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diffwave-vocoder-test / pipeline_bddm.py
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#!/bin/env python
# -*- coding: utf-8 -*-
########################################################################
#
# DiffWave: A Versatile Diffusion Model for Audio Synthesis
# (https://arxiv.org/abs/2009.09761)
# Modified from https://github.com/philsyn/DiffWave-Vocoder
#
# Author: Max W. Y. Lam (maxwylam@tencent.com)
# Copyright (c) 2021Tencent. All Rights Reserved
#
########################################################################
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import tqdm
from diffusers.modeling_utils import ModelMixin
from diffusers.configuration_utils import ConfigMixin
from diffusers.pipeline_utils import DiffusionPipeline
def calc_diffusion_step_embedding(diffusion_steps, diffusion_step_embed_dim_in):
"""
Embed a diffusion step $t$ into a higher dimensional space
E.g. the embedding vector in the 128-dimensional space is
[sin(t * 10^(0*4/63)), ... , sin(t * 10^(63*4/63)),
cos(t * 10^(0*4/63)), ... , cos(t * 10^(63*4/63))]
Parameters:
diffusion_steps (torch.long tensor, shape=(batchsize, 1)):
diffusion steps for batch data
diffusion_step_embed_dim_in (int, default=128):
dimensionality of the embedding space for discrete diffusion steps
Returns:
the embedding vectors (torch.tensor, shape=(batchsize, diffusion_step_embed_dim_in)):
"""
assert diffusion_step_embed_dim_in % 2 == 0
half_dim = diffusion_step_embed_dim_in // 2
_embed = np.log(10000) / (half_dim - 1)
_embed = torch.exp(torch.arange(half_dim) * -_embed).cuda()
_embed = diffusion_steps * _embed
diffusion_step_embed = torch.cat((torch.sin(_embed),
torch.cos(_embed)), 1)
return diffusion_step_embed
"""
Below scripts were borrowed from
https://github.com/philsyn/DiffWave-Vocoder/blob/master/WaveNet.py
"""
def swish(x):
return x * torch.sigmoid(x)
# dilated conv layer with kaiming_normal initialization
# from https://github.com/ksw0306/FloWaveNet/blob/master/modules.py
class Conv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1):
super().__init__()
self.padding = dilation * (kernel_size - 1) // 2
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
dilation=dilation, padding=self.padding)
self.conv = nn.utils.weight_norm(self.conv)
nn.init.kaiming_normal_(self.conv.weight)
def forward(self, x):
out = self.conv(x)
return out
# conv1x1 layer with zero initialization
# from https://github.com/ksw0306/FloWaveNet/blob/master/modules.py but the scale parameter is removed
class ZeroConv1d(nn.Module):
def __init__(self, in_channel, out_channel):
super().__init__()
self.conv = nn.Conv1d(in_channel, out_channel, kernel_size=1, padding=0)
self.conv.weight.data.zero_()
self.conv.bias.data.zero_()
def forward(self, x):
out = self.conv(x)
return out
# every residual block (named residual layer in paper)
# contains one noncausal dilated conv
class ResidualBlock(nn.Module):
def __init__(self, res_channels, skip_channels, dilation,
diffusion_step_embed_dim_out):
super().__init__()
self.res_channels = res_channels
# Use a FC layer for diffusion step embedding
self.fc_t = nn.Linear(diffusion_step_embed_dim_out, self.res_channels)
# Dilated conv layer
self.dilated_conv_layer = Conv(self.res_channels, 2 * self.res_channels,
kernel_size=3, dilation=dilation)
# Add mel spectrogram upsampler and conditioner conv1x1 layer
self.upsample_conv2d = nn.ModuleList()
for s in [16, 16]:
conv_trans2d = nn.ConvTranspose2d(1, 1, (3, 2 * s),
padding=(1, s // 2),
stride=(1, s))
conv_trans2d = nn.utils.weight_norm(conv_trans2d)
nn.init.kaiming_normal_(conv_trans2d.weight)
self.upsample_conv2d.append(conv_trans2d)
# 80 is mel bands
self.mel_conv = Conv(80, 2 * self.res_channels, kernel_size=1)
# Residual conv1x1 layer, connect to next residual layer
self.res_conv = nn.Conv1d(res_channels, res_channels, kernel_size=1)
self.res_conv = nn.utils.weight_norm(self.res_conv)
nn.init.kaiming_normal_(self.res_conv.weight)
# Skip conv1x1 layer, add to all skip outputs through skip connections
self.skip_conv = nn.Conv1d(res_channels, skip_channels, kernel_size=1)
self.skip_conv = nn.utils.weight_norm(self.skip_conv)
nn.init.kaiming_normal_(self.skip_conv.weight)
def forward(self, input_data):
x, mel_spec, diffusion_step_embed = input_data
h = x
batch_size, n_channels, seq_len = x.shape
assert n_channels == self.res_channels
# Add in diffusion step embedding
part_t = self.fc_t(diffusion_step_embed)
part_t = part_t.view([batch_size, self.res_channels, 1])
h += part_t
# Dilated conv layer
h = self.dilated_conv_layer(h)
# Upsample spectrogram to size of audio
mel_spec = torch.unsqueeze(mel_spec, dim=1)
mel_spec = F.leaky_relu(self.upsample_conv2d[0](mel_spec), 0.4, inplace=False)
mel_spec = F.leaky_relu(self.upsample_conv2d[1](mel_spec), 0.4, inplace=False)
mel_spec = torch.squeeze(mel_spec, dim=1)
assert mel_spec.size(2) >= seq_len
if mel_spec.size(2) > seq_len:
mel_spec = mel_spec[:, :, :seq_len]
mel_spec = self.mel_conv(mel_spec)
h += mel_spec
# Gated-tanh nonlinearity
out = torch.tanh(h[:, :self.res_channels, :]) * torch.sigmoid(h[:, self.res_channels:, :])
# Residual and skip outputs
res = self.res_conv(out)
assert x.shape == res.shape
skip = self.skip_conv(out)
# Normalize for training stability
return (x + res) * math.sqrt(0.5), skip
class ResidualGroup(nn.Module):
def __init__(self, res_channels, skip_channels, num_res_layers, dilation_cycle,
diffusion_step_embed_dim_in,
diffusion_step_embed_dim_mid,
diffusion_step_embed_dim_out):
super().__init__()
self.num_res_layers = num_res_layers
self.diffusion_step_embed_dim_in = diffusion_step_embed_dim_in
# Use the shared two FC layers for diffusion step embedding
self.fc_t1 = nn.Linear(diffusion_step_embed_dim_in, diffusion_step_embed_dim_mid)
self.fc_t2 = nn.Linear(diffusion_step_embed_dim_mid, diffusion_step_embed_dim_out)
# Stack all residual blocks with dilations 1, 2, ... , 512, ... , 1, 2, ..., 512
self.residual_blocks = nn.ModuleList()
for n in range(self.num_res_layers):
self.residual_blocks.append(
ResidualBlock(res_channels, skip_channels,
dilation=2 ** (n % dilation_cycle),
diffusion_step_embed_dim_out=diffusion_step_embed_dim_out))
def forward(self, input_data):
x, mel_spectrogram, diffusion_steps = input_data
# Embed diffusion step t
diffusion_step_embed = calc_diffusion_step_embedding(
diffusion_steps, self.diffusion_step_embed_dim_in)
diffusion_step_embed = swish(self.fc_t1(diffusion_step_embed))
diffusion_step_embed = swish(self.fc_t2(diffusion_step_embed))
# Pass all residual layers
h = x
skip = 0
for n in range(self.num_res_layers):
# Use the output from last residual layer
h, skip_n = self.residual_blocks[n]((h, mel_spectrogram, diffusion_step_embed))
# Accumulate all skip outputs
skip += skip_n
# Normalize for training stability
return skip * math.sqrt(1.0 / self.num_res_layers)
class DiffWave(ModelMixin, ConfigMixin):
def __init__(
self,
in_channels=1,
res_channels=128,
skip_channels=128,
out_channels=1,
num_res_layers=30,
dilation_cycle=10,
diffusion_step_embed_dim_in=128,
diffusion_step_embed_dim_mid=512,
diffusion_step_embed_dim_out=512,
):
super().__init__()
# register all init arguments with self.register
self.register(
in_channels=in_channels,
res_channels=res_channels,
skip_channels=skip_channels,
out_channels=out_channels,
num_res_layers=num_res_layers,
dilation_cycle=dilation_cycle,
diffusion_step_embed_dim_in=diffusion_step_embed_dim_in,
diffusion_step_embed_dim_mid=diffusion_step_embed_dim_mid,
diffusion_step_embed_dim_out=diffusion_step_embed_dim_out,
)
# Initial conv1x1 with relu
self.init_conv = nn.Sequential(Conv(in_channels, res_channels, kernel_size=1), nn.ReLU(inplace=False))
# All residual layers
self.residual_layer = ResidualGroup(res_channels,
skip_channels,
num_res_layers,
dilation_cycle,
diffusion_step_embed_dim_in,
diffusion_step_embed_dim_mid,
diffusion_step_embed_dim_out)
# Final conv1x1 -> relu -> zeroconv1x1
self.final_conv = nn.Sequential(Conv(skip_channels, skip_channels, kernel_size=1),
nn.ReLU(inplace=False), ZeroConv1d(skip_channels, out_channels))
def forward(self, input_data):
audio, mel_spectrogram, diffusion_steps = input_data
x = audio
x = self.init_conv(x).clone()
x = self.residual_layer((x, mel_spectrogram, diffusion_steps))
return self.final_conv(x)
class BDDM(DiffusionPipeline):
def __init__(self, diffwave, noise_scheduler):
super().__init__()
noise_scheduler = noise_scheduler.set_format("pt")
self.register_modules(diffwave=diffwave, noise_scheduler=noise_scheduler)
@torch.no_grad()
def __call__(self, mel_spectrogram, generator, torch_device=None):
if torch_device is None:
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
self.diffwave.to(torch_device)
mel_spectrogram = mel_spectrogram.to(torch_device)
audio_length = mel_spectrogram.size(-1) * 256
audio_size = (1, 1, audio_length)
# Sample gaussian noise to begin loop
audio = torch.normal(0, 1, size=audio_size, generator=generator).to(torch_device)
timestep_values = self.noise_scheduler.timestep_values
num_prediction_steps = len(self.noise_scheduler)
for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
# 1. predict noise residual
ts = (torch.tensor(timestep_values[t]) * torch.ones((1, 1))).to(torch_device)
residual = self.diffwave((audio, mel_spectrogram, ts))
# 2. predict previous mean of audio x_t-1
pred_prev_audio = self.noise_scheduler.step(residual, audio, t)
# 3. optionally sample variance
variance = 0
if t > 0:
noise = torch.normal(0, 1, size=audio_size, generator=generator).to(torch_device)
variance = self.noise_scheduler.get_variance(t).sqrt() * noise
# 4. set current audio to prev_audio: x_t -> x_t-1
audio = pred_prev_audio + variance
return audio