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!obj:pylearn2.train.Train { | |
dataset: &train !obj:research.code.pylearn2.datasets.timit.TIMIT { | |
which_set: 'train', | |
frame_length: &flen 160, | |
frames_per_example: &fpe 1, | |
samples_to_predict: &ylen 1, | |
n_next_phones: 1, | |
n_prev_phones: 1, | |
#start: 0, | |
#stop: 100, |
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using BinDeps | |
@BinDeps.setup | |
@unix_only begin | |
ecos = library_dependency("ecos",aliases=["libecos"]) | |
end | |
provides(Sources, URI("https://github.com/ifa-ethz/ecos/archive/master.zip"), | |
[ecos], os = :Unix, unpacked_dir="ecos-master") |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
import wave | |
import numpy as np | |
import scipy as sp | |
WINSIZE=8192 | |
sound='sound.wav' | |
def read_signal(filename, winsize): |
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### Keybase proof | |
I hereby claim: | |
* I am jfsantos on github. | |
* I am jfsantos (https://keybase.io/jfsantos) on keybase. | |
* I have a public key whose fingerprint is C422 70CC D7E3 C653 09B0 E52E 06AF B67E AD5E 95E5 | |
To claim this, I am signing this object: |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
import wave | |
import numpy as np | |
import scipy as sp | |
WINSIZE=8192 | |
sound='sound.wav' | |
def read_signal(filename, winsize): |
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Traceback (most recent call last): | |
File "<string>", line 1, in <module> | |
File "/private/var/folders/z3/s0__77pd3n1bpym4f75hrzbc0000gn/T/pip_build_jfsantos/music21/setup.py", line 65, in <module> | |
include_package_data=True, | |
File "/Users/jfsantos/anaconda/envs/py3/lib/python3.3/distutils/core.py", line 148, in setup |
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from gammatone.fftweight import fft_gtgram | |
from scipy.io.matlab import loadmat | |
s = loadmat("test.mat")["s"][:,0] | |
fs = 16000 | |
# gt_py has 260 frames | |
gt_py = fft_gtgram(s, fs, 0.010, 0.0025, 23, 125) | |
# gt_mat has 269 frames |
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ENV["MOCHA_USE_CUDA"] = "true" | |
using HDF5, JLD, Mocha | |
X = Array[] | |
push!(X, rand(Float32, 128,11*129,1,1)) | |
y = Array[] | |
push!(y, rand(Float32, 128, 129, 1, 1)) | |
#data_layer = AsyncHDF5DataLayer("train", "train.txt", 128, 1000, [:features, :targets], false, []) |
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import numpy as np | |
import scipy.signal as sig | |
from adaptfilt import lms | |
if __name__ == '__main__': | |
import matplotlib.pyplot as plt | |
from scipy.io import wavfile | |
sigma = 0.1 | |
order = 100 |
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