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Yatharth Sharma
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zipvoice/models/zipvoice.py
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534
zipvoice/models/zipvoice.py
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# Copyright 2024 Xiaomi Corp. (authors: Wei Kang
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# 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, Optional
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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.solver import EulerSolver
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from zipvoice.models.modules.zipformer import TTSZipformer
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from zipvoice.utils.common import (
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condition_time_mask,
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get_tokens_index,
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make_pad_mask,
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pad_labels,
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prepare_avg_tokens_durations,
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)
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class ZipVoice(nn.Module):
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"""The ZipVoice 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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):
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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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"""
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super().__init__()
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self.fm_decoder = TTSZipformer(
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in_dim=feat_dim * 3,
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out_dim=feat_dim,
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downsampling_factor=fm_decoder_downsampling_factor,
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num_encoder_layers=fm_decoder_num_layers,
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cnn_module_kernel=fm_decoder_cnn_module_kernel,
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encoder_dim=fm_decoder_dim,
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feedforward_dim=fm_decoder_feedforward_dim,
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num_heads=fm_decoder_num_heads,
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query_head_dim=query_head_dim,
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pos_head_dim=pos_head_dim,
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value_head_dim=value_head_dim,
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pos_dim=pos_dim,
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use_time_embed=True,
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time_embed_dim=time_embed_dim,
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)
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self.text_encoder = TTSZipformer(
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in_dim=text_embed_dim,
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out_dim=feat_dim,
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downsampling_factor=1,
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num_encoder_layers=text_encoder_num_layers,
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cnn_module_kernel=text_encoder_cnn_module_kernel,
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encoder_dim=text_encoder_dim,
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feedforward_dim=text_encoder_feedforward_dim,
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num_heads=text_encoder_num_heads,
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query_head_dim=query_head_dim,
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pos_head_dim=pos_head_dim,
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value_head_dim=value_head_dim,
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pos_dim=pos_dim,
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use_time_embed=False,
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)
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self.feat_dim = feat_dim
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self.text_embed_dim = text_embed_dim
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self.pad_id = pad_id
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self.embed = nn.Embedding(vocab_size, text_embed_dim)
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self.solver = EulerSolver(self, func_name="forward_fm_decoder")
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def forward_fm_decoder(
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self,
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t: torch.Tensor,
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xt: torch.Tensor,
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text_condition: torch.Tensor,
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speech_condition: torch.Tensor,
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padding_mask: Optional[torch.Tensor] = None,
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guidance_scale: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Compute velocity.
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Args:
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t: A tensor of shape (N, 1, 1) or a tensor of a float,
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in the range of (0, 1).
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xt: the input of the current timestep, including condition
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embeddings and noisy acoustic features.
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text_condition: the text condition embeddings, with the
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shape (batch, seq_len, emb_dim).
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speech_condition: the speech condition embeddings, with the
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shape (batch, seq_len, emb_dim).
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padding_mask: The mask for padding, True means masked
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position, with the shape (N, T).
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guidance_scale: The guidance scale in classifier-free guidance,
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which is a tensor of shape (N, 1, 1) or a tensor of a float.
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Returns:
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predicted velocity, with the shape (batch, seq_len, emb_dim).
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"""
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xt = torch.cat([xt, text_condition, speech_condition], dim=2)
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assert t.dim() in (0, 3)
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# Handle t with the shape (N, 1, 1):
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# squeeze the last dimension if it's size is 1.
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while t.dim() > 1 and t.size(-1) == 1:
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t = t.squeeze(-1)
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# Handle t with a single value: expand to the size of batch size.
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if t.dim() == 0:
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t = t.repeat(xt.shape[0])
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if guidance_scale is not None:
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while guidance_scale.dim() > 1 and guidance_scale.size(-1) == 1:
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guidance_scale = guidance_scale.squeeze(-1)
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if guidance_scale.dim() == 0:
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guidance_scale = guidance_scale.repeat(xt.shape[0])
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vt = self.fm_decoder(
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x=xt, t=t, padding_mask=padding_mask, guidance_scale=guidance_scale
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)
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else:
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vt = self.fm_decoder(x=xt, t=t, padding_mask=padding_mask)
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return vt
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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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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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return embed, tokens_lens
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def forward_text_condition(
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self,
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embed: torch.Tensor,
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tokens_lens: torch.Tensor,
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features_lens: torch.Tensor,
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):
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"""
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Get the text condition with the same length of the acoustic feature.
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Args:
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embed: the text embeddings, shape (batch, token_seq_len, emb_dim).
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tokens_lens: the length of each token sequence, shape (batch,).
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features_lens: the length of each acoustic feature sequence,
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shape (batch,).
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Returns:
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text_condition: the text condition, shape
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(batch, feature_seq_len, emb_dim).
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padding_mask: the padding mask of text condition, shape
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(batch, feature_seq_len).
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"""
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num_frames = int(features_lens.max())
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padding_mask = make_pad_mask(features_lens, max_len=num_frames) # (B, T)
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tokens_durations = prepare_avg_tokens_durations(features_lens, tokens_lens)
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tokens_index = get_tokens_index(tokens_durations, num_frames).to(
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embed.device
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) # (B, T)
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text_condition = torch.gather(
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embed,
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dim=1,
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index=tokens_index.unsqueeze(-1).expand(
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embed.size(0), num_frames, embed.size(-1)
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),
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) # (B, T, F)
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return text_condition, padding_mask
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def forward_text_train(
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self,
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tokens: List[List[int]],
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features_lens: torch.Tensor,
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):
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"""
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Process text for training, given text tokens and real feature lengths.
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"""
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embed, tokens_lens = self.forward_text_embed(tokens)
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text_condition, padding_mask = self.forward_text_condition(
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embed, tokens_lens, features_lens
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)
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return (
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text_condition,
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padding_mask,
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)
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def forward_text_inference_gt_duration(
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self,
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tokens: List[List[int]],
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features_lens: torch.Tensor,
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prompt_tokens: List[List[int]],
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prompt_features_lens: torch.Tensor,
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):
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"""
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Process text for inference, given text tokens, real feature lengths and prompts.
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"""
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tokens = [
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prompt_token + token for prompt_token, token in zip(prompt_tokens, tokens)
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]
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features_lens = prompt_features_lens + features_lens
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embed, tokens_lens = self.forward_text_embed(tokens)
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text_condition, padding_mask = self.forward_text_condition(
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embed, tokens_lens, features_lens
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)
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return text_condition, padding_mask
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def forward_text_inference_ratio_duration(
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self,
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tokens: List[List[int]],
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prompt_tokens: List[List[int]],
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prompt_features_lens: torch.Tensor,
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speed: float,
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):
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"""
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Process text for inference, given text tokens and prompts,
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feature lengths are predicted with the ratio of token numbers.
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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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cat_tokens = [
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prompt_token + token for prompt_token, token in zip(prompt_tokens, tokens)
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]
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prompt_tokens_lens = torch.tensor(
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[len(token) for token in prompt_tokens],
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dtype=torch.int64,
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device=device,
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)
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tokens_lens = torch.tensor(
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[len(token) for token in tokens],
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dtype=torch.int64,
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device=device,
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)
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cat_embed, cat_tokens_lens = self.forward_text_embed(cat_tokens)
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features_lens = prompt_features_lens + torch.ceil(
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(prompt_features_lens / prompt_tokens_lens * tokens_lens / speed)
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).to(dtype=torch.int64)
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text_condition, padding_mask = self.forward_text_condition(
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cat_embed, cat_tokens_lens, features_lens
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)
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return text_condition, padding_mask
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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(
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features_lens=features_lens,
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mask_percent=(0.7, 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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def sample(
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self,
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tokens: List[List[int]],
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prompt_tokens: List[List[int]],
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prompt_features: torch.Tensor,
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prompt_features_lens: torch.Tensor,
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features_lens: Optional[torch.Tensor] = None,
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speed: float = 1.0,
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t_shift: float = 1.0,
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duration: str = "predict",
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num_step: int = 5,
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guidance_scale: float = 0.5,
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) -> torch.Tensor:
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"""
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Generate acoustic features, given text tokens, prompts feature
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and prompt transcription's text tokens.
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Args:
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tokens: a list of list of text tokens.
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prompt_tokens: a list of list of prompt tokens.
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prompt_features: the prompt feature with the shape
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(batch_size, seq_len, feat_dim).
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prompt_features_lens: the length of each prompt feature,
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with the shape (batch_size,).
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features_lens: the length of the predicted eature, with the
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shape (batch_size,). It is used only when duration is "real".
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duration: "real" or "predict". If "real", the predicted
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feature length is given by features_lens.
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num_step: the number of steps to use in the ODE solver.
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guidance_scale: the guidance scale for classifier-free guidance.
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"""
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assert duration in ["real", "predict"]
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if duration == "predict":
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(
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text_condition,
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padding_mask,
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) = self.forward_text_inference_ratio_duration(
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tokens=tokens,
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prompt_tokens=prompt_tokens,
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prompt_features_lens=prompt_features_lens,
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speed=speed,
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)
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else:
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assert features_lens is not None
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text_condition, padding_mask = self.forward_text_inference_gt_duration(
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tokens=tokens,
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features_lens=features_lens,
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prompt_tokens=prompt_tokens,
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prompt_features_lens=prompt_features_lens,
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)
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batch_size, num_frames, _ = text_condition.shape
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speech_condition = torch.nn.functional.pad(
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prompt_features, (0, 0, 0, num_frames - prompt_features.size(1))
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) # (B, T, F)
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# False means speech condition positions.
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speech_condition_mask = make_pad_mask(prompt_features_lens, num_frames)
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speech_condition = torch.where(
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speech_condition_mask.unsqueeze(-1),
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torch.zeros_like(speech_condition),
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speech_condition,
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)
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x0 = torch.randn(
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batch_size,
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num_frames,
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prompt_features.size(-1),
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device=text_condition.device,
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)
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x1 = self.solver.sample(
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x=x0,
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text_condition=text_condition,
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speech_condition=speech_condition,
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padding_mask=padding_mask,
|
||||
num_step=num_step,
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||||
guidance_scale=guidance_scale,
|
||||
t_shift=t_shift,
|
||||
)
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x1_wo_prompt_lens = (~padding_mask).sum(-1) - prompt_features_lens
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x1_prompt = torch.zeros(
|
||||
x1.size(0), prompt_features_lens.max(), x1.size(2), device=x1.device
|
||||
)
|
||||
x1_wo_prompt = torch.zeros(
|
||||
x1.size(0), x1_wo_prompt_lens.max(), x1.size(2), device=x1.device
|
||||
)
|
||||
for i in range(x1.size(0)):
|
||||
x1_wo_prompt[i, : x1_wo_prompt_lens[i], :] = x1[
|
||||
i,
|
||||
prompt_features_lens[i] : prompt_features_lens[i]
|
||||
+ x1_wo_prompt_lens[i],
|
||||
]
|
||||
x1_prompt[i, : prompt_features_lens[i], :] = x1[
|
||||
i, : prompt_features_lens[i]
|
||||
]
|
||||
|
||||
return x1_wo_prompt, x1_wo_prompt_lens, x1_prompt, prompt_features_lens
|
||||
|
||||
def sample_intermediate(
|
||||
self,
|
||||
tokens: List[List[int]],
|
||||
features: torch.Tensor,
|
||||
features_lens: torch.Tensor,
|
||||
noise: torch.Tensor,
|
||||
speech_condition_mask: torch.Tensor,
|
||||
t_start: float,
|
||||
t_end: float,
|
||||
num_step: int = 1,
|
||||
guidance_scale: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Generate acoustic features in intermediate timesteps.
|
||||
Args:
|
||||
tokens: 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,
|
||||
with the shape (batch,).
|
||||
noise: The initial noise, with the shape (batch, seq_len, feat_dim).
|
||||
speech_condition_mask: The mask for speech condition, True means
|
||||
non-condition positions, with the shape (batch, seq_len).
|
||||
t_start: The start timestep.
|
||||
t_end: The end timestep.
|
||||
num_step: The number of steps for sampling.
|
||||
guidance_scale: The scale for classifier-free guidance inference,
|
||||
with the shape (batch, 1, 1).
|
||||
"""
|
||||
(text_condition, padding_mask,) = self.forward_text_train(
|
||||
tokens=tokens,
|
||||
features_lens=features_lens,
|
||||
)
|
||||
|
||||
speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
|
||||
|
||||
x_t_end = self.solver.sample(
|
||||
x=noise,
|
||||
text_condition=text_condition,
|
||||
speech_condition=speech_condition,
|
||||
padding_mask=padding_mask,
|
||||
num_step=num_step,
|
||||
guidance_scale=guidance_scale,
|
||||
t_start=t_start,
|
||||
t_end=t_end,
|
||||
)
|
||||
x_t_end_lens = (~padding_mask).sum(-1)
|
||||
return x_t_end, x_t_end_lens
|
||||
Reference in New Issue
Block a user