Create pipeline_bddm.py

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 ([email protected])
+#  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 ..modeling_utils import ModelMixin
+from ..configuration_utils import ConfigMixin
+from ..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