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gcommands_loader.py
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gcommands_loader.py
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# coding=utf-8
# Copyright 2018 jose.fonollosa@upc.edu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data generator for the Google speech commands data set using standard Kaldi data folders."""
from __future__ import print_function, division
import os.path
import subprocess
import struct
import wave
import numpy as np
from mfsc import mfsc
import torch
import torch.utils.data as data
# Words for Google Speech Commands v0.02 plus 'silence' for noise recordings
CLASSES = ['backward', 'bed', 'bird', 'cat', 'dog', 'down', 'eight', 'five', 'follow', 'forward', 'four',
'go', 'happy', 'house', 'learn', 'left', 'marvin', 'nine', 'no', 'off', 'on', 'one', 'right',
'seven', 'sheila', 'six', 'stop', 'three', 'tree', 'two', 'up', 'visual', 'wow', 'yes', 'zero',
'silence']
print("Number of labels:", len(CLASSES))
# pylint: disable=ungrouped-imports
try:
from subprocess import DEVNULL # python3
except ImportError:
DEVNULL = open(os.devnull, 'wb')
def wav_read(pipe):
if pipe[-1] == '|':
tpipe = subprocess.Popen(pipe[:-1], shell=True, stderr=DEVNULL, stdout=subprocess.PIPE)
audio = tpipe.stdout
else:
tpipe = None
audio = pipe
try:
wav = wave.open(audio, 'r')
except EOFError:
print('EOFError:', pipe)
exit(-1)
sfreq = wav.getframerate()
assert wav.getsampwidth() == 2
wav_bytes = wav.readframes(-1)
npts = len(wav_bytes) // wav.getsampwidth()
wav.close()
# convert binary chunks
wav_array = np.array(struct.unpack("%ih" % npts, wav_bytes), dtype=float) / (1 << 15)
return wav_array, sfreq
def get_classes():
classes = CLASSES
weight = None
class_to_id = {label: i for i, label in enumerate(classes)}
return classes, weight, class_to_id
def get_segment(wav, seg_ini, seg_end):
nwav = None
if float(seg_end) > float(seg_ini):
if wav[-1] == '|':
nwav = wav + ' sox -t wav - -t wav - trim {} ={} |'.format(seg_ini, seg_end)
else:
nwav = 'sox {} -t wav - trim {} ={} |'.format(wav, seg_ini, seg_end)
return nwav
def make_dataset(kaldi_path, class_to_id):
text_path = os.path.join(kaldi_path, 'text')
wav_path = os.path.join(kaldi_path, 'wav.scp')
segments_path = os.path.join(kaldi_path, 'segments')
with open(text_path, 'rt') as text:
key_to_word = dict()
for line in text:
key, word = line.strip().split(' ', 1)
key_to_word[key] = word
with open(wav_path, 'rt') as wav_scp:
key_to_wav = dict()
for line in wav_scp:
key, wav = line.strip().split(' ', 1)
key_to_wav[key] = wav
wavs = []
if os.path.isfile(segments_path):
with open(segments_path, 'rt') as segments:
for line in segments:
key, wav_key, seg_ini, seg_end = line.strip().split()
wav_command = key_to_wav[wav_key]
word = key_to_word[key]
word_id = class_to_id[word]
wav_item = [key, get_segment(wav_command, seg_ini, seg_end), word_id]
wavs.append(wav_item)
else:
for key, wav_command in key_to_wav.items():
word = key_to_word[key]
word_id = class_to_id[word]
wav_item = [key, wav_command, word_id]
wavs.append(wav_item)
return wavs
def param_loader(path, window_size, window_stride, window, normalize, max_len):
y, sfr = wav_read(path)
param = mfsc(y, sfr, window_size=window_size, window_stride=window_stride, window=window, normalize=normalize, log=False, n_mels=40, preemCoef=0, melfloor=1.0)
# Add zero padding to make all param with the same dims
if param.shape[1] < max_len:
pad = np.zeros((param.shape[0], max_len - param.shape[1]))
param = np.hstack((pad, param))
# If exceeds max_len keep last samples
elif param.shape[1] > max_len:
param = param[:, -max_len:]
param = torch.FloatTensor(param)
return param
class Loader(data.Dataset):
"""A google commands data set loader using Kaldi data format::
Args:
root (string): Kaldi directory path.
transform (callable, optional): A function/transform that takes in a spectrogram
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
window_size: window size for the stft, default value is .02
window_stride: window stride for the stft, default value is .01
window_type: typye of window to extract the stft, default value is 'hamming'
normalize: boolean, whether or not to normalize the param to have zero mean and one std
max_len: the maximum length of frames to use
Attributes:
classes (list): List of the class names.
class_to_id (dict): Dict with items (class_name, class_index).
wavs (list): List of (wavs path, class_index) tuples
STFT parameters: window_size, window_stride, window_type, normalize
"""
def __init__(self, root, transform=None, target_transform=None, window_size=.02,
window_stride=.01, window_type='hamming', normalize=True, max_len=99):
classes, weight, class_to_id = get_classes()
wavs = make_dataset(root, class_to_id)
if not wavs:
raise RuntimeError("Found 0 segments in '" + root + "'. Folder should be in standard Kaldi format") # pylint: disable=line-too-long
self.root = root
self.wavs = wavs
self.classes = classes
self.weight = torch.FloatTensor(weight) if weight is not None else None
self.class_to_idx = class_to_id
self.transform = transform
self.target_transform = target_transform
self.loader = param_loader
self.window_size = window_size
self.window_stride = window_stride
self.window_type = window_type
self.normalize = normalize
self.max_len = max_len
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (params, target) where target is class_index of the target class.
"""
key, path, target = self.wavs[index]
params = self.loader(path, self.window_size, self.window_stride, self.window_type, self.normalize, self.max_len) # pylint: disable=line-too-long
if self.transform is not None:
params = self.transform(params)
if self.target_transform is not None:
target = self.target_transform(target)
return key, params, target
def __len__(self):
return len(self.wavs)