Init

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 NVIDIA CORPORATION.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software. 
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,4 +1,3 @@
----
 title: Nvidia Denoiser
 emoji: 🔥
 colorFrom: blue
@@ -7,7 +6,4 @@ sdk: gradio
 sdk_version: 3.23.0
 app_file: app.py
 pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+license: apache-2.0

app.py

@@ -0,0 +1,97 @@
+import os
+import json
+from tqdm import tqdm
+from copy import deepcopy
+
+import numpy as np
+import gradio as gr
+import torch
+
+import random
+random.seed(0)
+torch.manual_seed(0)
+np.random.seed(0)
+
+from scipy.io.wavfile import write as wavwrite
+
+from util import print_size, sampling
+from network import CleanUNet
+import torchaudio
+
+def load_simple(filename):
+    audio, _ = torchaudio.load(filename)
+    return audio
+
+CONFIG = "configs/DNS-large-full.json"
+CHECKPOINT = "./exp/DNS-large-high/checkpoint/pretrained.pkl"
+
+# Parse configs. Globals nicer in this case
+with open(CONFIG) as f:
+    data = f.read()
+    config = json.loads(data)
+    gen_config              = config["gen_config"]
+    global network_config
+    network_config          = config["network_config"]      # to define wavenet
+    global train_config
+    train_config            = config["train_config"]        # train config
+    global trainset_config
+    trainset_config         = config["trainset_config"]     # to read trainset configurations
+
+def denoise(files, ckpt_path):
+    """
+    Denoise audio
+    Parameters:
+    output_directory (str):         save generated speeches to this path
+    ckpt_iter (int or 'max'):       the pretrained checkpoint to be loaded; 
+                                    automitically selects the maximum iteration if 'max' is selected
+    subset (str):                   training, testing, validation
+    dump (bool):                    whether save enhanced (denoised) audio
+    """
+
+    # setup local experiment path
+    exp_path = train_config["exp_path"]
+    print('exp_path:', exp_path)
+
+    # load data
+    loader_config = deepcopy(trainset_config)
+    loader_config["crop_length_sec"] = 0
+
+    # predefine model
+    net = CleanUNet(**network_config)
+    print_size(net)
+
+    # load checkpoint
+    checkpoint = torch.load(ckpt_path, map_location='cpu')
+    net.load_state_dict(checkpoint['model_state_dict'])
+    net.eval()
+
+    # inference
+    batch_size = 1000000
+    for file_path in tqdm(files):
+        file_name = os.path.basename(file_path)
+        file_dir = os.path.dirname(file_name)
+        new_file_name = file_name + "_denoised.wav"
+        noisy_audio = load_simple(file_path)
+        LENGTH = len(noisy_audio[0].squeeze())
+        noisy_audio = torch.chunk(noisy_audio, LENGTH // batch_size + 1, dim=1)
+        all_audio = []
+
+        for batch in tqdm(noisy_audio):
+            with torch.no_grad():
+                generated_audio = sampling(net, batch)
+                generated_audio = generated_audio.cpu().numpy().squeeze()
+                all_audio.append(generated_audio)
+
+        all_audio = np.concatenate(all_audio, axis=0)
+        save_file = os.path.join(file_dir, new_file_name)
+        print("saved to:", save_file)
+        wavwrite(save_file, 32000, all_audio.squeeze())
+
+
+audio = gr.inputs.Audio(label = "Audio to denoise", type = 'filepath')
+inputs = [audio, CHECKPOINT]
+outputs = gr.outputs.Audio(label = "Denoised audio", type = 'filepath')
+
+title = "Speech Denoising in the Waveform Domain with Self-Attention from Nvidia"
+
+gr.Interface(denoise, inputs, outputs, title=title, enable_queue=True).launch()

configs/DNS-large-full.json

@@ -0,0 +1,54 @@
+{   
+    "network_config": {
+        "channels_input": 1,
+        "channels_output": 1,
+        "channels_H": 64,
+        "max_H": 768,
+        "encoder_n_layers": 8,
+        "kernel_size": 4,
+        "stride": 2,
+        "tsfm_n_layers": 5, 
+        "tsfm_n_head": 8,
+        "tsfm_d_model": 512, 
+        "tsfm_d_inner": 2048
+    },
+    "train_config": {
+        "exp_path": "DNS-large-full",
+        "log":{
+            "directory": "./exp",
+            "ckpt_iter": "max",
+            "iters_per_ckpt": 10000,
+            "iters_per_valid": 500
+        },
+        "optimization":{
+            "n_iters": 250000,
+            "learning_rate": 2e-4,
+            "batch_size_per_gpu": 8
+        },
+        "loss_config":{
+            "ell_p": 1,
+            "ell_p_lambda": 1,
+            "stft_lambda": 1,
+            "stft_config":{
+                "sc_lambda": 0.5,
+                "mag_lambda": 0.5,
+                "band": "full",
+                "hop_sizes": [50, 120, 240],
+                "win_lengths": [240, 600, 1200],
+                "fft_sizes": [512, 1024, 2048]
+            }
+        }
+    },
+    "trainset_config": {
+        "root": "./dns",
+        "crop_length_sec": 10,
+        "sample_rate": 16000
+    },
+    "gen_config":{
+        "output_directory": "./exp"
+    },
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321"
+    }
+}

configs/DNS-large-high.json

@@ -0,0 +1,54 @@
+{   
+    "network_config": {
+        "channels_input": 1,
+        "channels_output": 1,
+        "channels_H": 64,
+        "max_H": 768,
+        "encoder_n_layers": 8,
+        "kernel_size": 4,
+        "stride": 2,
+        "tsfm_n_layers": 5, 
+        "tsfm_n_head": 8,
+        "tsfm_d_model": 512, 
+        "tsfm_d_inner": 2048
+    },
+    "train_config": {
+        "exp_path": "DNS-large-high",
+        "log":{
+            "directory": "./exp",
+            "ckpt_iter": "max",
+            "iters_per_ckpt": 10000,
+            "iters_per_valid": 500
+        },
+        "optimization":{
+            "n_iters": 250000,
+            "learning_rate": 2e-4,
+            "batch_size_per_gpu": 8
+        },
+        "loss_config":{
+            "ell_p": 1,
+            "ell_p_lambda": 1,
+            "stft_lambda": 1,
+            "stft_config":{
+                "sc_lambda": 0.5,
+                "mag_lambda": 0.5,
+                "band": "high",
+                "hop_sizes": [50, 120, 240],
+                "win_lengths": [240, 600, 1200],
+                "fft_sizes": [512, 1024, 2048]
+            }
+        }
+    },
+    "trainset_config": {
+        "root": "./dns",
+        "crop_length_sec": 10,
+        "sample_rate": 16000
+    },
+    "gen_config":{
+        "output_directory": "./exp"
+    },
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321"
+    }
+}

dataset.py

@@ -0,0 +1,120 @@
+# Copyright (c) 2022 NVIDIA CORPORATION. 
+#   Licensed under the MIT license.
+
+import os
+import numpy as np
+
+from scipy.io.wavfile import read as wavread
+import warnings
+warnings.filterwarnings("ignore")
+
+import torch
+from torch.utils.data import Dataset
+from torch.utils.data.distributed import DistributedSampler
+
+import random
+random.seed(0)
+torch.manual_seed(0)
+np.random.seed(0)
+
+from torchvision import datasets, models, transforms
+import torchaudio
+
+
+class CleanNoisyPairDataset(Dataset):
+    """
+    Create a Dataset of clean and noisy audio pairs. 
+    Each element is a tuple of the form (clean waveform, noisy waveform, file_id)
+    """
+    
+    def __init__(self, root='./', subset='training', crop_length_sec=0):
+        super(CleanNoisyPairDataset).__init__()
+
+        assert subset is None or subset in ["training", "testing"]
+        self.crop_length_sec = crop_length_sec
+        self.subset = subset
+
+        N_clean = len(os.listdir(os.path.join(root, 'training_set/clean')))
+        N_noisy = len(os.listdir(os.path.join(root, 'training_set/noisy')))
+        assert N_clean == N_noisy
+
+        if subset == "training":
+            self.files = [(os.path.join(root, 'training_set/clean', 'fileid_{}.wav'.format(i)),
+                           os.path.join(root, 'training_set/noisy', 'fileid_{}.wav'.format(i))) for i in range(N_clean)]
+        
+        elif subset == "testing":
+            sortkey = lambda name: '_'.join(name.split('_')[-2:])  # specific for dns due to test sample names
+            _p = os.path.join(root, 'datasets/test_set/synthetic/no_reverb')  # path for DNS
+            
+            clean_files = os.listdir(os.path.join(_p, 'clean'))
+            noisy_files = os.listdir(os.path.join(_p, 'noisy'))
+            
+            clean_files.sort(key=sortkey)
+            noisy_files.sort(key=sortkey)
+
+            self.files = []
+            for _c, _n in zip(clean_files, noisy_files):
+                assert sortkey(_c) == sortkey(_n)
+                self.files.append((os.path.join(_p, 'clean', _c), 
+                                   os.path.join(_p, 'noisy', _n)))
+            self.crop_length_sec = 0
+
+        else:
+            raise NotImplementedError
+
+    def __getitem__(self, n):
+        fileid = self.files[n]
+        clean_audio, sample_rate = torchaudio.load(fileid[0])
+        noisy_audio, sample_rate = torchaudio.load(fileid[1])
+        clean_audio, noisy_audio = clean_audio.squeeze(0), noisy_audio.squeeze(0)
+        assert len(clean_audio) == len(noisy_audio)
+
+        crop_length = int(self.crop_length_sec * sample_rate)
+        assert crop_length < len(clean_audio)
+
+        # random crop
+        if self.subset != 'testing' and crop_length > 0:
+            start = np.random.randint(low=0, high=len(clean_audio) - crop_length + 1)
+            clean_audio = clean_audio[start:(start + crop_length)]
+            noisy_audio = noisy_audio[start:(start + crop_length)]
+        
+        clean_audio, noisy_audio = clean_audio.unsqueeze(0), noisy_audio.unsqueeze(0)
+        return (clean_audio, noisy_audio, fileid)
+
+    def __len__(self):
+        return len(self.files)
+
+
+def load_CleanNoisyPairDataset(root, subset, crop_length_sec, batch_size, sample_rate, num_gpus=1):
+    """
+    Get dataloader with distributed sampling
+    """
+    dataset = CleanNoisyPairDataset(root=root, subset=subset, crop_length_sec=crop_length_sec)                                                       
+    kwargs = {"batch_size": batch_size, "num_workers": 4, "pin_memory": False, "drop_last": False}
+
+    if num_gpus > 1:
+        train_sampler = DistributedSampler(dataset)
+        dataloader = torch.utils.data.DataLoader(dataset, sampler=train_sampler, **kwargs)
+    else:
+        dataloader = torch.utils.data.DataLoader(dataset, sampler=None, shuffle=True, **kwargs)
+        
+    return dataloader
+
+
+if __name__ == '__main__':
+    import json
+    with open('./configs/DNS-large-full.json') as f:
+        data = f.read()
+    config = json.loads(data)
+    trainset_config = config["trainset_config"]
+
+    trainloader = load_CleanNoisyPairDataset(**trainset_config, subset='training', batch_size=2, num_gpus=1)
+    testloader = load_CleanNoisyPairDataset(**trainset_config, subset='testing', batch_size=2, num_gpus=1)
+    print(len(trainloader), len(testloader))
+
+    for clean_audio, noisy_audio, fileid in trainloader: 
+        clean_audio = clean_audio.cuda()
+        noisy_audio = noisy_audio.cuda()
+        print(clean_audio.shape, noisy_audio.shape, fileid)
+        break
+    

denoise.py

@@ -0,0 +1,124 @@
+import os
+import argparse
+import json
+from tqdm import tqdm
+from copy import deepcopy
+
+import numpy as np
+import torch
+
+import random
+random.seed(0)
+torch.manual_seed(0)
+np.random.seed(0)
+
+from scipy.io.wavfile import write as wavwrite
+
+from dataset import load_CleanNoisyPairDataset
+from util import find_max_epoch, print_size, sampling
+from network import CleanUNet
+
+
+def denoise(output_directory, ckpt_iter, subset, dump=False):
+    """
+    Denoise audio
+
+    Parameters:
+    output_directory (str):         save generated speeches to this path
+    ckpt_iter (int or 'max'):       the pretrained checkpoint to be loaded; 
+                                    automitically selects the maximum iteration if 'max' is selected
+    subset (str):                   training, testing, validation
+    dump (bool):                    whether save enhanced (denoised) audio
+    """
+
+    # setup local experiment path
+    exp_path = train_config["exp_path"]
+    print('exp_path:', exp_path)
+
+    # load data
+    loader_config = deepcopy(trainset_config)
+    loader_config["crop_length_sec"] = 0
+    dataloader = load_CleanNoisyPairDataset(
+        **loader_config, 
+        subset=subset,
+        batch_size=1, 
+        num_gpus=1
+    )
+
+    # predefine model
+    net = CleanUNet(**network_config).cuda()
+    print_size(net)
+
+    # load checkpoint
+    ckpt_directory = os.path.join(train_config["log"]["directory"], exp_path, 'checkpoint')
+    if ckpt_iter == 'max':
+        ckpt_iter = find_max_epoch(ckpt_directory)
+    if ckpt_iter != 'pretrained':
+        ckpt_iter = int(ckpt_iter)
+    model_path = os.path.join(ckpt_directory, '{}.pkl'.format(ckpt_iter))
+    checkpoint = torch.load(model_path, map_location='cpu')
+    net.load_state_dict(checkpoint['model_state_dict'])
+    net.eval()
+
+    # get output directory ready
+    if ckpt_iter == "pretrained":
+        speech_directory = os.path.join(output_directory, exp_path, 'speech', ckpt_iter) 
+    else:
+        speech_directory = os.path.join(output_directory, exp_path, 'speech', '{}k'.format(ckpt_iter//1000))
+    if dump and not os.path.isdir(speech_directory):
+        os.makedirs(speech_directory)
+        os.chmod(speech_directory, 0o775)
+    print("speech_directory: ", speech_directory, flush=True)
+
+    # inference
+    all_generated_audio = []
+    all_clean_audio = []
+    sortkey = lambda name: '_'.join(name.split('/')[-1].split('_')[1:])
+    for clean_audio, noisy_audio, fileid in tqdm(dataloader):
+        filename = sortkey(fileid[0][0])
+
+        noisy_audio = noisy_audio.cuda()
+        LENGTH = len(noisy_audio[0].squeeze())
+        generated_audio = sampling(net, noisy_audio)
+        
+        if dump:
+            wavwrite(os.path.join(speech_directory, 'enhanced_{}'.format(filename)), 
+                    trainset_config["sample_rate"],
+                    generated_audio[0].squeeze().cpu().numpy())
+        else:
+            all_clean_audio.append(clean_audio[0].squeeze().cpu().numpy())
+            all_generated_audio.append(generated_audio[0].squeeze().cpu().numpy())
+
+    return all_clean_audio, all_generated_audio
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', type=str, default='config.json',
+                        help='JSON file for configuration')
+    parser.add_argument('-ckpt_iter', '--ckpt_iter', default='max',
+                        help='Which checkpoint to use; assign a number or "max" or "pretrained"')     
+    parser.add_argument('-subset', '--subset', type=str, choices=['training', 'testing', 'validation'],
+                        default='testing', help='subset for denoising')
+    args = parser.parse_args()
+
+    # Parse configs. Globals nicer in this case
+    with open(args.config) as f:
+        data = f.read()
+    config = json.loads(data)
+    gen_config              = config["gen_config"]
+    global network_config
+    network_config          = config["network_config"]      # to define wavenet
+    global train_config
+    train_config            = config["train_config"]        # train config
+    global trainset_config
+    trainset_config         = config["trainset_config"]     # to read trainset configurations
+
+    torch.backends.cudnn.enabled = True
+    torch.backends.cudnn.benchmark = True
+
+    if args.subset == "testing":
+        denoise(gen_config["output_directory"],
+                subset=args.subset,
+                ckpt_iter=args.ckpt_iter,
+                dump=True)

exp/DNS-large-full/checkpoint/pretrained.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:145c101eb5bbfa3ba52fb2b4ec7e5b64a361c102f89291f75e1dd42601d95dc9
+size 184336765

exp/DNS-large-high/checkpoint/pretrained.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:513d9e4f69483bf2bcc3059dd6b3644140763bf3f22df41d7ee366cc2cbd1829
+size 184336765

network.py

@@ -0,0 +1,386 @@
+# Copyright (c) 2022 NVIDIA CORPORATION. 
+#   Licensed under the MIT license.
+
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from util import weight_scaling_init
+
+
+# Transformer (encoder) https://github.com/jadore801120/attention-is-all-you-need-pytorch
+# Original Copyright 2017 Victor Huang
+#  MIT License (https://opensource.org/licenses/MIT)
+
+class ScaledDotProductAttention(nn.Module):
+    ''' Scaled Dot-Product Attention '''
+
+    def __init__(self, temperature, attn_dropout=0.1):
+        super().__init__()
+        self.temperature = temperature
+        self.dropout = nn.Dropout(attn_dropout)
+
+    def forward(self, q, k, v, mask=None):
+
+        attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
+
+        if mask is not None:
+            _MASKING_VALUE = -1e9 if attn.dtype == torch.float32 else -1e4
+            attn = attn.masked_fill(mask == 0, _MASKING_VALUE)
+
+        attn = self.dropout(F.softmax(attn, dim=-1))
+        output = torch.matmul(attn, v)
+
+        return output, attn
+
+
+class MultiHeadAttention(nn.Module):
+    ''' Multi-Head Attention module '''
+
+    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
+        super().__init__()
+
+        self.n_head = n_head
+        self.d_k = d_k
+        self.d_v = d_v
+
+        self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
+        self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
+        self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
+        self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
+
+        self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
+
+        self.dropout = nn.Dropout(dropout)
+        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
+
+
+    def forward(self, q, k, v, mask=None):
+
+        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
+        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
+
+        residual = q
+
+        # Pass through the pre-attention projection: b x lq x (n*dv)
+        # Separate different heads: b x lq x n x dv
+        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
+        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
+        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
+
+        # Transpose for attention dot product: b x n x lq x dv
+        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
+
+        if mask is not None:
+            mask = mask.unsqueeze(1)   # For head axis broadcasting.
+
+        q, attn = self.attention(q, k, v, mask=mask)
+
+        # Transpose to move the head dimension back: b x lq x n x dv
+        # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
+        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
+        q = self.dropout(self.fc(q))
+        q += residual
+
+        q = self.layer_norm(q)
+
+        return q, attn
+
+
+class PositionwiseFeedForward(nn.Module):
+    ''' A two-feed-forward-layer module '''
+
+    def __init__(self, d_in, d_hid, dropout=0.1):
+        super().__init__()
+        self.w_1 = nn.Linear(d_in, d_hid) # position-wise
+        self.w_2 = nn.Linear(d_hid, d_in) # position-wise
+        self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+
+        residual = x
+
+        x = self.w_2(F.relu(self.w_1(x)))
+        x = self.dropout(x)
+        x += residual
+
+        x = self.layer_norm(x)
+
+        return x
+
+
+def get_subsequent_mask(seq):
+    ''' For masking out the subsequent info. '''
+    sz_b, len_s = seq.size()
+    subsequent_mask = (1 - torch.triu(
+        torch.ones((1, len_s, len_s), device=seq.device), diagonal=1)).bool()
+    return subsequent_mask
+
+
+class PositionalEncoding(nn.Module):
+
+    def __init__(self, d_hid, n_position=200):
+        super(PositionalEncoding, self).__init__()
+
+        # Not a parameter
+        self.register_buffer('pos_table', self._get_sinusoid_encoding_table(n_position, d_hid))
+
+    def _get_sinusoid_encoding_table(self, n_position, d_hid):
+        ''' Sinusoid position encoding table '''
+        # TODO: make it with torch instead of numpy
+
+        def get_position_angle_vec(position):
+            return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
+
+        sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
+        sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
+        sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
+
+        return torch.FloatTensor(sinusoid_table).unsqueeze(0)
+
+    def forward(self, x):
+        return x + self.pos_table[:, :x.size(1)].clone().detach()
+
+
+class EncoderLayer(nn.Module):
+    ''' Compose with two layers '''
+
+    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.0):
+        super(EncoderLayer, self).__init__()
+        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
+        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)
+
+    def forward(self, enc_input, slf_attn_mask=None):
+        enc_output, enc_slf_attn = self.slf_attn(
+            enc_input, enc_input, enc_input, mask=slf_attn_mask)
+        enc_output = self.pos_ffn(enc_output)
+        return enc_output, enc_slf_attn
+
+
+class TransformerEncoder(nn.Module):
+    ''' A encoder model with self attention mechanism. '''
+
+    def __init__(
+            self, d_word_vec=512, n_layers=2, n_head=8, d_k=64, d_v=64,
+            d_model=512, d_inner=2048, dropout=0.1, n_position=624, scale_emb=False):
+
+        super().__init__()
+
+        # self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=pad_idx)
+        if n_position > 0:
+            self.position_enc = PositionalEncoding(d_word_vec, n_position=n_position)
+        else:
+            self.position_enc = lambda x: x
+        self.dropout = nn.Dropout(p=dropout)
+        self.layer_stack = nn.ModuleList([
+            EncoderLayer(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
+            for _ in range(n_layers)])
+        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
+        self.scale_emb = scale_emb
+        self.d_model = d_model
+
+    def forward(self, src_seq, src_mask, return_attns=False):
+
+        enc_slf_attn_list = []
+
+        # -- Forward
+        # enc_output = self.src_word_emb(src_seq)
+        enc_output = src_seq
+        if self.scale_emb:
+            enc_output *= self.d_model ** 0.5
+        enc_output = self.dropout(self.position_enc(enc_output))
+        enc_output = self.layer_norm(enc_output)
+
+        for enc_layer in self.layer_stack:
+            enc_output, enc_slf_attn = enc_layer(enc_output, slf_attn_mask=src_mask)
+            enc_slf_attn_list += [enc_slf_attn] if return_attns else []
+
+        if return_attns:
+            return enc_output, enc_slf_attn_list
+        return enc_output
+
+
+# CleanUNet architecture
+
+
+def padding(x, D, K, S):
+    """padding zeroes to x so that denoised audio has the same length"""
+
+    L = x.shape[-1]
+    for _ in range(D):
+        if L < K:
+            L = 1
+        else:
+            L = 1 + np.ceil((L - K) / S)
+
+    for _ in range(D):
+        L = (L - 1) * S + K
+    
+    L = int(L)
+    x = F.pad(x, (0, L - x.shape[-1]))
+    return x
+
+
+class CleanUNet(nn.Module):
+    """ CleanUNet architecture. """
+
+    def __init__(self, channels_input=1, channels_output=1,
+                 channels_H=64, max_H=768,
+                 encoder_n_layers=8, kernel_size=4, stride=2,
+                 tsfm_n_layers=3, 
+                 tsfm_n_head=8,
+                 tsfm_d_model=512, 
+                 tsfm_d_inner=2048):
+        
+        """
+        Parameters:
+        channels_input (int):   input channels
+        channels_output (int):  output channels
+        channels_H (int):       middle channels H that controls capacity
+        max_H (int):            maximum H
+        encoder_n_layers (int): number of encoder/decoder layers D
+        kernel_size (int):      kernel size K
+        stride (int):           stride S
+        tsfm_n_layers (int):    number of self attention blocks N
+        tsfm_n_head (int):      number of heads in each self attention block
+        tsfm_d_model (int):     d_model of self attention
+        tsfm_d_inner (int):     d_inner of self attention
+        """
+
+        super(CleanUNet, self).__init__()
+
+        self.channels_input = channels_input
+        self.channels_output = channels_output
+        self.channels_H = channels_H
+        self.max_H = max_H
+        self.encoder_n_layers = encoder_n_layers
+        self.kernel_size = kernel_size
+        self.stride = stride
+
+        self.tsfm_n_layers = tsfm_n_layers
+        self.tsfm_n_head = tsfm_n_head
+        self.tsfm_d_model = tsfm_d_model
+        self.tsfm_d_inner = tsfm_d_inner
+
+        # encoder and decoder
+        self.encoder = nn.ModuleList()
+        self.decoder = nn.ModuleList()
+
+        for i in range(encoder_n_layers):
+            self.encoder.append(nn.Sequential(
+                nn.Conv1d(channels_input, channels_H, kernel_size, stride),
+                nn.ReLU(),
+                nn.Conv1d(channels_H, channels_H * 2, 1), 
+                nn.GLU(dim=1)
+            ))
+            channels_input = channels_H
+
+            if i == 0:
+                # no relu at end
+                self.decoder.append(nn.Sequential(
+                    nn.Conv1d(channels_H, channels_H * 2, 1), 
+                    nn.GLU(dim=1),
+                    nn.ConvTranspose1d(channels_H, channels_output, kernel_size, stride)
+                ))
+            else:
+                self.decoder.insert(0, nn.Sequential(
+                    nn.Conv1d(channels_H, channels_H * 2, 1), 
+                    nn.GLU(dim=1),
+                    nn.ConvTranspose1d(channels_H, channels_output, kernel_size, stride),
+                    nn.ReLU()
+                ))
+            channels_output = channels_H
+            
+            # double H but keep below max_H
+            channels_H *= 2
+            channels_H = min(channels_H, max_H)
+        
+        # self attention block
+        self.tsfm_conv1 = nn.Conv1d(channels_output, tsfm_d_model, kernel_size=1)
+        self.tsfm_encoder = TransformerEncoder(d_word_vec=tsfm_d_model, 
+                                               n_layers=tsfm_n_layers, 
+                                               n_head=tsfm_n_head, 
+                                               d_k=tsfm_d_model // tsfm_n_head, 
+                                               d_v=tsfm_d_model // tsfm_n_head, 
+                                               d_model=tsfm_d_model, 
+                                               d_inner=tsfm_d_inner, 
+                                               dropout=0.0, 
+                                               n_position=0, 
+                                               scale_emb=False)
+        self.tsfm_conv2 = nn.Conv1d(tsfm_d_model, channels_output, kernel_size=1)
+
+        # weight scaling initialization
+        for layer in self.modules():
+            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
+                weight_scaling_init(layer)
+
+    def forward(self, noisy_audio):
+        # (B, L) -> (B, C, L)
+        if len(noisy_audio.shape) == 2:
+            noisy_audio = noisy_audio.unsqueeze(1)
+        B, C, L = noisy_audio.shape
+        assert C == 1
+        
+        # normalization and padding
+        std = noisy_audio.std(dim=2, keepdim=True) + 1e-3
+        noisy_audio /= std
+        x = padding(noisy_audio, self.encoder_n_layers, self.kernel_size, self.stride)
+        
+        # encoder
+        skip_connections = []
+        for downsampling_block in self.encoder:
+            x = downsampling_block(x)
+            skip_connections.append(x)
+        skip_connections = skip_connections[::-1]
+
+        # attention mask for causal inference; for non-causal, set attn_mask to None
+        len_s = x.shape[-1]  # length at bottleneck
+        attn_mask = (1 - torch.triu(torch.ones((1, len_s, len_s), device=x.device), diagonal=1)).bool()
+
+        x = self.tsfm_conv1(x)  # C 1024 -> 512
+        x = x.permute(0, 2, 1)
+        x = self.tsfm_encoder(x, src_mask=attn_mask)
+        x = x.permute(0, 2, 1)
+        x = self.tsfm_conv2(x)  # C 512 -> 1024
+
+        # decoder
+        for i, upsampling_block in enumerate(self.decoder):
+            skip_i = skip_connections[i]
+            x += skip_i[:, :, :x.shape[-1]]
+            x = upsampling_block(x)
+
+        x = x[:, :, :L] * std
+        return x
+
+
+if __name__ == '__main__':
+    import json
+    import argparse 
+    import os
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', type=str, default='configs/DNS-large-full.json', 
+                        help='JSON file for configuration')
+    args = parser.parse_args()
+
+    with open(args.config) as f:
+        data = f.read()
+    config = json.loads(data)
+    network_config = config["network_config"]
+
+    model = CleanUNet(**network_config).cuda()
+    from util import print_size
+    print_size(model, keyword="tsfm")
+    
+    input_data = torch.ones([4,1,int(4.5*16000)]).cuda()
+    output = model(input_data)
+    print(output.shape)
+
+    y = torch.rand([4,1,int(4.5*16000)]).cuda()
+    loss = torch.nn.MSELoss()(y, output)
+    loss.backward()
+    print(loss.item())
+    

util.py

@@ -0,0 +1,224 @@
+import os
+import time 
+import functools
+import numpy as np
+from math import cos, pi, floor, sin
+from tqdm import tqdm
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from stft_loss import MultiResolutionSTFTLoss
+
+
+def flatten(v):
+    return [x for y in v for x in y]
+
+
+def rescale(x):
+    return (x - x.min()) / (x.max() - x.min())
+
+
+def find_max_epoch(path):
+    """
+    Find latest checkpoint
+    
+    Returns:
+    maximum iteration, -1 if there is no (valid) checkpoint
+    """
+
+    files = os.listdir(path)
+    epoch = -1
+    for f in files:
+        if len(f) <= 4:
+            continue
+        if f[-4:]  == '.pkl':
+            number = f[:-4]
+            try:
+                epoch = max(epoch, int(number))
+            except:
+                continue
+    return epoch
+
+
+def print_size(net, keyword=None):
+    """
+    Print the number of parameters of a network
+    """
+
+    if net is not None and isinstance(net, torch.nn.Module):
+        module_parameters = filter(lambda p: p.requires_grad, net.parameters())
+        params = sum([np.prod(p.size()) for p in module_parameters])
+        
+        print("{} Parameters: {:.6f}M".format(
+            net.__class__.__name__, params / 1e6), flush=True, end="; ")
+        
+        if keyword is not None:
+            keyword_parameters = [p for name, p in net.named_parameters() if p.requires_grad and keyword in name]
+            params = sum([np.prod(p.size()) for p in keyword_parameters])
+            print("{} Parameters: {:.6f}M".format(
+                keyword, params / 1e6), flush=True, end="; ")
+        
+        print(" ")
+
+
+####################### lr scheduler: Linear Warmup then Cosine Decay #############################
+
+# Adapted from https://github.com/rosinality/vq-vae-2-pytorch
+
+# Original Copyright 2019 Kim Seonghyeon
+#  MIT License (https://opensource.org/licenses/MIT)
+
+
+def anneal_linear(start, end, proportion):
+    return start + proportion * (end - start)
+
+
+def anneal_cosine(start, end, proportion):
+    cos_val = cos(pi * proportion) + 1
+    return end + (start - end) / 2 * cos_val
+
+
+class Phase:
+    def __init__(self, start, end, n_iter, cur_iter, anneal_fn):
+        self.start, self.end = start, end
+        self.n_iter = n_iter
+        self.anneal_fn = anneal_fn
+        self.n = cur_iter
+
+    def step(self):
+        self.n += 1
+
+        return self.anneal_fn(self.start, self.end, self.n / self.n_iter)
+
+    def reset(self):
+        self.n = 0
+
+    @property
+    def is_done(self):
+        return self.n >= self.n_iter
+
+
+class LinearWarmupCosineDecay:
+    def __init__(
+        self,
+        optimizer,
+        lr_max,
+        n_iter,
+        iteration=0,
+        divider=25,
+        warmup_proportion=0.3,
+        phase=('linear', 'cosine'),
+    ):
+        self.optimizer = optimizer
+
+        phase1 = int(n_iter * warmup_proportion)
+        phase2 = n_iter - phase1
+        lr_min = lr_max / divider
+
+        phase_map = {'linear': anneal_linear, 'cosine': anneal_cosine}
+
+        cur_iter_phase1 = iteration
+        cur_iter_phase2 = max(0, iteration - phase1)
+        self.lr_phase = [
+            Phase(lr_min, lr_max, phase1, cur_iter_phase1, phase_map[phase[0]]),
+            Phase(lr_max, lr_min / 1e4, phase2, cur_iter_phase2, phase_map[phase[1]]),
+        ]
+
+        if iteration < phase1:
+            self.phase = 0
+        else:
+            self.phase = 1
+
+    def step(self):
+        lr = self.lr_phase[self.phase].step()
+
+        for group in self.optimizer.param_groups:
+            group['lr'] = lr
+
+        if self.lr_phase[self.phase].is_done:
+            self.phase += 1
+
+        if self.phase >= len(self.lr_phase):
+            for phase in self.lr_phase:
+                phase.reset()
+
+            self.phase = 0
+
+        return lr
+
+
+####################### model util #############################
+
+def std_normal(size):
+    """
+    Generate the standard Gaussian variable of a certain size
+    """
+
+    return torch.normal(0, 1, size=size).cuda()
+
+
+def weight_scaling_init(layer):
+    """
+    weight rescaling initialization from https://arxiv.org/abs/1911.13254
+    """
+    w = layer.weight.detach()
+    alpha = 10.0 * w.std()
+    layer.weight.data /= torch.sqrt(alpha)
+    layer.bias.data /= torch.sqrt(alpha)
+
+
+@torch.no_grad()
+def sampling(net, noisy_audio):
+    """
+    Perform denoising (forward) step
+    """
+
+    return net(noisy_audio)
+
+
+def loss_fn(net, X, ell_p, ell_p_lambda, stft_lambda, mrstftloss, **kwargs):
+    """
+    Loss function in CleanUNet
+
+    Parameters:
+    net: network
+    X: training data pair (clean audio, noisy_audio)
+    ell_p: \ell_p norm (1 or 2) of the AE loss
+    ell_p_lambda: factor of the AE loss
+    stft_lambda: factor of the STFT loss
+    mrstftloss: multi-resolution STFT loss function
+
+    Returns:
+    loss: value of objective function
+    output_dic: values of each component of loss
+    """
+
+    assert type(X) == tuple and len(X) == 2
+    
+    clean_audio, noisy_audio = X
+    B, C, L = clean_audio.shape
+    output_dic = {}
+    loss = 0.0
+    
+    # AE loss
+    denoised_audio = net(noisy_audio)  
+
+    if ell_p == 2:
+        ae_loss = nn.MSELoss()(denoised_audio, clean_audio)
+    elif ell_p == 1:
+        ae_loss = F.l1_loss(denoised_audio, clean_audio)
+    else:
+        raise NotImplementedError
+    loss += ae_loss * ell_p_lambda
+    output_dic["reconstruct"] = ae_loss.data * ell_p_lambda
+
+    if stft_lambda > 0:
+        sc_loss, mag_loss = mrstftloss(denoised_audio.squeeze(1), clean_audio.squeeze(1))
+        loss += (sc_loss + mag_loss) * stft_lambda
+        output_dic["stft_sc"] = sc_loss.data * stft_lambda
+        output_dic["stft_mag"] = mag_loss.data * stft_lambda
+
+    return loss, output_dic
+