This repository implements the audio super-resolution model proposed in:
S. Birnbaum, V. Kuleshov, Z. Enam, P. W.. Koh, and S. Ermon. Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. NeurIPS 2019
V. Kuleshov, Z. Enam, and S. Ermon. Audio Super Resolution Using Neural Networks. ICLR 2017 (Workshop track)
The model is implemented in Python 3.7.10 and uses several additional libraries.
tensorflow==2.4.1keras==2.4.0numpy==1.19.5scipy==1.6.0librosa==0.8.3h5py==2.10.0matplotlib==3.3.4
A full list of the packages on our enviornment is in requirements.txt
To install this package, simply clone the git repo:
git clone https://github.com/kuleshov/audio-super-res.git;
cd audio-super-res;
conda env create -f environment.yaml
conda activate audio-super-res
The repository is structured as follows.
./src: model source code./data: code to download the model data
The ./data subfolder contains code for preparing the VCTK speech dataset.
Make sure you have enough disk space and bandwidth (the dataset is over 18G, uncompressed).
You need to type:
cd ./data/vctk;
make;
Next, you must prepare the dataset for training:
you will need to create pairs of high and low resolution sound patches (typically, about 0.5s in length).
We have included a script called prep_vctk.py that does that, which works as follows.
usage: prep_vctk.py [-h] [--file-list FILE_LIST] [--in-dir IN_DIR] [--out OUT]
[--scale SCALE] [--dimension DIMENSION] [--stride STRIDE]
[--interpolate] [--low-pass] [--batch-size BATCH_SIZE]
[--sr SR] [--sam SAM]
optional arguments:
-h, --help show this help message and exit
--file-list FILE_LIST
list of input wav files to process
--in-dir IN_DIR folder where input files are located
--out OUT path to output h5 archive
--scale SCALE scaling factor
--dimension DIMENSION
dimension of patches (use -1 for no patching)
--stride STRIDE stride when extracting patches
--interpolate interpolate low-res patches with cubic splines
--low-pass apply low-pass filter when generating low-res patches
--batch-size BATCH_SIZE
we produce # of patches that is a multiple of batch
size
--sr SR audio sampling rate
--sam SAM subsampling factor for the data (only applicable for multispeaker data)
The output of the data preparation step are two .h5 archives containing, respectively, the training and validation pairs of high/low resolution sound patches.
You can also generate these by running make in the corresponding directory, e.g.
cd ./speaker1;
make;
This will use a set of default parameters.
To generate the files needed for the training example below, run the following from the speaker1 directory:
python ../prep_vctk.py \
--file-list speaker1-train-files.txt \
--in-dir ../VCTK-Corpus/wav48/p225 \
--out vctk-speaker1-train.4.16000.8192.4096.h5 \
--scale 4 \
--sr 16000 \
--dimension 8192 \
--stride 4096 \
--interpolate \
--low-pass
python ../prep_vctk.py \
--file-list speaker1-val-files.txt \
--in-dir ../VCTK-Corpus/wav48/p225 \
--out vctk-speaker1-val.4.16000.8192.4096.h5.tmp \
--scale 4 \
--sr 16000 \
--dimension 8192 \
--stride 4096 \
--interpolate \
--low-pass
python ../prep_vctk.py \
--file-list speaker1-train-files.txt \
--in-dir ../VCTK-Corpus/wav48/p225 \
--out vctk-speaker1-train.4.16000.-1.4096.h5 \
--scale 4 \
--sr 16000 \
--dimension -1 \
--stride 4096 \
--interpolate \
--low-pass
python ../prep_vctk.py \
--file-list speaker1-val-files.txt \
--in-dir ../VCTK-Corpus/wav48/p225 \
--out vctk-speaker1-val.4.16000.-1.4096.h5.tmp \
--scale 4 \
--sr 16000 \
--dimension -1 \
--stride 4096 \
--interpolate \
--low-pass
We have included code to prepare two datasets.
- The single-speaker dataset consists only of VCTK speaker #1; it is relatively quick to train a model (a few hours).
- The multi-speaker dataset uses the last 8 VCTK speakers for evaluation, and the rest for training; it takes several days to train the model, and several hours to prepare the data.
We suggest starting with the single-speaker dataset.
Running the model is handled by the src/run.py script.
usage: run.py train [-h] --train TRAIN --val VAL [-e EPOCHS]
[--batch-size BATCH_SIZE] [--logname LOGNAME]
[--layers LAYERS] [--alg ALG] [--lr LR] [--model MODEL]
[--r R] [--piano PIANO] [--grocery GROCERY]
optional arguments:
-h, --help show this help message and exit
--train TRAIN path to h5 archive of training patches
--val VAL path to h5 archive of validation set patches
-e EPOCHS, --epochs EPOCHS
number of epochs to train
--batch-size BATCH_SIZE
training batch size
--logname LOGNAME folder where logs will be stored
--layers LAYERS number of layers in each of the D and U halves of the
network
--alg ALG optimization algorithm
--lr LR learning rate
--model the model to use for training (audiounet, audiotfilm,
dnn, or spline). Defaults to audiounet.
--r the upscaling ratio of the data: make sure that the appropriate
datafile have been generated (note: to generate data with different
scaling ratios change the SCA parameter in the makefile)
--piano false by default--make true to train on piano data
--grocery false by default--make true to train on grocery imputation data
--speaker number of speakers being trained on (single or multi). Defaults to single
--pools_size size of pooling window
--strides size of pooling strides
--full false by default--whether to calculate the "full" snr after each epoch. The "full" snr
is the snr acorss the non-patched data file, rather than the average snr over all the
patches which is calculated by default
Note: to generate the data needed for the grocery imputation experiment, download train.csv.7z from https://www.kaggle.com/c/favorita-grocery-sales-forecasting/data into the data/grocery/grocery directory, unzip the csv, and run prep_grocery.py from the data/grocery directory.
For example, to run the model on data prepared for the single speaker dataset, you would type:
python run.py train \
--train ../data/vctk/speaker1/vctk-speaker1-train.4.16000.8192.4096.h5 \
--val ../data/vctk/speaker1/vctk-speaker1-val.4.16000.8192.4096.h5 \
-e 120 \
--batch-size 64 \
--lr 3e-4 \
--logname singlespeaker \
--model audiotfilm \
--r 4 \
--layers 4 \
--piano false \
--pool_size 2 \
--strides 2
--full true
The above run will store checkpoints in ./singlespeaker.lr0.000300.1.g4.b64.
Note on the models: audiotfilm is the best model.
See the below link for a pre-trained single-speaker model. This model was trained with the following parameters:
python run.py train \
--train ../data/vctk/speaker1/vctk-speaker1-train.4.16000.8192.4096.h5 \
--val ../data/vctk/speaker1/vctk-speaker1-val.4.16000.8192.4096.h5 \
-e 2 --batch-size 16 --lr 3e-4 --logname singlespeaker \
--model audiotfilm --r 4 --layers 4 --piano false \
--pool_size 2 --strides 2
https://drive.google.com/file/d/1pqIaxtZpt9GRc-Yp1zCzVoSbQFSLnERF/view?usp=sharing
To use the model, unzip the file in the src directory and run eval with the logname corresponding to the checkpoint file.
The run.py command may be also used to evaluate the model on new audio samples.
usage: run.py eval [-h] --logname LOGNAME [--out-label OUT_LABEL]
[--wav-file-list WAV_FILE_LIST] [--r R] [--sr SR]
optional arguments:
-h, --help show this help message and exit
--logname LOGNAME path to training checkpoint
--out-label OUT_LABEL
append label to output samples
--wav-file-list WAV_FILE_LIST
list of audio files for evaluation
--r R upscaling factor
--sr SR high-res sampling rate
In the above example, we would type:
python run.py eval \
--logname ./singlespeaker.lr0.000300.1.g4.b64/model.ckpt-20101 \
--out-label singlespeaker-out \
--wav-file-list ../data/vctk/speaker1/speaker1-val-files.txt \
--r 4 \
--pool_size 2 \
--strides 2 \
--model audiotfilm
This will look at each file specified via the --wav-file-list argument (these must be high-resolution samples),
and create for each file f.wav three audio samples:
f.singlespeaker-out.hr.wav: the high resolution versionf.singlespeaker-out.lr.wav: the low resolution version processed by the modelf.singlespeaker-out.pr.wav: the super-resolved version
These will be found in the same folder as f.wav. Because of how our model is defined, the number of samples in the input must be a multiple of 2**downscaling_layers; if that's not the case, we will clip the input file (potentially shortening it by a fraction of a second).
Disclaimer: We recently upgraded the versions of many of the packages, including Keras and Tensorflow. The example workflow for training and predicting should work, but the codebase has not been fully tested. Please create an issue if you run into any errors.
keras_layer.py implements the TFiLM layer as a customer Keras layer. The below code illustrates how to use this custom layer.
import numpy as np
from keras.layers import Input, Dense, Flatten, Lambda
from keras.models import Model
import tensorflow as tf
### Insert definition of TFiLM layer here. ####
x = np.random.random((2, 100, 50))
y = np.zeros((2))
inputs = Input(shape=(100, 50))
l = TFiLM(2)(inputs)
l = Flatten()(l)
outputs = Dense(1, activation='sigmoid')(l)
# This creates a model that includes
# the Input layer, a TFILM layer, and a dense layer
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
print(model.summary())
model.fit(x, y, epochs=10) # starts training
We would like to emphasize a few points.
- Machine learning algorithms are only as good as their training data. If you want to apply our method to your personal recordings, you will most likely need to collect additional labeled examples.
- You will need a very large model to fit large and diverse datasets (such as the 1M Songs Dataset)
- Interestingly, super-resolution works better on aliased input (no low-pass filter). This is not reflected well in objective benchmarks, but is noticeable when listening to the samples. For applications like compression (where you control the low-res signal), this may be important.
- More generally, the model is very sensitive to how low resolution samples are generated. Even the type of low-pass filter (Butterworth, Chebyshev) will affect performance.
The same architecture can be used on many time series tasks outside the audio domain. We have successfully used it to impute functional genomics data and denoise EEG recordings.
Send feedback to Sawyer Birnbaum.
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