From b6f828f2fb644c2cc84f285778873596dc0ff15a Mon Sep 17 00:00:00 2001
From: Pushkal Katara <katarapushkal@gmail.com>
Date: Thu, 22 Aug 2019 02:40:23 +0530
Subject: [PATCH 1/3] SRNN Datapreprocessing script
---
examples/pytorch/SRNN/SRNN_Example.ipynb | 60 +++++++-----
examples/pytorch/SRNN/SRNN_Example.py | 20 ++--
examples/pytorch/SRNN/process_google.py | 120 ++++++++++-------------
examples/pytorch/requirements.txt | 2 +
4 files changed, 102 insertions(+), 100 deletions(-)
create mode 100644 examples/pytorch/requirements.txt
diff --git a/examples/pytorch/SRNN/SRNN_Example.ipynb b/examples/pytorch/SRNN/SRNN_Example.ipynb
index 4538688f4..1f5811015 100644
--- a/examples/pytorch/SRNN/SRNN_Example.ipynb
+++ b/examples/pytorch/SRNN/SRNN_Example.ipynb
@@ -25,7 +25,8 @@
"import sys\n",
"import os\n",
"import numpy as np\n",
- "import torch"
+ "import torch\n",
+ "import h5py"
]
},
{
@@ -80,9 +81,16 @@
}
],
"source": [
- "x_train_, y_train = np.load(DATA_DIR + 'x_train.npy'), np.load(DATA_DIR + 'y_train.npy')\n",
- "x_val_, y_val = np.load(DATA_DIR + 'x_val.npy'), np.load(DATA_DIR + 'y_val.npy')\n",
- "x_test_, y_test = np.load(DATA_DIR + 'x_test.npy'), np.load(DATA_DIR + 'y_test.npy')\n",
+ "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
+ "f = h5py.File(DATA_DIR + 'train.h5','r')\n",
+ "x_train_ = np.array(f.get('X'))\n",
+ "y_train = np.array(f.get('Y'))\n",
+ "f = h5py.File(DATA_DIR + 'val.h5','r')\n",
+ "x_val_ = np.array(f.get('X'))\n",
+ "y_val = np.array(f.get('Y'))\n",
+ "f = h5py.File(DATA_DIR + 'test.h5','r')\n",
+ "x_test_ = np.array(f.get('X'))\n",
+ "y_test = np.array(f.get('Y'))\n",
"# Mean-var normalize\n",
"mean = np.mean(np.reshape(x_train_, [-1, x_train_.shape[-1]]), axis=0)\n",
"std = np.std(np.reshape(x_train_, [-1, x_train_.shape[-1]]), axis=0)\n",
@@ -161,28 +169,28 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch 0 batch 0 loss 4.295151 acc 0.031250\n",
- "Epoch 0 batch 200 loss 1.002617 acc 0.718750\n",
- "Epoch 1 batch 0 loss 0.647069 acc 0.796875\n",
- "Epoch 1 batch 200 loss 0.469229 acc 0.835938\n",
- "Epoch 2 batch 0 loss 0.388671 acc 0.882812\n",
- "Epoch 2 batch 200 loss 0.396696 acc 0.859375\n",
- "Epoch 3 batch 0 loss 0.266433 acc 0.921875\n",
- "Epoch 3 batch 200 loss 0.281694 acc 0.867188\n",
- "Epoch 4 batch 0 loss 0.302240 acc 0.906250\n",
- "Epoch 4 batch 200 loss 0.245797 acc 0.929688\n",
- "Validation accuracy: 0.911003\n",
- "Epoch 5 batch 0 loss 0.202542 acc 0.945312\n",
- "Epoch 5 batch 200 loss 0.192004 acc 0.929688\n",
- "Epoch 6 batch 0 loss 0.256735 acc 0.921875\n",
- "Epoch 6 batch 200 loss 0.279066 acc 0.921875\n",
- "Epoch 7 batch 0 loss 0.228837 acc 0.945312\n",
- "Epoch 7 batch 200 loss 0.222357 acc 0.937500\n",
- "Epoch 8 batch 0 loss 0.164639 acc 0.960938\n",
- "Epoch 8 batch 200 loss 0.160117 acc 0.945312\n",
- "Epoch 9 batch 0 loss 0.173849 acc 0.953125\n",
- "Epoch 9 batch 200 loss 0.201694 acc 0.929688\n",
- "Validation accuracy: 0.912474\n"
+ "Epoch 0 batch 0 loss 2.049031 acc 0.632812\n",
+ "Epoch 0 batch 200 loss 0.739568 acc 0.695312\n",
+ "Epoch 1 batch 0 loss 0.536956 acc 0.843750\n",
+ "Epoch 1 batch 200 loss 0.402417 acc 0.882812\n",
+ "Epoch 2 batch 0 loss 0.299402 acc 0.921875\n",
+ "Epoch 2 batch 200 loss 0.316270 acc 0.882812\n",
+ "Epoch 3 batch 0 loss 0.237716 acc 0.929688\n",
+ "Epoch 3 batch 200 loss 0.215562 acc 0.929688\n",
+ "Epoch 4 batch 0 loss 0.235044 acc 0.929688\n",
+ "Epoch 4 batch 200 loss 0.177791 acc 0.945312\n",
+ "Validation accuracy: 0.913504\n",
+ "Epoch 5 batch 0 loss 0.181037 acc 0.945312\n",
+ "Epoch 5 batch 200 loss 0.167289 acc 0.937500\n",
+ "Epoch 6 batch 0 loss 0.201628 acc 0.921875\n",
+ "Epoch 6 batch 200 loss 0.266160 acc 0.914062\n",
+ "Epoch 7 batch 0 loss 0.199887 acc 0.937500\n",
+ "Epoch 7 batch 200 loss 0.154214 acc 0.929688\n",
+ "Epoch 8 batch 0 loss 0.193560 acc 0.945312\n",
+ "Epoch 8 batch 200 loss 0.194838 acc 0.937500\n",
+ "Epoch 9 batch 0 loss 0.205967 acc 0.921875\n",
+ "Epoch 9 batch 200 loss 0.186773 acc 0.937500\n",
+ "Validation accuracy: 0.913063\n"
]
}
],
diff --git a/examples/pytorch/SRNN/SRNN_Example.py b/examples/pytorch/SRNN/SRNN_Example.py
index c30ecc589..cfa1e00c7 100644
--- a/examples/pytorch/SRNN/SRNN_Example.py
+++ b/examples/pytorch/SRNN/SRNN_Example.py
@@ -6,6 +6,7 @@
import os
import numpy as np
import torch
+import h5py
from edgeml_pytorch.graph.rnn import SRNN2
from edgeml_pytorch.trainer.srnnTrainer import SRNNTrainer
@@ -16,12 +17,15 @@
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
DATA_DIR = config.data_dir
-x_train_ = np.load(DATA_DIR + 'x_train.npy')
-y_train = np.load(DATA_DIR + 'y_train.npy')
-x_val_ = np.load(DATA_DIR + 'x_val.npy')
-y_val = np.load(DATA_DIR + 'y_val.npy')
-x_test_ = np.load(DATA_DIR + 'x_test.npy')
-y_test = np.load(DATA_DIR + 'y_test.npy')
+f = h5py.File(DATA_DIR + 'train.h5','r')
+x_train_ = np.array(f.get('X'))
+y_train = np.array(f.get('Y'))
+f = h5py.File(DATA_DIR + 'val.h5','r')
+x_val_ = np.array(f.get('X'))
+y_val = np.array(f.get('Y'))
+f = h5py.File(DATA_DIR + 'test.h5','r')
+x_test_ = np.array(f.get('X'))
+y_test = np.array(f.get('Y'))
# Mean-var normalize
mean = np.mean(np.reshape(x_train_, [-1, x_train_.shape[-1]]), axis=0)
@@ -60,7 +64,7 @@
Example OPTIONAL args for FastGRNNCell
cellArgs = {'gate_non_linearity':"sigmoid",'update_non_linearity':"tanh",
- 'wRank':None, 'uRank':None,'zetaInit':1.0, 'nuInit':-4.0,
+ 'wRank':None, 'uRank':None,'zetaInit':1.0, 'nuInit':-4.0,
'batch_first':False}
'''
@@ -68,7 +72,7 @@
srnn2 = SRNN2(numInput, numClasses, hiddenDim0, hiddenDim1, cellType,
dropoutProbability_l0, dropoutProbability_l1,
- **cellArgs).to(device)
+ **cellArgs).to(device)
trainer = SRNNTrainer(srnn2, learningRate, lossType='xentropy', device=device)
trainer.train(brickSize, batchSize, epochs, x_train, x_val, y_train, y_val,
diff --git a/examples/pytorch/SRNN/process_google.py b/examples/pytorch/SRNN/process_google.py
index afce89a33..223e82848 100644
--- a/examples/pytorch/SRNN/process_google.py
+++ b/examples/pytorch/SRNN/process_google.py
@@ -1,4 +1,3 @@
-
# Google Speech data feature extraction
# Note that the 'testing_list.txt' and 'validation_list.txt'
@@ -27,7 +26,7 @@
import numpy as np
import scipy.io.wavfile as r
import random
-
+import h5py
# Various version can be created depending on which labels are chosen and which
# are moved to the negative (noise) set. We use LABELMAP13 for most of our
@@ -131,15 +130,15 @@ def createFileList(audioFileDir, testingList,
np.save(outPrefix + 'file_test.npy', testingList)
np.save(outPrefix + 'file_val.npy', validationList)
-
-def extractFeatures(fileList, LABELMAP, maxlen, numFilt, samplerate, winlen,
- winstep):
+def extractFeatures(fileList, LABELMAP, numLabels, maxlen, numFilt, samplerate,
+ winlen, winstep, X, Y):
'''
Reads audio from files specified in fileList, extracts features and assigns
labels to them.
fileList: List of audio file names.
LABELMAP: The label map to use.
+ numLabels: No of labels
maxlen: maximum length of the audio file. Every other
files is zero padded to maxlen
numFilt: number of filters to use in MFCC
@@ -147,62 +146,42 @@ def extractFeatures(fileList, LABELMAP, maxlen, numFilt, samplerate, winlen,
assumed to be of same sample rate
winLen: winLen to use for fbank in seconds
winstep: winstep for fbank in seconds
+ X: dataset input
+ Y: dataset ground-truth
'''
- def __extractFeatures(stackedWav, numSteps, numFilt,
- samplerate, winlen, winstep):
- '''
- [number of waves, Len(wave)]
- returns [number of waves, numSteps, numFilt]
- All waves are assumed to be of fixed length
- '''
- assert stackedWav.ndim == 2, 'Should be [number of waves, len(wav)]'
- extractedList = []
+ def __extractFeatures(sample, numSteps, numFilt, samplerate, winLen,
+ winstep):
eps = 1e-10
- for sample in stackedWav:
- temp, _ = fbank(sample, samplerate=samplerate, winlen=winlen,
- winstep=winstep, nfilt=numFilt,
- winfunc=np.hamming)
- temp = np.log(temp + eps)
- assert temp.ndim == 2, 'Should be [numSteps, numFilt]'
- assert temp.shape[0] == numSteps, 'Should be [numSteps, numFilt]'
- extractedList.append(temp)
- return np.array(extractedList)
-
+ temp, _ = fbank(sample, samplerate=samplerate, winlen=winlen,
+ winstep=winstep, nfilt=numFilt, winfunc=np.hamming)
+ temp = [np.log(temp + eps)]
+ assert temp.ndim == 2, 'Should be [numSteps, numFilt]'
+ assert temp.shape[0] == numSteps, 'Should be [numSteps, numFilt]'
+ return np.array(temp)
+
fileList = np.array(fileList)
- assert(fileList.ndim == 1)
- allSamples = np.zeros((len(fileList), maxlen))
i = 0
- for i,file in enumerate(fileList):
- _, x = r.read(file)
- assert(len(x) <= maxlen)
- allSamples[i, maxlen - len(x):maxlen] += x
- i += 1
- assert allSamples.ndim == 2
winstepSamples = winstep * samplerate
winlenSamples = winlen * samplerate
- assert(winstepSamples.is_integer())
- assert(winlenSamples.is_integer())
numSteps = int(np.ceil((maxlen - winlenSamples)/winstepSamples) + 1)
- x = __extractFeatures(allSamples, numSteps, numFilt, samplerate, winlen,
- winstep)
- y_ = [t.split('/') for t in fileList]
- y_ = [t[-2] for t in y_]
- y = []
- for t in y_:
- assert t in LABELMAP
- y.append(LABELMAP[t])
-
- def to_onehot(indices, numClasses):
- assert indices.ndim == 1
- n = max(indices) + 1
- assert numClasses <= n
- b = np.zeros((len(indices), numClasses))
- b[np.arange(len(indices)), indices] = 1
- return b
- y = to_onehot(np.array(y), np.max(y) + 1)
- return x, y
+ for i, file in enumerate(fileList):
+ print('Processing', file)
+ sample = np.zeros(maxlen)
+ _, data = r.read(file)
+ sample[maxlen-len(data):maxlen] += data
+ x = __extractFeatures(sample, numSteps, numFilt, samplerate, winlen,
+ winstep)
+ X[i] = x
+ y_ = file.split('/')
+ y_ = y_[-2]
+ y = LABELMAP[y_]
+ b = np.zeros(numLabels)
+ b[y] = 1
+ Y[i] = b
+ i += 1
+ print('Total Processed Samples:', i)
-if __name__=='__main__':
+if __name__ == '__main__':
# ----------------------------------------- #
# Configuration
# ----------------------------------------- #
@@ -216,7 +195,7 @@ def to_onehot(indices, numClasses):
numLabels = 13 # 0 not assigned
samplerate=16000
# For creation of training file list, testing file list
- # and validation list.
+ # and validation list.
audioFileDir = './GoogleSpeech/Raw/'
testingList = './GoogleSpeech/Raw/testing_list.txt'
validationList = './GoogleSpeech/Raw/validation_list.txt'
@@ -249,17 +228,26 @@ def to_onehot(indices, numClasses):
trainFileList_ = [audioFileDir + x for x in trainFileList]
valFileList_ = [audioFileDir + x for x in valFileList]
testFileList_ = [audioFileDir + x for x in testFileList]
- x_test, y_test = extractFeatures(testFileList_, LABELMAP, maxlen, numFilt,
+ def generateDataset(datasetType, fileList, LABELMAP, numLabels, maxlen,
+ numFilt, samplerate, winlen, winstep):
+ winstepSamples = winstep * samplerate
+ winlenSamples = winlen * samplerate
+ assert(winstepSamples.is_integer())
+ assert(winlenSamples.is_integer())
+ numSteps = int(np.ceil((maxlen - winlenSamples)/winstepSamples) + 1)
+
+ with h5py.File(outDir+datasetType+'.h5') as f:
+ x = f.create_dataset("X", shape=(len(fileList), numSteps, numFilt),
+ dtype=np.float64)
+ y = f.create_dataset("Y", shape=(len(fileList), numLabels),
+ dtype=np.float64)
+ extractFeatures(fileList, LABELMAP, numLabels, maxlen, numFilt,
+ samplerate, winlen, winstep, x, y)
+ print(datasetType, 'dataset generated')
+
+ generateDataset('test', testFileList_, LABELMAP, numLabels, maxlen, numFilt,
samplerate, winlen, winstep)
- x_val, y_val = extractFeatures(valFileList_, LABELMAP, maxlen, numFilt,
+ generateDataset('val', valFileList_, LABELMAP, numLabels, maxlen, numFilt,
samplerate, winlen, winstep)
- x_train, y_train = extractFeatures(trainFileList_, LABELMAP, maxlen,
- numFilt, samplerate, winlen, winstep)
- np.save(outDir + 'x_train', x_train);np.save(outDir + 'y_train', y_train)
- np.save(outDir + 'x_test', x_test);np.save(outDir + 'y_test', y_test)
- np.save(outDir + 'x_val', x_val);np.save(outDir + 'y_val', y_val)
- print("Shape train", x_train.shape, y_train.shape)
- print("Shape test", x_test.shape, y_test.shape)
- print("Shape val", x_val.shape, y_val.shape)
-
-
+ generateDataset('train', trainFileList_, LABELMAP, numLabels, maxlen,
+ numFilt, samplerate, winlen, winstep)
diff --git a/examples/pytorch/requirements.txt b/examples/pytorch/requirements.txt
new file mode 100644
index 000000000..60af0cc89
--- /dev/null
+++ b/examples/pytorch/requirements.txt
@@ -0,0 +1,2 @@
+python-speech-features==0.6
+h5py==2.9.0
From 25b0210ee2b088f0df7a188fb9e6729865d6056e Mon Sep 17 00:00:00 2001
From: Pushkal Katara <katarapushkal@gmail.com>
Date: Thu, 22 Aug 2019 02:45:54 +0530
Subject: [PATCH 2/3] clean
---
examples/pytorch/SRNN/process_google.py | 8 +++-----
1 file changed, 3 insertions(+), 5 deletions(-)
diff --git a/examples/pytorch/SRNN/process_google.py b/examples/pytorch/SRNN/process_google.py
index 223e82848..1aaf8aca1 100644
--- a/examples/pytorch/SRNN/process_google.py
+++ b/examples/pytorch/SRNN/process_google.py
@@ -158,7 +158,7 @@ def __extractFeatures(sample, numSteps, numFilt, samplerate, winLen,
assert temp.ndim == 2, 'Should be [numSteps, numFilt]'
assert temp.shape[0] == numSteps, 'Should be [numSteps, numFilt]'
return np.array(temp)
-
+
fileList = np.array(fileList)
i = 0
winstepSamples = winstep * samplerate
@@ -169,11 +169,9 @@ def __extractFeatures(sample, numSteps, numFilt, samplerate, winLen,
sample = np.zeros(maxlen)
_, data = r.read(file)
sample[maxlen-len(data):maxlen] += data
- x = __extractFeatures(sample, numSteps, numFilt, samplerate, winlen,
+ X[i] = __extractFeatures(sample, numSteps, numFilt, samplerate, winlen,
winstep)
- X[i] = x
- y_ = file.split('/')
- y_ = y_[-2]
+ y_ = file.split('/')[-2]
y = LABELMAP[y_]
b = np.zeros(numLabels)
b[y] = 1
From 58b2cb559373242e699b38c00b2032fa7285df8d Mon Sep 17 00:00:00 2001
From: Pushkal Katara <katarapushkal@gmail.com>
Date: Thu, 22 Aug 2019 02:50:36 +0530
Subject: [PATCH 3/3] clean
---
examples/pytorch/SRNN/process_google.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/examples/pytorch/SRNN/process_google.py b/examples/pytorch/SRNN/process_google.py
index 1aaf8aca1..e02743291 100644
--- a/examples/pytorch/SRNN/process_google.py
+++ b/examples/pytorch/SRNN/process_google.py
@@ -154,10 +154,10 @@ def __extractFeatures(sample, numSteps, numFilt, samplerate, winLen,
eps = 1e-10
temp, _ = fbank(sample, samplerate=samplerate, winlen=winlen,
winstep=winstep, nfilt=numFilt, winfunc=np.hamming)
- temp = [np.log(temp + eps)]
+ temp = np.log(temp + eps)
assert temp.ndim == 2, 'Should be [numSteps, numFilt]'
assert temp.shape[0] == numSteps, 'Should be [numSteps, numFilt]'
- return np.array(temp)
+ return np.array([temp])
fileList = np.array(fileList)
i = 0