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# Copyright 2025 Xiaomi Corp. (authors: Han Zhu)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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.
from typing import List
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
from zipvoice.models.modules.zipformer_two_stream import TTSZipformerTwoStream
from zipvoice.models.zipvoice import ZipVoice
from zipvoice.utils.common import condition_time_mask_suffix, make_pad_mask, pad_labels
class ZipVoiceDialog(ZipVoice):
"""The ZipVoice-Dialog model."""
def __init__(
self,
fm_decoder_downsampling_factor: List[int] = [1, 2, 4, 2, 1],
fm_decoder_num_layers: List[int] = [2, 2, 4, 4, 4],
fm_decoder_cnn_module_kernel: List[int] = [31, 15, 7, 15, 31],
fm_decoder_feedforward_dim: int = 1536,
fm_decoder_num_heads: int = 4,
fm_decoder_dim: int = 512,
text_encoder_num_layers: int = 4,
text_encoder_feedforward_dim: int = 512,
text_encoder_cnn_module_kernel: int = 9,
text_encoder_num_heads: int = 4,
text_encoder_dim: int = 192,
time_embed_dim: int = 192,
text_embed_dim: int = 192,
query_head_dim: int = 32,
value_head_dim: int = 12,
pos_head_dim: int = 4,
pos_dim: int = 48,
feat_dim: int = 100,
vocab_size: int = 26,
pad_id: int = 0,
spk_a_id: int = 360,
spk_b_id: int = 361,
):
"""
Initialize the model with specified configuration parameters.
Args:
fm_decoder_downsampling_factor: List of downsampling factors for each layer
in the flow-matching decoder.
fm_decoder_num_layers: List of the number of layers for each block in the
flow-matching decoder.
fm_decoder_cnn_module_kernel: List of kernel sizes for CNN modules in the
flow-matching decoder.
fm_decoder_feedforward_dim: Dimension of the feedforward network in the
flow-matching decoder.
fm_decoder_num_heads: Number of attention heads in the flow-matching
decoder.
fm_decoder_dim: Hidden dimension of the flow-matching decoder.
text_encoder_num_layers: Number of layers in the text encoder.
text_encoder_feedforward_dim: Dimension of the feedforward network in the
text encoder.
text_encoder_cnn_module_kernel: Kernel size for the CNN module in the
text encoder.
text_encoder_num_heads: Number of attention heads in the text encoder.
text_encoder_dim: Hidden dimension of the text encoder.
time_embed_dim: Dimension of the time embedding.
text_embed_dim: Dimension of the text embedding.
query_head_dim: Dimension of the query attention head.
value_head_dim: Dimension of the value attention head.
pos_head_dim: Dimension of the position attention head.
pos_dim: Dimension of the positional encoding.
feat_dim: Dimension of the acoustic features.
vocab_size: Size of the vocabulary.
pad_id: ID used for padding tokens.
spk_a_id: ID of speaker A / [S1].
spk_b_id: ID of speaker B / [S2].
"""
super().__init__(
fm_decoder_downsampling_factor=fm_decoder_downsampling_factor,
fm_decoder_num_layers=fm_decoder_num_layers,
fm_decoder_cnn_module_kernel=fm_decoder_cnn_module_kernel,
fm_decoder_feedforward_dim=fm_decoder_feedforward_dim,
fm_decoder_num_heads=fm_decoder_num_heads,
fm_decoder_dim=fm_decoder_dim,
text_encoder_num_layers=text_encoder_num_layers,
text_encoder_feedforward_dim=text_encoder_feedforward_dim,
text_encoder_cnn_module_kernel=text_encoder_cnn_module_kernel,
text_encoder_num_heads=text_encoder_num_heads,
text_encoder_dim=text_encoder_dim,
time_embed_dim=time_embed_dim,
text_embed_dim=text_embed_dim,
query_head_dim=query_head_dim,
value_head_dim=value_head_dim,
pos_head_dim=pos_head_dim,
pos_dim=pos_dim,
feat_dim=feat_dim,
vocab_size=vocab_size,
pad_id=pad_id,
)
self.spk_a_id = spk_a_id
self.spk_b_id = spk_b_id
self.spk_embed = nn.Embedding(2, feat_dim)
torch.nn.init.normal_(self.spk_embed.weight, mean=0, std=0.1)
def extract_spk_indices(self, tensor):
turn_mask = ((tensor == self.spk_a_id) | (tensor == self.spk_b_id)).long()
turn_counts = turn_mask.cumsum(dim=1)
spk_mask = turn_counts % 2
spk_mask = torch.where(tensor == self.pad_id, -1, spk_mask)
spk_a_indices = torch.where(spk_mask == 0)
spk_b_indices = torch.where(spk_mask == 1)
return spk_a_indices, spk_b_indices
def forward_text_embed(
self,
tokens: List[List[int]],
):
"""
Get the text embeddings.
Args:
tokens: a list of list of token ids.
Returns:
embed: the text embeddings, shape (batch, seq_len, emb_dim).
tokens_lens: the length of each token sequence, shape (batch,).
"""
device = (
self.device if isinstance(self, DDP) else next(self.parameters()).device
)
tokens_padded = pad_labels(tokens, pad_id=self.pad_id, device=device) # (B, S)
embed = self.embed(tokens_padded) # (B, S, C)
spk_a_indices, spk_b_indices = self.extract_spk_indices(tokens_padded)
tokens_lens = torch.tensor(
[len(token) for token in tokens], dtype=torch.int64, device=device
)
tokens_padding_mask = make_pad_mask(tokens_lens, embed.shape[1]) # (B, S)
embed = self.text_encoder(
x=embed, t=None, padding_mask=tokens_padding_mask
) # (B, S, C)
embed[spk_a_indices] += self.spk_embed(torch.tensor(0, device=device)).to(
embed.dtype
)
embed[spk_b_indices] += self.spk_embed(torch.tensor(1, device=device)).to(
embed.dtype
)
return embed, tokens_lens
def forward(
self,
tokens: List[List[int]],
features: torch.Tensor,
features_lens: torch.Tensor,
noise: torch.Tensor,
t: torch.Tensor,
condition_drop_ratio: float = 0.0,
) -> torch.Tensor:
"""Forward pass of the model for training.
Args:
tokens: a list of list of token ids.
features: the acoustic features, with the shape (batch, seq_len, feat_dim).
features_lens: the length of each acoustic feature sequence, shape (batch,).
noise: the intitial noise, with the shape (batch, seq_len, feat_dim).
t: the time step, with the shape (batch, 1, 1).
condition_drop_ratio: the ratio of dropped text condition.
Returns:
fm_loss: the flow-matching loss.
"""
(text_condition, padding_mask,) = self.forward_text_train(
tokens=tokens,
features_lens=features_lens,
)
speech_condition_mask = condition_time_mask_suffix(
features_lens=features_lens,
mask_percent=(0.5, 1.0),
max_len=features.size(1),
)
speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
if condition_drop_ratio > 0.0:
drop_mask = (
torch.rand(text_condition.size(0), 1, 1).to(text_condition.device)
> condition_drop_ratio
)
text_condition = text_condition * drop_mask
xt = features * t + noise * (1 - t)
ut = features - noise # (B, T, F)
vt = self.forward_fm_decoder(
t=t,
xt=xt,
text_condition=text_condition,
speech_condition=speech_condition,
padding_mask=padding_mask,
)
loss_mask = speech_condition_mask & (~padding_mask)
fm_loss = torch.mean((vt[loss_mask] - ut[loss_mask]) ** 2)
return fm_loss
class ZipVoiceDialogStereo(ZipVoiceDialog):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
required_params = {
"feat_dim",
"fm_decoder_downsampling_factor",
"fm_decoder_num_layers",
"fm_decoder_cnn_module_kernel",
"fm_decoder_dim",
"fm_decoder_feedforward_dim",
"fm_decoder_num_heads",
"query_head_dim",
"pos_head_dim",
"value_head_dim",
"pos_dim",
"time_embed_dim",
}
missing = [p for p in required_params if p not in kwargs]
if missing:
raise ValueError(f"Missing required parameters: {', '.join(missing)}")
self.fm_decoder = TTSZipformerTwoStream(
in_dim=(kwargs["feat_dim"] * 5, kwargs["feat_dim"] * 3),
out_dim=(kwargs["feat_dim"] * 2, kwargs["feat_dim"]),
downsampling_factor=kwargs["fm_decoder_downsampling_factor"],
num_encoder_layers=kwargs["fm_decoder_num_layers"],
cnn_module_kernel=kwargs["fm_decoder_cnn_module_kernel"],
encoder_dim=kwargs["fm_decoder_dim"],
feedforward_dim=kwargs["fm_decoder_feedforward_dim"],
num_heads=kwargs["fm_decoder_num_heads"],
query_head_dim=kwargs["query_head_dim"],
pos_head_dim=kwargs["pos_head_dim"],
value_head_dim=kwargs["value_head_dim"],
pos_dim=kwargs["pos_dim"],
use_time_embed=True,
time_embed_dim=kwargs["time_embed_dim"],
)
def forward(
self,
tokens: List[List[int]],
features: torch.Tensor,
features_lens: torch.Tensor,
noise: torch.Tensor,
t: torch.Tensor,
condition_drop_ratio: float = 0.0,
se_weight: float = 1.0,
) -> torch.Tensor:
"""Forward pass of the model for training.
Args:
tokens: a list of list of token ids.
features: the acoustic features, with the shape (batch, seq_len, feat_dim).
features_lens: the length of each acoustic feature sequence, shape (batch,).
noise: the intitial noise, with the shape (batch, seq_len, feat_dim).
t: the time step, with the shape (batch, 1, 1).
condition_drop_ratio: the ratio of dropped text condition.
se_weight: the weight of the speaker exclusive loss.
Returns:
fm_loss: the flow-matching loss.
"""
(text_condition, padding_mask,) = self.forward_text_train(
tokens=tokens,
features_lens=features_lens,
)
speech_condition_mask = condition_time_mask_suffix(
features_lens=features_lens,
mask_percent=(0.5, 1.0),
max_len=features.size(1),
)
speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
if condition_drop_ratio > 0.0:
drop_mask = (
torch.rand(text_condition.size(0), 1, 1).to(text_condition.device)
> condition_drop_ratio
)
text_condition = text_condition * drop_mask
xt = features * t + noise * (1 - t)
ut = features - noise # (B, T, F)
vt = self.forward_fm_decoder(
t=t,
xt=xt,
text_condition=text_condition,
speech_condition=speech_condition,
padding_mask=padding_mask,
)
loss_mask = speech_condition_mask & (~padding_mask)
fm_loss = torch.mean((vt[loss_mask] - ut[loss_mask]) ** 2)
if se_weight > 0:
target = xt + vt * (1 - t)
fbank_1 = target[:, :, : self.feat_dim]
fbank_2 = target[:, :, self.feat_dim :]
energy_loss = torch.mean(
self.energy_based_loss(fbank_1, fbank_2, features)[loss_mask]
)
loss = fm_loss + energy_loss * se_weight
else:
loss = fm_loss
return loss
def energy_based_loss(self, fbank1, fbank2, gt_fbank):
energy1 = self.energy(fbank1)
energy2 = self.energy(fbank2)
energy_thresholds = self.adaptive_threshold_from_gt(
torch.cat(
[
gt_fbank[:, :, : self.feat_dim],
gt_fbank[:, :, self.feat_dim :],
],
dim=1,
)
)
both_speaking = (
(energy1 > energy_thresholds) & (energy2 > energy_thresholds)
).float()
penalty = (
both_speaking
* (energy1 - energy_thresholds)
* (energy2 - energy_thresholds)
)
return penalty
def energy(self, fbank):
return torch.mean(fbank, dim=-1)
def adaptive_threshold_from_gt(self, gt_fbank, percentile=50):
frame_energies = self.energy(gt_fbank)
thresholds = torch.quantile(frame_energies, q=percentile / 100, dim=1)
return thresholds.unsqueeze(1)
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