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Yatharth Sharma
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zipvoice/models/zipvoice_dialog.py
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358
zipvoice/models/zipvoice_dialog.py
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# Copyright 2025 Xiaomi Corp. (authors: Han Zhu)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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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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from typing import List
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import torch
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import torch.nn as nn
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from torch.nn.parallel import DistributedDataParallel as DDP
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from zipvoice.models.modules.zipformer_two_stream import TTSZipformerTwoStream
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from zipvoice.models.zipvoice import ZipVoice
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from zipvoice.utils.common import condition_time_mask_suffix, make_pad_mask, pad_labels
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class ZipVoiceDialog(ZipVoice):
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"""The ZipVoice-Dialog model."""
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def __init__(
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self,
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fm_decoder_downsampling_factor: List[int] = [1, 2, 4, 2, 1],
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fm_decoder_num_layers: List[int] = [2, 2, 4, 4, 4],
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fm_decoder_cnn_module_kernel: List[int] = [31, 15, 7, 15, 31],
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fm_decoder_feedforward_dim: int = 1536,
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fm_decoder_num_heads: int = 4,
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fm_decoder_dim: int = 512,
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text_encoder_num_layers: int = 4,
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text_encoder_feedforward_dim: int = 512,
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text_encoder_cnn_module_kernel: int = 9,
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text_encoder_num_heads: int = 4,
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text_encoder_dim: int = 192,
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time_embed_dim: int = 192,
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text_embed_dim: int = 192,
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query_head_dim: int = 32,
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value_head_dim: int = 12,
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pos_head_dim: int = 4,
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pos_dim: int = 48,
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feat_dim: int = 100,
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vocab_size: int = 26,
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pad_id: int = 0,
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spk_a_id: int = 360,
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spk_b_id: int = 361,
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):
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"""
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Initialize the model with specified configuration parameters.
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Args:
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fm_decoder_downsampling_factor: List of downsampling factors for each layer
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in the flow-matching decoder.
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fm_decoder_num_layers: List of the number of layers for each block in the
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flow-matching decoder.
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fm_decoder_cnn_module_kernel: List of kernel sizes for CNN modules in the
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flow-matching decoder.
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fm_decoder_feedforward_dim: Dimension of the feedforward network in the
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flow-matching decoder.
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fm_decoder_num_heads: Number of attention heads in the flow-matching
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decoder.
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fm_decoder_dim: Hidden dimension of the flow-matching decoder.
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text_encoder_num_layers: Number of layers in the text encoder.
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text_encoder_feedforward_dim: Dimension of the feedforward network in the
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text encoder.
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text_encoder_cnn_module_kernel: Kernel size for the CNN module in the
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text encoder.
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text_encoder_num_heads: Number of attention heads in the text encoder.
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text_encoder_dim: Hidden dimension of the text encoder.
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time_embed_dim: Dimension of the time embedding.
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text_embed_dim: Dimension of the text embedding.
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query_head_dim: Dimension of the query attention head.
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value_head_dim: Dimension of the value attention head.
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pos_head_dim: Dimension of the position attention head.
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pos_dim: Dimension of the positional encoding.
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feat_dim: Dimension of the acoustic features.
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vocab_size: Size of the vocabulary.
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pad_id: ID used for padding tokens.
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spk_a_id: ID of speaker A / [S1].
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spk_b_id: ID of speaker B / [S2].
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"""
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super().__init__(
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fm_decoder_downsampling_factor=fm_decoder_downsampling_factor,
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fm_decoder_num_layers=fm_decoder_num_layers,
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fm_decoder_cnn_module_kernel=fm_decoder_cnn_module_kernel,
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fm_decoder_feedforward_dim=fm_decoder_feedforward_dim,
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fm_decoder_num_heads=fm_decoder_num_heads,
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fm_decoder_dim=fm_decoder_dim,
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text_encoder_num_layers=text_encoder_num_layers,
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text_encoder_feedforward_dim=text_encoder_feedforward_dim,
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text_encoder_cnn_module_kernel=text_encoder_cnn_module_kernel,
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text_encoder_num_heads=text_encoder_num_heads,
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text_encoder_dim=text_encoder_dim,
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time_embed_dim=time_embed_dim,
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text_embed_dim=text_embed_dim,
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query_head_dim=query_head_dim,
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value_head_dim=value_head_dim,
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pos_head_dim=pos_head_dim,
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pos_dim=pos_dim,
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feat_dim=feat_dim,
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vocab_size=vocab_size,
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pad_id=pad_id,
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)
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self.spk_a_id = spk_a_id
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self.spk_b_id = spk_b_id
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self.spk_embed = nn.Embedding(2, feat_dim)
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torch.nn.init.normal_(self.spk_embed.weight, mean=0, std=0.1)
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def extract_spk_indices(self, tensor):
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turn_mask = ((tensor == self.spk_a_id) | (tensor == self.spk_b_id)).long()
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turn_counts = turn_mask.cumsum(dim=1)
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spk_mask = turn_counts % 2
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spk_mask = torch.where(tensor == self.pad_id, -1, spk_mask)
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spk_a_indices = torch.where(spk_mask == 0)
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spk_b_indices = torch.where(spk_mask == 1)
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return spk_a_indices, spk_b_indices
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def forward_text_embed(
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self,
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tokens: List[List[int]],
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):
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"""
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Get the text embeddings.
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Args:
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tokens: a list of list of token ids.
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Returns:
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embed: the text embeddings, shape (batch, seq_len, emb_dim).
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tokens_lens: the length of each token sequence, shape (batch,).
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"""
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device = (
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self.device if isinstance(self, DDP) else next(self.parameters()).device
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)
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tokens_padded = pad_labels(tokens, pad_id=self.pad_id, device=device) # (B, S)
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embed = self.embed(tokens_padded) # (B, S, C)
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spk_a_indices, spk_b_indices = self.extract_spk_indices(tokens_padded)
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tokens_lens = torch.tensor(
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[len(token) for token in tokens], dtype=torch.int64, device=device
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)
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tokens_padding_mask = make_pad_mask(tokens_lens, embed.shape[1]) # (B, S)
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embed = self.text_encoder(
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x=embed, t=None, padding_mask=tokens_padding_mask
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) # (B, S, C)
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embed[spk_a_indices] += self.spk_embed(torch.tensor(0, device=device)).to(
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embed.dtype
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)
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embed[spk_b_indices] += self.spk_embed(torch.tensor(1, device=device)).to(
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embed.dtype
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)
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return embed, tokens_lens
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def forward(
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self,
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tokens: List[List[int]],
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features: torch.Tensor,
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features_lens: torch.Tensor,
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noise: torch.Tensor,
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t: torch.Tensor,
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condition_drop_ratio: float = 0.0,
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) -> torch.Tensor:
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"""Forward pass of the model for training.
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Args:
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tokens: a list of list of token ids.
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features: the acoustic features, with the shape (batch, seq_len, feat_dim).
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features_lens: the length of each acoustic feature sequence, shape (batch,).
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noise: the intitial noise, with the shape (batch, seq_len, feat_dim).
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t: the time step, with the shape (batch, 1, 1).
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condition_drop_ratio: the ratio of dropped text condition.
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Returns:
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fm_loss: the flow-matching loss.
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"""
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(text_condition, padding_mask,) = self.forward_text_train(
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tokens=tokens,
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features_lens=features_lens,
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)
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speech_condition_mask = condition_time_mask_suffix(
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features_lens=features_lens,
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mask_percent=(0.5, 1.0),
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max_len=features.size(1),
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)
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speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
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if condition_drop_ratio > 0.0:
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drop_mask = (
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torch.rand(text_condition.size(0), 1, 1).to(text_condition.device)
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> condition_drop_ratio
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)
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text_condition = text_condition * drop_mask
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xt = features * t + noise * (1 - t)
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ut = features - noise # (B, T, F)
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vt = self.forward_fm_decoder(
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t=t,
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xt=xt,
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text_condition=text_condition,
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speech_condition=speech_condition,
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padding_mask=padding_mask,
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)
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loss_mask = speech_condition_mask & (~padding_mask)
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fm_loss = torch.mean((vt[loss_mask] - ut[loss_mask]) ** 2)
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return fm_loss
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class ZipVoiceDialogStereo(ZipVoiceDialog):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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required_params = {
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"feat_dim",
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"fm_decoder_downsampling_factor",
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"fm_decoder_num_layers",
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"fm_decoder_cnn_module_kernel",
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"fm_decoder_dim",
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"fm_decoder_feedforward_dim",
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"fm_decoder_num_heads",
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"query_head_dim",
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"pos_head_dim",
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"value_head_dim",
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"pos_dim",
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"time_embed_dim",
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}
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missing = [p for p in required_params if p not in kwargs]
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if missing:
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raise ValueError(f"Missing required parameters: {', '.join(missing)}")
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self.fm_decoder = TTSZipformerTwoStream(
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in_dim=(kwargs["feat_dim"] * 5, kwargs["feat_dim"] * 3),
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out_dim=(kwargs["feat_dim"] * 2, kwargs["feat_dim"]),
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downsampling_factor=kwargs["fm_decoder_downsampling_factor"],
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num_encoder_layers=kwargs["fm_decoder_num_layers"],
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cnn_module_kernel=kwargs["fm_decoder_cnn_module_kernel"],
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encoder_dim=kwargs["fm_decoder_dim"],
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feedforward_dim=kwargs["fm_decoder_feedforward_dim"],
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num_heads=kwargs["fm_decoder_num_heads"],
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query_head_dim=kwargs["query_head_dim"],
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pos_head_dim=kwargs["pos_head_dim"],
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value_head_dim=kwargs["value_head_dim"],
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pos_dim=kwargs["pos_dim"],
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use_time_embed=True,
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time_embed_dim=kwargs["time_embed_dim"],
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)
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def forward(
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self,
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tokens: List[List[int]],
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features: torch.Tensor,
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features_lens: torch.Tensor,
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noise: torch.Tensor,
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t: torch.Tensor,
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condition_drop_ratio: float = 0.0,
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se_weight: float = 1.0,
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) -> torch.Tensor:
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"""Forward pass of the model for training.
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Args:
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tokens: a list of list of token ids.
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features: the acoustic features, with the shape (batch, seq_len, feat_dim).
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features_lens: the length of each acoustic feature sequence, shape (batch,).
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noise: the intitial noise, with the shape (batch, seq_len, feat_dim).
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t: the time step, with the shape (batch, 1, 1).
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condition_drop_ratio: the ratio of dropped text condition.
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se_weight: the weight of the speaker exclusive loss.
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Returns:
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fm_loss: the flow-matching loss.
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"""
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(text_condition, padding_mask,) = self.forward_text_train(
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tokens=tokens,
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features_lens=features_lens,
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)
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speech_condition_mask = condition_time_mask_suffix(
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features_lens=features_lens,
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mask_percent=(0.5, 1.0),
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max_len=features.size(1),
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)
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speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
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if condition_drop_ratio > 0.0:
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drop_mask = (
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torch.rand(text_condition.size(0), 1, 1).to(text_condition.device)
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> condition_drop_ratio
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)
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text_condition = text_condition * drop_mask
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xt = features * t + noise * (1 - t)
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ut = features - noise # (B, T, F)
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vt = self.forward_fm_decoder(
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t=t,
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xt=xt,
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text_condition=text_condition,
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speech_condition=speech_condition,
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padding_mask=padding_mask,
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)
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loss_mask = speech_condition_mask & (~padding_mask)
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fm_loss = torch.mean((vt[loss_mask] - ut[loss_mask]) ** 2)
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if se_weight > 0:
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target = xt + vt * (1 - t)
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fbank_1 = target[:, :, : self.feat_dim]
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fbank_2 = target[:, :, self.feat_dim :]
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energy_loss = torch.mean(
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self.energy_based_loss(fbank_1, fbank_2, features)[loss_mask]
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)
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loss = fm_loss + energy_loss * se_weight
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else:
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loss = fm_loss
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return loss
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def energy_based_loss(self, fbank1, fbank2, gt_fbank):
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energy1 = self.energy(fbank1)
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energy2 = self.energy(fbank2)
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energy_thresholds = self.adaptive_threshold_from_gt(
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torch.cat(
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[
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gt_fbank[:, :, : self.feat_dim],
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gt_fbank[:, :, self.feat_dim :],
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],
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dim=1,
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)
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)
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both_speaking = (
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(energy1 > energy_thresholds) & (energy2 > energy_thresholds)
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).float()
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penalty = (
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both_speaking
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* (energy1 - energy_thresholds)
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* (energy2 - energy_thresholds)
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)
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return penalty
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def energy(self, fbank):
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return torch.mean(fbank, dim=-1)
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def adaptive_threshold_from_gt(self, gt_fbank, percentile=50):
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frame_energies = self.energy(gt_fbank)
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thresholds = torch.quantile(frame_energies, q=percentile / 100, dim=1)
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return thresholds.unsqueeze(1)
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