Deep-Music-Enhancer (code, models, paper)

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+On[[:space:]]Filter[[:space:]]Generalization[[:space:]]for[[:space:]]Music[[:space:]]Bandwidth[[:space:]]Extension[[:space:]]Using[[:space:]]Deep[[:space:]]Neural[[:space:]]Networks.pdf filter=lfs diff=lfs merge=lfs -text

On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks.pdf

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models/ailia-models/code/LICENSE.md

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+Copyright © 2019 INESC TEC 
+ 
+Deep music enhancer. Uses deep neural networks to extend the bandwidth of musical audio.
+
+This software is authored by: 
+Serkan Sulun
+
+
+This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
+You can reach INESC TEC at info@inesctec.pt, or
+Campus da Faculdade de Engenharia da Universidade do Porto
+Rua Dr. Roberto Frias
+4200-465 Porto
+Portugal
+
+A commercial license is also available for use in industrial projects and collaborations that do not wish to use the GPL 3 license.
+
+If you use deep music enhancer in a work that leads to a scientific publication, we would appreciate it if you would kindly cite deep music enhancer in your manuscript.
+
+S. Sulun and M. E. P. Davies, "On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks," in IEEE Journal of Selected Topics in Signal Processing
+
+The paper can be found at https://doi.org/10.1109/JSTSP.2020.3037485
+
+
+
+
+
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+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
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+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
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+
+  13. Use with the GNU Affero General Public License.
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+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
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+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+copy of the Program in return for a fee.

models/ailia-models/code/README.md

@@ -0,0 +1,85 @@
+# On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks
+
+## Input
+
+Audio file (.wav file)
+
+input.wav is `(/Test/003 - Actions - One Minute Smile/mixture.wav)` in DSD100 dataset. (can be donwloaded from http://liutkus.net/DSD100.zip)   
+To reduce calculation cost, input.wav is clipped from original.
+
+
+## Output
+
+Bandwidth extented audio file (.wav file)
+
+
+## Usage
+Automatically downloads the onnx and prototxt files on the first run.
+It is necessary to be connected to the Internet while downloading.
+
+For the sample wav,
+```bash
+$ python3 deep_music_enhancer.py
+```
+
+Supported model types are [`resnet`, `resnet_bn`, `resnet_da`, `resnet_do`, `unet`, `unet_bn`, `unet_da`, `unet_do`].     
+bn means batch normlization, do means dropout, da means data augmentation.    
+Model type can be specified as below.
+```
+$ python3 deep_music_enhancer.py --model [MODEL TYPE]
+```
+
+
+You can specify input audio files by adding `--input` option.
+
+```
+$ python3 deep_music_enhancer.py --input [INPUT WAV FILE]
+```
+
+If you save audio output with specified name, you have to add `--savefile` option.
+
+```
+$ python3 deep_music_enhancer.py --savepath [OUTPUT NAME]
+```
+
+Additionaly, you can use `--vis` option in order to visualize spectrogram of input and output audio.
+
+Spectrogram of input audio    
+![Spectrogram of input audio](input_butter_input_spec.png "Spectrogram of input audio")
+
+Spectrogram of output audio (butter filter)    
+![Spectrogram of output audio (butter filter)](input_butter_output_spec.png "Spectrogram of output audio (butter filter)")
+
+Spectrogram of output audio (cheby1 filter)    
+![Spectrogram of output audio (cheby1 filter)](input_cheby1_output_spec.png "Spectrogram of output audio (cheby1 filter)")
+
+
+## Reference
+
+[deep-music-enhancer](https://github.com/serkansulun/deep-music-enhancer)
+
+
+## Framework
+
+Pytorch
+
+## Model Format
+
+ONNX opset=11
+
+## Netron
+[resnet.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnet.onnx.prototxt)
+
+[resnetbn.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetbn.onnx.prototxt)
+
+[resnetda.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetda.onnx.prototxt)
+
+[resnetdo.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetdo.onnx.prototxt)
+
+[unet.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/deep-music-enhancer/unet.onnx.prototxt)
+
+[unetbn.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/deep-music-enhancer/unetbn.onnx.prototxt)
+
+[unetda.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/deep-music-enhancer/unetda.onnx.prototxt)
+
+[unetdo.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/deep-music-enhancer/unetdo.onnx.prototxt)

models/ailia-models/code/deep_music_enhancer.py

@@ -0,0 +1,174 @@
+import time
+import sys
+import argparse
+
+import numpy as np
+
+import ailia  # noqa: E402
+
+# import original modules
+sys.path.append('../../util')
+from arg_utils import get_base_parser, update_parser, get_savepath  # noqa: E402
+from model_utils import check_and_download_models  # noqa: E402
+
+# logger
+from logging import getLogger   # noqa: E402
+logger = getLogger(__name__)
+
+import os
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+from scipy.io import wavfile
+from deep_music_enhancer_utils import (
+    read_audio,
+    SingleSong
+)
+
+
+# ======================
+# PARAMETERS
+# ======================
+WAV_PATH = 'input.wav'
+SAVE_WAV_PATH = 'output.wav'
+
+WEIGHT_PATH_RESNET = 'resnet.onnx'
+MODEL_PATH_RESNET = 'resnet.onnx.prototxt'
+WEIGHT_PATH_RESNET_BN = 'resnetbn.onnx'
+MODEL_PATH_RESNET_BN = 'resnetbn.onnx.prototxt'
+WEIGHT_PATH_RESNET_DA = 'resnetda.onnx'
+MODEL_PATH_RESNET_DA = 'resnetda.onnx.prototxt'
+WEIGHT_PATH_RESNET_DO = 'resnetdo.onnx'
+MODEL_PATH_RESNET_DO = 'resnetdo.onnx.prototxt'
+
+WEIGHT_PATH_UNET = 'unet.onnx'
+MODEL_PATH_UNET = 'unet.onnx.prototxt'
+WEIGHT_PATH_UNET_BN = 'unetbn.onnx'
+MODEL_PATH_UNET_BN = 'unetbn.onnx.prototxt'
+WEIGHT_PATH_UNET_DA = 'unetda.onnx'
+MODEL_PATH_UNET_DA = 'unetda.onnx.prototxt'
+WEIGHT_PATH_UNET_DO = 'unetdo.onnx'
+MODEL_PATH_UNET_DO = 'unetdo.onnx.prototxt'
+
+REMOTE_PATH = 'https://storage.googleapis.com/ailia-models/deep-music-enhancer/'
+
+
+# ======================
+# Arguemnt Parser Config
+# ======================
+parser = get_base_parser(
+    'On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks', 
+    WAV_PATH, 
+    SAVE_WAV_PATH
+)
+# overwrite
+parser.add_argument(
+    '--input', '-i', metavar='WAV', default=WAV_PATH,
+    help='input audio'
+)
+parser.add_argument(
+    '--ailia_audio', action='store_true',
+    help='use ailia audio library'
+)
+parser.add_argument(
+    '--vis', action='store_true',
+    help='save visualized spectrogram'
+)
+parser.add_argument(
+    '--model', type=str, default='unet',
+    choices=[
+        'resnet', 'resnet_bn', 'resnet_da', 'resnet_do',
+        'unet', 'unet_bn', 'unet_da', 'unet_do'
+    ],
+)
+args = update_parser(parser, check_input_type=False)
+
+
+# ======================
+# Main function
+# ======================
+def audio_bandwidth_extension(net):
+    FILTERS_TEST = [('cheby1', 6), ('butter', 6)]
+    c_SAMPLE_RATE = 44100
+    c_WAV_SAMPLE_LEN = 8192 
+    cutoff = 11025
+    duration = None
+    start = 0
+
+    for filter_ in FILTERS_TEST:
+        input_name = args.input[0]
+        input_name_without_ext = os.path.splitext(os.path.basename(input_name))[0]
+        hq_path = input_name
+
+        logger.info('filter: {}, input_name: {}'.format(filter_, input_name))
+
+        # create dataset
+        song_data = SingleSong(
+            c_WAV_SAMPLE_LEN, 
+            filter_, 
+            hq_path,
+            cutoff=cutoff, 
+            duration=duration, 
+            start=start
+        )
+
+        y_full = song_data.preallocate()  # preallocation to keep individual output chunks
+
+        idx_start_chunk = 0 # model works on chunks of audio, these are concatenated later
+
+        for i in tqdm(range(len(song_data))):
+            x, t = song_data[i]
+            x = x[np.newaxis, :, :]
+
+            y = net.predict(x)
+
+            idx_end_chunk = idx_start_chunk + y.shape[0]
+            y_full[idx_start_chunk:idx_end_chunk] = y
+            idx_start_chunk = idx_end_chunk
+
+        y_full = np.concatenate(y_full, axis=-1) # create full song out of chunks
+
+        x_full, t_full = song_data.get_full_signals()
+        y_full = np.clip(y_full, -1, 1 - np.finfo(np.float32).eps)
+
+        # save audio
+        wavfile.write(args.savepath, c_SAMPLE_RATE, y_full.T)
+
+        # save spec
+        if args.vis:
+            _, _, _, _ = plt.specgram(x_full.T[:c_SAMPLE_RATE*5, 0], Fs=c_SAMPLE_RATE)
+            plt.savefig('{}_{}_input_spec.png'.format(input_name_without_ext, filter_[0]))
+            _, _, _, _ = plt.specgram(y_full.T[:c_SAMPLE_RATE*5, 0], Fs=c_SAMPLE_RATE)
+            plt.savefig('{}_{}_output_spec.png'.format(input_name_without_ext, filter_[0]))
+
+    logger.info('Script finished successfully.')
+
+
+def main():
+    # model files check and download
+    if args.model == 'resnet':
+        weight_path, model_path = WEIGHT_PATH_RESNET, MODEL_PATH_RESNET
+    elif args.model == 'resnet_bn':
+        weight_path, model_path = WEIGHT_PATH_RESNET_BN, MODEL_PATH_RESNET_BN
+    elif args.model == 'resnet_da':
+        weight_path, model_path = WEIGHT_PATH_RESNET_DA, MODEL_PATH_RESNET_DA
+    elif args.model == 'resnet_do':
+        weight_path, model_path = WEIGHT_PATH_RESNET_DO, MODEL_PATH_RESNET_DO
+    elif args.model == 'unet':
+        weight_path, model_path = WEIGHT_PATH_UNET, MODEL_PATH_UNET
+    elif args.model == 'unet_bn':
+        weight_path, model_path = WEIGHT_PATH_UNET_BN, MODEL_PATH_UNET_BN
+    elif args.model == 'unet_da':
+        weight_path, model_path = WEIGHT_PATH_UNET_DA, MODEL_PATH_UNET_DA
+    elif args.model == 'unet_do':
+        weight_path, model_path = WEIGHT_PATH_UNET_DO, MODEL_PATH_UNET_DO
+
+    env_id = args.env_id
+
+    check_and_download_models(weight_path, model_path, REMOTE_PATH)
+    net = ailia.Net(model_path, weight_path, env_id=env_id)
+
+    audio_bandwidth_extension(net)
+
+
+if __name__ == "__main__":
+    main()

models/ailia-models/code/deep_music_enhancer_utils.py

@@ -0,0 +1,104 @@
+from scipy.io import wavfile
+from scipy import signal
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+class SingleSong:
+    # To load one excerpt with arbitrary length, or one full song, for test or validation
+    def __init__(self, chunk_len, filter_, hq_path, cutoff, duration=None, start=8):
+
+        hq, sr = read_audio(hq_path)    # high quality target
+        lq = lowpass(hq, cutoff, filter_=filter_)  # low quality input
+
+        # CROP
+        song_len = lq.shape[-1]
+
+        if duration is None:    # save entire song
+            test_start = 0
+            test_len = song_len
+        else:
+            test_start = start * sr    # start from n th second
+            test_len = duration * sr
+            
+        test_len = min(test_len, song_len - test_start)    
+
+        lq = lq[:, test_start:test_start + test_len]
+        hq = hq[:, test_start:test_start + test_len]
+
+        self.x_full = lq.copy()
+        self.t_full = hq.copy()
+
+        # To have equal length chunks for minibatching
+        time_len = lq.shape[-1]
+        n_chunks, rem = divmod(time_len, chunk_len)
+        lq = lq[..., :-rem or None]    # or None handles rem=0
+        hq = hq[..., :-rem or None]    
+
+        # adjust lengths
+        self.x_full = self.x_full[..., :lq.shape[-1] or None]
+        self.t_full = self.t_full[..., :lq.shape[-1] or None]
+
+        # Save full samples
+    
+        self.lq = np.split(lq, n_chunks, axis=-1)   # create a lists of chunks
+        self.hq = np.split(hq, n_chunks, axis=-1)   # create a lists of chunks
+
+    def get_full_signals(self):
+        # Returns full length input and target
+        return self.x_full, self.t_full
+
+    def preallocate(self):
+        """
+        Preallocates the matrix to save all minibatch outputs.
+        It is faster to transfer all minibatches from GPU to CPU at once.
+        """
+        return np.zeros((len(self.lq), *self.lq[0].shape))
+
+    def __len__(self):
+        return len(self.lq)
+
+    def __getitem__(self, idx):
+        return self.lq[idx], self.hq[idx]
+
+
+def lowpass(sig, cutoff, filter_=('cheby1', 8), sr=44100):
+    """Lowpasses input signal based on a cutoff frequency
+    
+    Arguments:
+        sig {numpy 1d array} -- input signal
+        cutoff {int} -- cutoff frequency
+    
+    Keyword Arguments:
+        sr {int} -- sampling rate of the input signal (default: {44100})
+        filter_type {str} -- type of filter, only butter and cheby1 are implemented (default: {'butter'})
+    
+    Returns:
+        numpy 1d array -- lowpassed signal
+    """
+    nyq = sr / 2
+    cutoff /= nyq
+
+    if filter_[0] == 'butter':
+        B, A = signal.butter(filter_[1], cutoff)
+    elif filter_[0] == 'cheby1':
+        B, A = signal.cheby1(filter_[1], 0.05, cutoff)
+    elif filter_[0] == 'bessel':
+        B, A = signal.bessel(filter_[1], cutoff, norm='mag')
+    elif filter_[0] == 'ellip':
+        B, A = signal.ellip(filter_[1], 0.05, 20, cutoff)
+
+    sig_lp = signal.filtfilt(B, A, sig)
+    return sig_lp.astype(np.float32)
+
+
+def read_audio(path, make_stereo=True):
+    sr, audio = wavfile.read(path)
+    audio = audio.T
+    if np.issubdtype(audio.dtype, np.int16):
+        audio = audio.astype(np.float32) / 32768.0
+    if len(audio.shape) == 1:    # if mono
+        audio = np.expand_dims(audio, axis=0)
+        if make_stereo:
+            audio = np.repeat(audio, 2, axis=0)
+    return audio, sr

models/ailia-models/code/input.wav

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+size 2721504

models/ailia-models/code/output.wav

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+oid sha256:bf30b706e6182e359cd5411597c1ebd7a6f126b194d68910f2851c0cd01459b7
+size 10747962

models/ailia-models/resnet.onnx

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models/ailia-models/resnet.onnx.prototxt

@@ -0,0 +1,2023 @@
+ir_version: 6
+producer_name: "pytorch"
+producer_version: "1.7"
+model_version: 0
+graph {
+  name: "torch-jit-export"
+  node {
+    input: "x"
+    input: "model.0.weight"
+    input: "model.0.bias"
+    output: "65"
+    name: "Conv_0"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 7
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "65"
+    input: "model.1.body.0.weight"
+    input: "model.1.body.0.bias"
+    output: "66"
+    name: "Conv_1"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      type: INTS
+    }
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+      name: "group"
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+      type: INT
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+      ints: 7
+      type: INTS
+    }
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+      name: "pads"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
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+      type: INTS
+    }
+  }
+  node {
+    input: "66"
+    output: "67"
+    name: "Relu_2"
+    op_type: "Relu"
+  }
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+    name: "Constant_4"
+    op_type: "Constant"
+    attribute {
+      name: "value"
+      t {
+        data_type: 1
+        raw_data: "\315\314\314="
+      }
+      type: TENSOR
+    }
+  }
+  node {
+    input: "68"
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+    output: "70"
+    name: "Mul_5"
+    op_type: "Mul"
+  }
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+    output: "71"
+    name: "Add_6"
+    op_type: "Add"
+  }
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models/ailia-models/resnetdo.onnx

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models/ailia-models/resnetdo.onnx.prototxt

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models/ailia-models/source.txt

@@ -0,0 +1,25 @@
+https://github.com/axinc-ai/ailia-models/blob/master/audio_processing/deep-music-enhancer
+
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnet.onnx
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnet.onnx.prototxt
+
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetbn.onnx
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetbn.onnx.prototxt
+
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetda.onnx
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetda.onnx.prototxt
+
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetdo.onnx
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/resnetdo.onnx.prototxt
+
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/unet.onnx
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/unet.onnx.prototxt
+
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/unetbn.onnx
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/unetbn.onnx.prototxt
+
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/unetda.onnx
+https://storage.googleapis.com/ailia-models/deep-music-enhancer/unetda.onnx.prototxt
+
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