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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import threading
import torch
import torch.nn.functional as F
from .matcha.flow_matching import BASECFM
from .configs import CFM_PARAMS
from tqdm import tqdm
def cast_all(*args, dtype):
return [a if (not a.dtype.is_floating_point) or a.dtype == dtype else a.to(dtype) for a in args]
class ConditionalCFM(BASECFM):
def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
super().__init__(
n_feats=in_channels,
cfm_params=cfm_params,
n_spks=n_spks,
spk_emb_dim=spk_emb_dim,
)
self.t_scheduler = cfm_params.t_scheduler
self.training_cfg_rate = cfm_params.training_cfg_rate
self.inference_cfg_rate = cfm_params.inference_cfg_rate
in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
# Just change the architecture of the estimator here
self.estimator = estimator
@torch.inference_mode()
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, flow_cache=torch.zeros(1, 80, 0, 2)):
"""Forward diffusion
Args:
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, mel_timesteps)
n_timesteps (int): number of diffusion steps
temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
spks (torch.Tensor, optional): speaker ids. Defaults to None.
shape: (batch_size, spk_emb_dim)
cond: Not used but kept for future purposes
Returns:
sample: generated mel-spectrogram
shape: (batch_size, n_feats, mel_timesteps)
"""
raise NotImplementedError("unused, needs updating for meanflow model")
z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature
cache_size = flow_cache.shape[2]
# fix prompt and overlap part mu and z
if cache_size != 0:
z[:, :, :cache_size] = flow_cache[:, :, :, 0]
mu[:, :, :cache_size] = flow_cache[:, :, :, 1]
z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2)
mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2)
flow_cache = torch.stack([z_cache, mu_cache], dim=-1)
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
if self.t_scheduler == 'cosine':
t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), flow_cache
def solve_euler(self, x, t_span, mu, mask, spks, cond, meanflow=False):
"""
Fixed euler solver for ODEs.
Args:
x (torch.Tensor): random noise
t_span (torch.Tensor): n_timesteps interpolated
shape: (n_timesteps + 1,)
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, mel_timesteps)
spks (torch.Tensor, optional): speaker ids. Defaults to None.
shape: (batch_size, spk_emb_dim)
cond: Not used but kept for future purposes
meanflow: meanflow mode
"""
in_dtype = x.dtype
x, t_span, mu, mask, spks, cond = cast_all(x, t_span, mu, mask, spks, cond, dtype=self.estimator.dtype)
# Duplicated batch dims are for CFG
# Do not use concat, it may cause memory format changed and trt infer with wrong results!
B, T = mu.size(0), x.size(2)
x_in = torch.zeros([2 * B, 80, T], device=x.device, dtype=x.dtype)
mask_in = torch.zeros([2 * B, 1, T], device=x.device, dtype=x.dtype)
mu_in = torch.zeros([2 * B, 80, T], device=x.device, dtype=x.dtype)
t_in = torch.zeros([2 * B ], device=x.device, dtype=x.dtype)
spks_in = torch.zeros([2 * B, 80 ], device=x.device, dtype=x.dtype)
cond_in = torch.zeros([2 * B, 80, T], device=x.device, dtype=x.dtype)
r_in = torch.zeros([2 * B ], device=x.device, dtype=x.dtype) # (only used for meanflow)
for t, r in zip(t_span[:-1], t_span[1:]):
t = t.unsqueeze(dim=0)
r = r.unsqueeze(dim=0)
# Shapes:
# x_in ( 2B, 80, T )
# mask_in ( 2B, 1, T )
# mu_in ( 2B, 80, T )
# t_in ( 2B, )
# spks_in ( 2B, 80, )
# cond_in ( 2B, 80, T )
# r_in ( 2B, )
# x ( B, 80, T )
# mask ( B, 1, T )
# mu ( B, 80, T )
# t ( B, )
# spks ( B, 80, )
# cond ( B, 80, T )
# r ( B, )
x_in[:B] = x_in[B:] = x
mask_in[:B] = mask_in[B:] = mask
mu_in[:B] = mu
t_in[:B] = t_in[B:] = t
spks_in[:B] = spks
cond_in[:B] = cond
r_in[:B] = r_in[B:] = r # (only used for meanflow)
dxdt = self.estimator.forward(
x=x_in, mask=mask_in, mu=mu_in, t=t_in, spks=spks_in, cond=cond_in,
r=r_in if meanflow else None,
)
dxdt, cfg_dxdt = torch.split(dxdt, [B, B], dim=0)
dxdt = ((1.0 + self.inference_cfg_rate) * dxdt - self.inference_cfg_rate * cfg_dxdt)
dt = r - t
x = x + dt * dxdt
return x.to(in_dtype)
def compute_loss(self, x1, mask, mu, spks=None, cond=None):
"""Computes diffusion loss
Args:
x1 (torch.Tensor): Target
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): target mask
shape: (batch_size, 1, mel_timesteps)
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
spks (torch.Tensor, optional): speaker embedding. Defaults to None.
shape: (batch_size, spk_emb_dim)
Returns:
loss: conditional flow matching loss
y: conditional flow
shape: (batch_size, n_feats, mel_timesteps)
"""
b, _, t = mu.shape
# random timestep
t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
if self.t_scheduler == 'cosine':
t = 1 - torch.cos(t * 0.5 * torch.pi)
# sample noise p(x_0)
z = torch.randn_like(x1)
y = (1 - (1 - self.sigma_min) * t) * z + t * x1
u = x1 - (1 - self.sigma_min) * z
# during training, we randomly drop condition to trade off mode coverage and sample fidelity
if self.training_cfg_rate > 0:
cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate
mu = mu * cfg_mask.view(-1, 1, 1)
spks = spks * cfg_mask.view(-1, 1)
cond = cond * cfg_mask.view(-1, 1, 1)
pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond)
loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
return loss, y
class CausalConditionalCFM(ConditionalCFM):
def __init__(self, in_channels=240, cfm_params=CFM_PARAMS, n_spks=1, spk_emb_dim=80, estimator=None):
super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator)
# TODO: BAD BAD IDEA - IT'LL MESS UP DISTILLATION - SETTING TO NONE
self.rand_noise = None
@torch.inference_mode()
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, noised_mels=None, meanflow=False):
"""Forward diffusion
Args:
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, mel_timesteps)
n_timesteps (int): number of diffusion steps
temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
spks (torch.Tensor, optional): speaker ids. Defaults to None.
shape: (batch_size, spk_emb_dim)
cond: Not used but kept for future purposes
noised_mels: gt mels noised a time t
Returns:
sample: generated mel-spectrogram
shape: (batch_size, n_feats, mel_timesteps)
"""
B = mu.size(0)
z = torch.randn_like(mu)
if noised_mels is not None:
prompt_len = mu.size(2) - noised_mels.size(2)
z[..., prompt_len:] = noised_mels
# time steps for reverse diffusion
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
if (not meanflow) and (self.t_scheduler == 'cosine'):
t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
# NOTE: right now, the only meanflow models are also distilled models, which don't need CFG
# because they were distilled with CFG outputs. We would need to add another hparam and
# change the conditional logic here if we want to use CFG inference with a meanflow model.
if meanflow:
return self.basic_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), None
return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond, meanflow=meanflow), None
def basic_euler(self, x, t_span, mu, mask, spks, cond):
in_dtype = x.dtype
x, t_span, mu, mask, spks, cond = cast_all(x, t_span, mu, mask, spks, cond, dtype=self.estimator.dtype)
print("S3 Token -> Mel Inference...")
for t, r in tqdm(zip(t_span[..., :-1], t_span[..., 1:]), total=t_span.shape[-1] - 1):
t, r = t[None], r[None]
dxdt = self.estimator.forward(x, mask=mask, mu=mu, t=t, spks=spks, cond=cond, r=r)
dt = r - t
x = x + dt * dxdt
return x.to(in_dtype)
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