=2,
lstm=2,
disable_norm_outer_blocks=0,
trim_right_ratio=1.0,
gcmvn_stats={},
)
@vocoder_arch("24khz")
def _24khz_vocoder() -> VocoderConfig:
(
encoder_frontend_config,
fft_layer_config,
decoder_frontend_config,
) = pretssel_config()
return VocoderConfig(
encoder_frontend_config=encoder_frontend_config,
fft_layer_config=fft_layer_config,
decoder_frontend_config=decoder_frontend_config,
pn_conv_dim=512,
pn_layers=5,
pn_conv_kernel_size=5,
pn_dropout=0.5,
vocab_info=VocabularyInfo(
size=10004, unk_idx=3, bos_idx=0, eos_idx=2, pad_idx=1
),
model_dim=256,
max_seq_len=10000,
encoder_layers=4,
decoder_layers=4,
mel_dim=80,
langs=[],
upsample_rates=[5, 4, 4, 3],
upsample_kernel_sizes=[10, 8, 8, 6],
upsample_initial_channel=512,
resblock_kernel_sizes=[3, 7, 11],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
channels=1,
dimension=128,
n_filters=32,
ratios=[8, 5, 4, 2],
norm="weight_norm",
norm_params={},
kernel_size=7,
last_kernel_size=7,
residual_kernel_size=3,
causal=False,
pad_mode="constant",
true_skip=True,
compress=2,
lstm=2,
disable_norm_outer_blocks=0,
trim_right_ratio=1.0,
gcmvn_stats={},
)
class PretsselVocoderBuilder:
config: VocoderConfig
prosody_encoder_builder: EcapaTDNNBuilder
device: Optional[Device] = None
dtype: Optional[DataType] = None
def __init__(
self,
config: VocoderConfig,
prosody_encoder_builder: EcapaTDNNBuilder,
*,
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
) -> None:
"""
:param config:
The configuration to use.
:param device:
The device on which to initialize modules.
:param dtype:
The data type of module parameters and buffers.
"""
self.config = config
self.prosody_encoder_builder = prosody_encoder_builder
self.device, self.dtype = device, dtype
def build_embed_tokens(self) -> StandardEmbedding:
"""Build a unit embedding table."""
return StandardEmbedding(
num_embeddings=self.config.vocab_info.size,
embedding_dim=self.config.model_dim,
init_fn=init_scaled_embedding,
device=self.device,
dtype=self.dtype,
)
def build_fft(self, num_layers: int) -> FeedForwardTransformer:
"""Build a Transformer encoder."""
layers = [self.build_fft_layer() for _ in range(num_layers)]
return FeedForwardTransformer(
layers,
norm_order=TransformerNormOrder.POST,
device=self.device,
dtype=self.dtype,
)
def build_fft_layer(self) -> FeedForwardTransformerLayer:
"""Build a Transformer decoder layer."""
self_attn = self.build_attention(self.config.fft_layer_config.attention_heads)
conv1d = Conv1dBlock(
self.config.model_dim,
self.config.fft_layer_config.hidden_dim,
self.config.fft_layer_config.kernel_size,
bias=True,
device=self.device,
dtype=self.dtype,
)
return FeedForwardTransformerLayer(
self_attn,
conv1d,
dropout_p=0.0, # fairseq1 doesn't have this
conv1d_dropout_p=self.config.fft_layer_config.conv1d_dropout,
use_film=self.config.fft_layer_config.use_film,
film_cond_dim=self.config.fft_layer_config.film_cond_dim,
device=self.device,
dtype=self.dtype,
)
def build_attention(self, num_heads: int) -> MultiheadAttention:
"""Build a Transformer multi-head attention layer."""
sdpa = create_default_sdpa(attn_dropout_p=self.config.fft_layer_config.dropout)
return StandardMultiheadAttention(
self.config.model_dim,
num_heads,
sdpa=sdpa,
device=self.device,
dtype=self.dtype,
)
def build_variance_adaptor(
self,
decoder_frontend_config: PretsselDecoderFrontendConfig,
) -> VarianceAdaptor:
"""Build a variance adaptor module."""
variance_predictor_config = decoder_frontend_config.variance_predictor_config
pitch_predictor = VariancePredictor(
self.config.model_dim,
variance_predictor_config.var_pred_hidden_dim,
variance_predictor_config.var_pred_kernel_size,
variance_predictor_config.var_pred_dropout,
use_film=variance_predictor_config.use_film,
film_cond_dim=variance_predictor_config.film_cond_dim,
device=self.device,
dtype=self.dtype,
)
embed_pitch = Conv1d(1, self.config.model_dim, kernel_size=1)
vuv_predictor = VariancePredictor(
self.config.model_dim,
variance_predictor_config.var_pred_hidden_dim,
variance_predictor_config.var_pred_kernel_size,
variance_predictor_config.var_pred_dropout,
use_film=variance_predictor_config.use_film,
film_cond_dim=variance_predictor_config.film_cond_dim,
device=self.device,
dtype=self.dtype,
)
energy_predictor = VariancePredictor(
self.config.model_dim,
variance_predictor_config.var_pred_hidden_dim,
variance_predictor_config.var_pred_kernel_size,
variance_predictor_config.var_pred_dropout,
use_film=variance_predictor_config.use_film,
film_cond_dim=variance_predictor_config.film_cond_dim,
device=self.device,
dtype=self.dtype,
)
embed_energy = Conv1d(1, self.config.model_dim, kernel_size=1)
variance_adaptor = VarianceAdaptor(
duration_predictor=None,
pitch_predictor=pitch_predictor,
embed_pitch=embed_pitch,
vuv_predictor=vuv_predictor,
energy_predictor=energy_predictor,
embed_energy=embed_energy,
add_variance_parallel=decoder_frontend_config.add_variance_parallel,
upsampling_type=decoder_frontend_config.upsampling_type,
)
return variance_adaptor
def build_model(self) -> PretsselVocoder:
"""build the pretssel vocoder."""
prosody_encoder = self.prosody_encoder_builder.build_model()
embed_tokens = self.build_embed_tokens()
embed_positions = SinusoidalPositionEncoder(
self.config.model_dim,
self.config.max_seq_len,
_legacy_pad_idx=self.config.vocab_info.pad_idx,
device=self.device,
)
lang_to_index = {l: i for i, l in enumerate(self.config.langs)}
encoder_frontend = PretsselEncoderFrontend(
prosody_encoder,
embed_tokens,
embed_positions,
lang_to_index,
lang_embed_dim=self.config.encoder_frontend_config.lang_embed_dim,
dropout_p=self.config.encoder_frontend_config.dropout,
device=self.device,
dtype=self.dtype,
)
encoder = self.build_fft(self.config.encoder_layers)
variance_adaptor = self.build_variance_adaptor(
self.config.decoder_frontend_config
)
decoder_frontend = PretsselDecoderFrontend(
variance_adaptor,
embed_positions,
device=self.device,
dtype=self.dtype,
)
decoder = self.build_fft(self.config.decoder_layers)
final_proj = Linear(
self.config.model_dim,
self.config.mel_dim,
bias=True,
device=self.device,
dtype=self.dtype,
)
gcmvn_mean = gcmvn_std = None
if self.config.gcmvn_stats is not None:
gcmvn_mean = self.config.gcmvn_stats["mean"]
gcmvn_std = self.config.gcmvn_stats["std"]
vocoder = PretsselVocoder(
encoder_frontend=encoder_frontend,
encoder=encoder,
decoder_frontend=decoder_frontend,
decoder=decoder,
final_proj=final_proj,
pn_n_channels=self.config.pn_conv_dim,
pn_kernel_size=self.config.pn_conv_kernel_size,
pn_layers=self.config.pn_layers,
pn_dropout=self.config.pn_dropout,
upsample_rates=self.config.upsample_rates,
upsample_kernel_sizes=self.config.upsample_kernel_sizes,
upsample_initial_channel=self.config.upsample_initial_channel,
resblock_kernel_sizes=self.config.resblock_kernel_sizes,
resblock_dilation_sizes=self.config.resblock_dilation_sizes,
channels=self.config.channels,
dimension=self.config.dimension,
n_filters=self.config.n_filters,
ratios=self.config.ratios,
norm=self.config.norm,
norm_params=self.config.norm_params,
kernel_size=self.config.kernel_size,
last_kernel_size=self.config.last_kernel_size,
residual_kernel_size=self.config.residual_kernel_size,
causal=self.config.causal,
pad_mode=self.config.pad_mode,
true_skip=self.config.true_skip,
compress=self.config.compress,
lstm=self.config.lstm,
disable_norm_outer_blocks=self.config.disable_norm_outer_blocks,
trim_right_ratio=self.config.trim_right_ratio,
gcmvn_mean=gcmvn_mean,
gcmvn_std=gcmvn_std,
)
vocoder.to(dtype=self.dtype, device=self.device)
return vocoder
def create_vocoder_model(
config: VocoderConfig,
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
) -> PretsselVocoder:
prosody_encoder_builder = EcapaTDNNBuilder(
config.encoder_frontend_config.prosody_encoder_config,
device=device,
dtype=dtype,
)
return PretsselVocoderBuilder(
config, prosody_encoder_builder, device=device, dtype=dtype
).build_model()
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