Initial commit
This commit is contained in:
Yatharth Sharma
1590
zipvoice/models/modules/scaling.py
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1590
zipvoice/models/modules/scaling.py
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File diff suppressed because it is too large
Load Diff
277
zipvoice/models/modules/solver.py
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277
zipvoice/models/modules/solver.py
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#!/usr/bin/env python3
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# Copyright 2024 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 Optional, Union
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import torch
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class DiffusionModel(torch.nn.Module):
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"""A wrapper of diffusion models for inference.
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Args:
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model: The diffusion model.
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func_name: The function name to call.
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"""
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def __init__(
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self,
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model: torch.nn.Module,
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func_name: str = "forward_fm_decoder",
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):
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super().__init__()
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self.model = model
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self.func_name = func_name
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self.model_func = getattr(self.model, func_name)
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def forward(
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self,
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t: torch.Tensor,
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x: 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: Union[float, torch.Tensor] = 0.0,
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**kwargs
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) -> torch.Tensor:
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"""
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Forward function that Handles the classifier-free guidance.
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Args:
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t: The current timestep, a tensor of a tensor of a single float.
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x: The initial value, with the shape (batch, seq_len, emb_dim).
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text_condition: The text_condition of the diffision model, with
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the shape (batch, seq_len, emb_dim).
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speech_condition: The speech_condition of the diffision model, 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 position, with the
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shape (batch, seq_len).
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guidance_scale: The scale of classifier-free guidance, a float or a tensor
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of shape (batch, 1, 1).
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Retrun:
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The prediction with the shape (batch, seq_len, emb_dim).
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"""
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if not torch.is_tensor(guidance_scale):
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guidance_scale = torch.tensor(
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guidance_scale, dtype=t.dtype, device=t.device
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)
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if (guidance_scale == 0.0).all():
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return self.model_func(
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t=t,
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xt=x,
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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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**kwargs
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)
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else:
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assert t.dim() == 0
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x = torch.cat([x] * 2, dim=0)
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padding_mask = torch.cat([padding_mask] * 2, dim=0)
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text_condition = torch.cat(
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[torch.zeros_like(text_condition), text_condition], dim=0
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)
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if t > 0.5:
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speech_condition = torch.cat(
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[torch.zeros_like(speech_condition), speech_condition], dim=0
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)
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else:
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guidance_scale = guidance_scale * 2
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speech_condition = torch.cat(
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[speech_condition, speech_condition], dim=0
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)
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data_uncond, data_cond = self.model_func(
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t=t,
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xt=x,
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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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**kwargs
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).chunk(2, dim=0)
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res = (1 + guidance_scale) * data_cond - guidance_scale * data_uncond
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return res
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class DistillDiffusionModel(DiffusionModel):
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"""A wrapper of distilled diffusion models for inference.
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Args:
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model: The distilled diffusion model.
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func_name: The function name to call.
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"""
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def __init__(
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self,
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model: torch.nn.Module,
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func_name: str = "forward_fm_decoder",
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):
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super().__init__(model=model, func_name=func_name)
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def forward(
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self,
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t: torch.Tensor,
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x: 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: Union[float, torch.Tensor] = 0.0,
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**kwargs
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) -> torch.Tensor:
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"""
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Forward function that Handles the classifier-free guidance.
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Args:
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t: The current timestep, a tensor of a single float.
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x: The initial value, with the shape (batch, seq_len, emb_dim).
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text_condition: The text_condition of the diffision model, with
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the shape (batch, seq_len, emb_dim).
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speech_condition: The speech_condition of the diffision model, 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 position, with the
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shape (batch, seq_len).
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guidance_scale: The scale of classifier-free guidance, a float or a tensor
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of shape (batch, 1, 1).
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Retrun:
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The prediction with the shape (batch, seq_len, emb_dim).
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"""
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if not torch.is_tensor(guidance_scale):
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guidance_scale = torch.tensor(
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guidance_scale, dtype=t.dtype, device=t.device
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)
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return self.model_func(
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t=t,
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xt=x,
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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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guidance_scale=guidance_scale,
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**kwargs
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)
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class EulerSolver:
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def __init__(
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self,
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model: torch.nn.Module,
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func_name: str = "forward_fm_decoder",
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):
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"""Construct a Euler Solver
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Args:
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model: The diffusion model.
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func_name: The function name to call.
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"""
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self.model = DiffusionModel(model, func_name=func_name)
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def sample(
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self,
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x: torch.Tensor,
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text_condition: torch.Tensor,
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speech_condition: torch.Tensor,
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padding_mask: torch.Tensor,
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num_step: int = 10,
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guidance_scale: Union[float, torch.Tensor] = 0.0,
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t_start: float = 0.0,
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t_end: float = 1.0,
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t_shift: float = 1.0,
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**kwargs
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) -> torch.Tensor:
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device = x.device
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assert isinstance(t_start, float) and isinstance(t_end, float)
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# Generate the schedule of timesteps
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timesteps = get_time_steps(
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t_start=t_start,
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t_end=t_end,
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num_step=num_step,
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t_shift=t_shift,
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device=device,
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)
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for step in range(num_step):
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t_cur = timesteps[step]
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t_next = timesteps[step + 1]
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# Predict velocity (v)
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v = self.model(
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t=t_cur,
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x=x,
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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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guidance_scale=guidance_scale,
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**kwargs
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)
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# 1. Predict the clean 'data' (x_1) and 'noise' (x_0)
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# Flow matching formulation: x_t = (1 - t) * x_0 + t * x_1
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# Therefore: v = x_1 - x_0
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x_1_pred = x + (1.0 - t_cur) * v
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x_0_pred = x - t_cur * v
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if step < num_step - 1:
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# 2. Probability Flow ODE update (Anchor-based)
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# This 'anchors' the next point along the predicted line,
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# making it more robust than simple Euler integration.
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x = (1.0 - t_next) * x_0_pred + t_next * x_1_pred
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else:
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# Final step: Snap directly to the predicted clean data
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x = x_1_pred
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return x
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class DistillEulerSolver(EulerSolver):
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def __init__(
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self,
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model: torch.nn.Module,
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func_name: str = "forward_fm_decoder",
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):
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"""Construct a Euler Solver for distilled diffusion models.
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Args:
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model: The diffusion model.
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"""
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self.model = DistillDiffusionModel(model, func_name=func_name)
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def get_time_steps(
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t_start: float = 0.0,
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t_end: float = 1.0,
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num_step: int = 10,
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t_shift: float = 1.0,
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device: torch.device = torch.device("cpu"),
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) -> torch.Tensor:
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"""Compute the intermediate time steps for sampling.
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Args:
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t_start: The starting time of the sampling (default is 0).
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t_end: The starting time of the sampling (default is 1).
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num_step: The number of sampling.
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t_shift: shift the t toward smaller numbers so that the sampling
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will emphasize low SNR region. Should be in the range of (0, 1].
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The shifting will be more significant when the number is smaller.
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device: A torch device.
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Returns:
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The time step with the shape (num_step + 1,).
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"""
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timesteps = torch.linspace(t_start, t_end, num_step + 1).to(device)
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timesteps = t_shift * timesteps / (1 + (t_shift - 1) * timesteps)
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return timesteps
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1680
zipvoice/models/modules/zipformer.py
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1680
zipvoice/models/modules/zipformer.py
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File diff suppressed because it is too large
Load Diff
264
zipvoice/models/modules/zipformer_two_stream.py
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264
zipvoice/models/modules/zipformer_two_stream.py
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@@ -0,0 +1,264 @@
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#!/usr/bin/env python3
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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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# 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
|
||||
# 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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import math
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from typing import Optional, Tuple, Union
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import torch
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from torch import Tensor, nn
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from zipvoice.models.modules.scaling import FloatLike, ScheduledFloat, SwooshR
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from zipvoice.models.modules.zipformer import (
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DownsampledZipformer2Encoder,
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TTSZipformer,
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Zipformer2Encoder,
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Zipformer2EncoderLayer,
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)
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def timestep_embedding(timesteps, dim, max_period=10000):
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"""Create sinusoidal timestep embeddings.
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:param timesteps: shape of (N) or (N, T)
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an Tensor of positional embeddings. shape of (N, dim) or (T, N, dim)
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"""
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period)
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* torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device)
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/ half
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)
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if timesteps.dim() == 2:
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timesteps = timesteps.transpose(0, 1) # (N, T) -> (T, N)
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args = timesteps[..., None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat([embedding, torch.zeros_like(embedding[..., :1])], dim=-1)
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return embedding
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class TTSZipformerTwoStream(TTSZipformer):
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"""
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Args:
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Note: all "int or Tuple[int]" arguments below will be treated as lists of the same
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length as downsampling_factor if they are single ints or one-element tuples.
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The length of downsampling_factor defines the number of stacks.
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downsampling_factor (Tuple[int]): downsampling factor for each encoder stack.
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Note: this is in addition to the downsampling factor of 2 that is applied in
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the frontend (self.encoder_embed).
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encoder_dim (Tuple[int]): embedding dimension of each of the encoder stacks,
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one per encoder stack.
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num_encoder_layers (int or Tuple[int])): number of encoder layers for each stack
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query_head_dim (int or Tuple[int]): dimension of query and key per attention
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head: per stack, if a tuple..
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pos_head_dim (int or Tuple[int]): dimension of positional-encoding projection
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per attention head
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value_head_dim (int or Tuple[int]): dimension of value in each attention head
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num_heads: (int or Tuple[int]): number of heads in the self-attention mechanism.
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Must be at least 4.
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feedforward_dim (int or Tuple[int]): hidden dimension in feedforward modules
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cnn_module_kernel (int or Tuple[int])): Kernel size of convolution module
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pos_dim (int): the dimension of each positional-encoding vector prior to
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projection, e.g. 128.
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dropout (float): dropout rate
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warmup_batches (float): number of batches to warm up over; this controls
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dropout of encoder layers.
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use_time_embed: (bool): if True, do not take time embedding as additional input.
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time_embed_dim: (int): the dimension of the time embedding.
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"""
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def __init__(
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self,
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in_dim: Tuple[int],
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out_dim: Tuple[int],
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downsampling_factor: Tuple[int] = (2, 4),
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num_encoder_layers: Union[int, Tuple[int]] = 4,
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cnn_module_kernel: Union[int, Tuple[int]] = 31,
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encoder_dim: int = 384,
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query_head_dim: int = 24,
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pos_head_dim: int = 4,
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value_head_dim: int = 12,
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num_heads: int = 8,
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feedforward_dim: int = 1536,
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pos_dim: int = 192,
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dropout: FloatLike = None, # see code below for default
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warmup_batches: float = 4000.0,
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use_time_embed: bool = True,
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time_embed_dim: int = 192,
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use_conv: bool = True,
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) -> None:
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nn.Module.__init__(self)
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if dropout is None:
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dropout = ScheduledFloat((0.0, 0.3), (20000.0, 0.1))
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if isinstance(downsampling_factor, int):
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downsampling_factor = (downsampling_factor,)
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def _to_tuple(x):
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"""Converts a single int or a 1-tuple of an int to a tuple with the same
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length as downsampling_factor"""
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if isinstance(x, int):
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x = (x,)
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if len(x) == 1:
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x = x * len(downsampling_factor)
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else:
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assert len(x) == len(downsampling_factor) and isinstance(x[0], int)
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return x
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def _assert_downsampling_factor(factors):
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"""assert downsampling_factor follows u-net style"""
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assert factors[0] == 1 and factors[-1] == 1
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for i in range(1, len(factors) // 2 + 1):
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assert factors[i] == factors[i - 1] * 2
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for i in range(len(factors) // 2 + 1, len(factors)):
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assert factors[i] * 2 == factors[i - 1]
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_assert_downsampling_factor(downsampling_factor)
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self.downsampling_factor = downsampling_factor # tuple
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num_encoder_layers = _to_tuple(num_encoder_layers)
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self.cnn_module_kernel = cnn_module_kernel = _to_tuple(cnn_module_kernel)
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self.encoder_dim = encoder_dim
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self.num_encoder_layers = num_encoder_layers
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self.query_head_dim = query_head_dim
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self.value_head_dim = value_head_dim
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self.num_heads = num_heads
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self.use_time_embed = use_time_embed
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self.time_embed_dim = time_embed_dim
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if self.use_time_embed:
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assert time_embed_dim != -1
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else:
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time_embed_dim = -1
|
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||||
assert len(in_dim) == len(out_dim) == 2
|
||||
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self.in_dim = in_dim
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self.in_proj = nn.ModuleList(
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[nn.Linear(in_dim[0], encoder_dim), nn.Linear(in_dim[1], encoder_dim)]
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)
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self.out_dim = out_dim
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self.out_proj = nn.ModuleList(
|
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[nn.Linear(encoder_dim, out_dim[0]), nn.Linear(encoder_dim, out_dim[1])]
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||||
)
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||||
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# each one will be Zipformer2Encoder or DownsampledZipformer2Encoder
|
||||
encoders = []
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||||
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||||
num_encoders = len(downsampling_factor)
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||||
for i in range(num_encoders):
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||||
encoder_layer = Zipformer2EncoderLayer(
|
||||
embed_dim=encoder_dim,
|
||||
pos_dim=pos_dim,
|
||||
num_heads=num_heads,
|
||||
query_head_dim=query_head_dim,
|
||||
pos_head_dim=pos_head_dim,
|
||||
value_head_dim=value_head_dim,
|
||||
feedforward_dim=feedforward_dim,
|
||||
use_conv=use_conv,
|
||||
cnn_module_kernel=cnn_module_kernel[i],
|
||||
dropout=dropout,
|
||||
)
|
||||
|
||||
# For the segment of the warmup period, we let the Conv2dSubsampling
|
||||
# layer learn something. Then we start to warm up the other encoders.
|
||||
encoder = Zipformer2Encoder(
|
||||
encoder_layer,
|
||||
num_encoder_layers[i],
|
||||
embed_dim=encoder_dim,
|
||||
time_embed_dim=time_embed_dim,
|
||||
pos_dim=pos_dim,
|
||||
warmup_begin=warmup_batches * (i + 1) / (num_encoders + 1),
|
||||
warmup_end=warmup_batches * (i + 2) / (num_encoders + 1),
|
||||
final_layerdrop_rate=0.035 * (downsampling_factor[i] ** 0.5),
|
||||
)
|
||||
|
||||
if downsampling_factor[i] != 1:
|
||||
encoder = DownsampledZipformer2Encoder(
|
||||
encoder,
|
||||
dim=encoder_dim,
|
||||
downsample=downsampling_factor[i],
|
||||
)
|
||||
|
||||
encoders.append(encoder)
|
||||
|
||||
self.encoders = nn.ModuleList(encoders)
|
||||
if self.use_time_embed:
|
||||
self.time_embed = nn.Sequential(
|
||||
nn.Linear(time_embed_dim, time_embed_dim * 2),
|
||||
SwooshR(),
|
||||
nn.Linear(time_embed_dim * 2, time_embed_dim),
|
||||
)
|
||||
else:
|
||||
self.time_embed = None
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
t: Optional[Tensor] = None,
|
||||
padding_mask: Optional[Tensor] = None,
|
||||
) -> Tuple[Tensor, Tensor]:
|
||||
"""
|
||||
Args:
|
||||
x:
|
||||
The input tensor. Its shape is (batch_size, seq_len, feature_dim).
|
||||
t:
|
||||
A t tensor of shape (batch_size,) or (batch_size, seq_len)
|
||||
padding_mask:
|
||||
The mask for padding, of shape (batch_size, seq_len); True means
|
||||
masked position. May be None.
|
||||
Returns:
|
||||
Return the output embeddings. its shape is
|
||||
(batch_size, output_seq_len, encoder_dim)
|
||||
"""
|
||||
assert x.size(2) in self.in_dim, f"{x.size(2)} in {self.in_dim}"
|
||||
if x.size(2) == self.in_dim[0]:
|
||||
index = 0
|
||||
else:
|
||||
index = 1
|
||||
x = x.permute(1, 0, 2)
|
||||
x = self.in_proj[index](x)
|
||||
|
||||
if t is not None:
|
||||
assert t.dim() == 1 or t.dim() == 2, t.shape
|
||||
time_emb = timestep_embedding(t, self.time_embed_dim)
|
||||
time_emb = self.time_embed(time_emb)
|
||||
else:
|
||||
time_emb = None
|
||||
|
||||
attn_mask = None
|
||||
|
||||
for i, module in enumerate(self.encoders):
|
||||
x = module(
|
||||
x,
|
||||
time_emb=time_emb,
|
||||
src_key_padding_mask=padding_mask,
|
||||
attn_mask=attn_mask,
|
||||
)
|
||||
x = self.out_proj[index](x)
|
||||
x = x.permute(1, 0, 2)
|
||||
return x
|
||||
Reference in New Issue
Block a user