def test_scale(self):
audio_orig = self.sig.clone()
result = transforms.Scale()(audio_orig)
self.assertTrue(result.min() >= -1. and result.max() <= 1.)
maxminmax = float(max(abs(audio_orig.min()), abs(audio_orig.max())))
result = transforms.Scale(factor=maxminmax)(audio_orig)
self.assertTrue((result.min() == -1. or result.max() == 1.)
and result.min() >= -1. and result.max() <= 1.)
repr_test = transforms.Scale()
self.assertTrue(repr_test.__repr__())
def test_scale(self):
audio_orig = self.sig.clone()
result = transforms.Scale()(audio_orig)
self.assertTrue(
result.min() >= -1. and result.max() <= 1.,
print("min: {}, max: {}".format(result.min(), result.max())))
maxminmax = np.abs([audio_orig.min(),
audio_orig.max()]).max().astype(np.float)
result = transforms.Scale(factor=maxminmax)(audio_orig)
self.assertTrue(
(result.min() == -1. or result.max() == 1.) and result.min() >= -1.
and result.max() <= 1.,
print("min: {}, max: {}".format(result.min(), result.max())))
def test_mel2(self):
audio_orig = self.sig.clone() # (16000, 1)
audio_scaled = transforms.Scale()(audio_orig) # (16000, 1)
audio_scaled = transforms.LC2CL()(audio_scaled) # (1, 16000)
spectrogram_torch = transforms.MEL2()(audio_scaled) # (1, 319, 40)
self.assertTrue(spectrogram_torch.dim() == 3)
self.assertTrue(spectrogram_torch.max() <= 0.)
def test_mel(self):
audio = self.sig.clone()
audio = transforms.Scale()(audio)
self.assertTrue(len(audio.size()) == 2)
result = transforms.MEL()(audio)
self.assertTrue(len(result.size()) == 3)
result = transforms.BLC2CBL()(result)
self.assertTrue(len(result.size()) == 3)
def get_dataloader(self):
vx = VOXFORGE(args.data_path,
langs=args.languages,
label_type="lang",
use_cache=args.use_cache,
use_precompute=args.use_precompute)
if self.model_name == "resnet34_conv" or self.model_name == "resnet101_conv":
T = tat.Compose([
#tat.PadTrim(self.max_len),
tat.MEL(n_mels=224),
tat.BLC2CBL(),
tvt.ToPILImage(),
tvt.Resize((224, 224)),
tvt.ToTensor(),
])
TT = spl_transforms.LENC(vx.LABELS)
elif self.model_name == "resnet34_mfcc":
sr = 16000
ws = 800
hs = ws // 2
n_fft = 512 # 256
n_filterbanks = 26
n_coefficients = 12
low_mel_freq = 0
high_freq_mel = (2595 * math.log10(1 + (sr / 2) / 700))
mel_pts = torch.linspace(low_mel_freq, high_freq_mel,
n_filterbanks + 2) # sr = 16000
hz_pts = torch.floor(700 * (torch.pow(10, mel_pts / 2595) - 1))
bins = torch.floor((n_fft + 1) * hz_pts / sr)
td = {
"RfftPow": spl_transforms.RfftPow(n_fft),
"FilterBanks": spl_transforms.FilterBanks(n_filterbanks, bins),
"MFCC": spl_transforms.MFCC(n_filterbanks, n_coefficients),
}
T = tat.Compose([
tat.Scale(),
#tat.PadTrim(self.max_len, fill_value=1e-8),
spl_transforms.Preemphasis(),
spl_transforms.Sig2Features(ws, hs, td),
spl_transforms.DummyDim(),
tat.BLC2CBL(),
tvt.ToPILImage(),
tvt.Resize((224, 224)),
tvt.ToTensor(),
])
TT = spl_transforms.LENC(vx.LABELS)
vx.transform = T
vx.target_transform = TT
if args.use_precompute:
vx.load_precompute(args.model_name)
dl = data.DataLoader(vx,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=True)
return vx, dl
def load_dataset(dataset='VCTK', train_subset=1.0, person_filter=None):
transfs = transforms.Compose([
transforms.Scale(),
prepro.DB_Spec(n_fft=400, hop_t=0.010, win_t=0.025)
])
if dataset == 'VCTK':
person_filter = [
'p249', 'p239', 'p276', 'p283', 'p243', 'p254', 'p258', 'p271'
]
train_dataset = vctk_custom_dataset.VCTK('../datasets/VCTK-Corpus/',
preprocessed=True,
person_filter=person_filter,
filter_mode='exclude')
test_dataset = vctk_custom_dataset.VCTK('../datasets/VCTK-Corpus/',
preprocessed=True,
person_filter=person_filter,
filter_mode='include')
elif dataset == 'LibriSpeech':
train_dataset = librispeech_custom_dataset.LibriSpeech(
'../datasets/LibriSpeech/',
preprocessed=True,
split='train',
person_filter=person_filter,
filter_mode='include')
test_dataset = librispeech_custom_dataset.LibriSpeech(
'../datasets/LibriSpeech/',
preprocessed=True,
split='test',
person_filter=person_filter,
filter_mode='include')
indices = list(range(len(train_dataset)))
split = int(np.floor(len(train_dataset) * train_subset))
train_sampler = sampler.RandomSampler(
sampler.SubsetRandomSampler(indices[:split]))
test_sampler = sampler.RandomSampler(test_dataset)
kwargs = {'num_workers': 8, 'pin_memory': True} if args.use_cuda else {}
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
drop_last=False,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
sampler=test_sampler,
drop_last=False,
**kwargs)
return train_loader, test_loader, train_dataset, test_dataset
def test_mel2(self):
top_db = 80.
s2db = transforms.SpectrogramToDB("power", top_db)
audio_orig = self.sig.clone() # (16000, 1)
audio_scaled = transforms.Scale()(audio_orig) # (16000, 1)
audio_scaled = transforms.LC2CL()(audio_scaled) # (1, 16000)
mel_transform = transforms.MelSpectrogram()
# check defaults
spectrogram_torch = s2db(mel_transform(audio_scaled)) # (1, 319, 40)
self.assertTrue(spectrogram_torch.dim() == 3)
self.assertTrue(
spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram_torch.size(-1), mel_transform.n_mels)
# check correctness of filterbank conversion matrix
self.assertTrue(mel_transform.fm.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform.fm.fb.sum(1).ge(0.).all())
# check options
kwargs = {
"window": torch.hamming_window,
"pad": 10,
"ws": 500,
"hop": 125,
"n_fft": 800,
"n_mels": 50
}
mel_transform2 = transforms.MelSpectrogram(**kwargs)
spectrogram2_torch = s2db(mel_transform2(audio_scaled)) # (1, 506, 50)
self.assertTrue(spectrogram2_torch.dim() == 3)
self.assertTrue(
spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram2_torch.size(-1), mel_transform2.n_mels)
self.assertTrue(mel_transform2.fm.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform2.fm.fb.sum(1).ge(0.).all())
# check on multi-channel audio
x_stereo, sr_stereo = torchaudio.load(self.test_filepath)
spectrogram_stereo = s2db(mel_transform(x_stereo))
self.assertTrue(spectrogram_stereo.dim() == 3)
self.assertTrue(spectrogram_stereo.size(0) == 2)
self.assertTrue(
spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram_stereo.size(-1), mel_transform.n_mels)
# check filterbank matrix creation
fb_matrix_transform = transforms.MelScale(n_mels=100,
sr=16000,
f_max=None,
f_min=0.,
n_stft=400)
self.assertTrue(fb_matrix_transform.fb.sum(1).le(1.).all())
self.assertTrue(fb_matrix_transform.fb.sum(1).ge(0.).all())
self.assertEqual(fb_matrix_transform.fb.size(), (400, 100))
def test_mel(self):
audio = self.sig.clone()
audio = transforms.Scale()(audio)
self.assertTrue(audio.dim() == 2)
result = transforms.MEL()(audio)
self.assertTrue(result.dim() == 3)
result = transforms.BLC2CBL()(result)
self.assertTrue(result.dim() == 3)
repr_test = transforms.MEL()
repr_test.__repr__()
repr_test = transforms.BLC2CBL()
repr_test.__repr__()
def test_compose(self):
audio_orig = self.sig.clone()
length_orig = audio_orig.size(0)
length_new = int(length_orig * 1.2)
maxminmax = np.abs([audio_orig.min(),
audio_orig.max()]).max().astype(np.float)
tset = (transforms.Scale(factor=maxminmax),
transforms.PadTrim(max_len=length_new))
result = transforms.Compose(tset)(audio_orig)
self.assertTrue(np.abs([result.min(), result.max()]).max() == 1.)
self.assertTrue(result.size(0) == length_new)
def test_compose(self):
audio_orig = self.sig.clone()
length_orig = audio_orig.size(0)
length_new = int(length_orig * 1.2)
maxminmax = max(abs(audio_orig.min()), abs(audio_orig.max())).item()
tset = (transforms.Scale(factor=maxminmax),
transforms.PadTrim(max_len=length_new, channels_first=False))
result = transforms.Compose(tset)(audio_orig)
self.assertTrue(max(abs(result.min()), abs(result.max())) == 1.)
self.assertTrue(result.size(0) == length_new)
repr_test = transforms.Compose(tset)
self.assertTrue(repr_test.__repr__())
def test_mel2(self):
audio_orig = self.sig.clone() # (16000, 1)
audio_scaled = transforms.Scale()(audio_orig) # (16000, 1)
audio_scaled = transforms.LC2CL()(audio_scaled) # (1, 16000)
mel_transform = transforms.MEL2()
# check defaults
spectrogram_torch = mel_transform(audio_scaled) # (1, 319, 40)
self.assertTrue(spectrogram_torch.dim() == 3)
self.assertTrue(spectrogram_torch.le(0.).all())
self.assertTrue(spectrogram_torch.ge(mel_transform.top_db).all())
self.assertEqual(spectrogram_torch.size(-1), mel_transform.n_mels)
# check correctness of filterbank conversion matrix
self.assertTrue(mel_transform.fm.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform.fm.fb.sum(1).ge(0.).all())
# check options
mel_transform2 = transforms.MEL2(window=torch.hamming_window,
pad=10,
ws=500,
hop=125,
n_fft=800,
n_mels=50)
spectrogram2_torch = mel_transform2(audio_scaled) # (1, 506, 50)
self.assertTrue(spectrogram2_torch.dim() == 3)
self.assertTrue(spectrogram2_torch.le(0.).all())
self.assertTrue(spectrogram2_torch.ge(mel_transform.top_db).all())
self.assertEqual(spectrogram2_torch.size(-1), mel_transform2.n_mels)
self.assertTrue(mel_transform2.fm.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform2.fm.fb.sum(1).ge(0.).all())
# check on multi-channel audio
x_stereo, sr_stereo = torchaudio.load(self.test_filepath)
spectrogram_stereo = mel_transform(x_stereo)
self.assertTrue(spectrogram_stereo.dim() == 3)
self.assertTrue(spectrogram_stereo.size(0) == 2)
self.assertTrue(spectrogram_stereo.le(0.).all())
self.assertTrue(spectrogram_stereo.ge(mel_transform.top_db).all())
self.assertEqual(spectrogram_stereo.size(-1), mel_transform.n_mels)
# check filterbank matrix creation
fb_matrix_transform = transforms.F2M(n_mels=100,
sr=16000,
f_max=None,
f_min=0.,
n_stft=400)
self.assertTrue(fb_matrix_transform.fb.sum(1).le(1.).all())
self.assertTrue(fb_matrix_transform.fb.sum(1).ge(0.).all())
self.assertEqual(fb_matrix_transform.fb.size(), (400, 100))
def test_mel2(self):
audio_orig = self.sig.clone() # (16000, 1)
audio_scaled = transforms.Scale()(audio_orig) # (16000, 1)
audio_scaled = transforms.LC2CL()(audio_scaled) # (1, 16000)
mel_transform = transforms.MEL2(window=torch.hamming_window, pad=10)
spectrogram_torch = mel_transform(audio_scaled) # (1, 319, 40)
self.assertTrue(spectrogram_torch.dim() == 3)
self.assertTrue(spectrogram_torch.le(0.).all())
self.assertTrue(spectrogram_torch.ge(mel_transform.top_db).all())
self.assertEqual(spectrogram_torch.size(-1), mel_transform.n_mels)
# load stereo file
x_stereo, sr_stereo = torchaudio.load(self.test_filepath)
spectrogram_stereo = mel_transform(x_stereo)
self.assertTrue(spectrogram_stereo.dim() == 3)
self.assertTrue(spectrogram_stereo.size(0) == 2)
self.assertTrue(spectrogram_stereo.le(0.).all())
self.assertTrue(spectrogram_stereo.ge(mel_transform.top_db).all())
self.assertEqual(spectrogram_stereo.size(-1), mel_transform.n_mels)
def test_mfcc(self):
audio_orig = self.sig.clone()
audio_scaled = transforms.Scale()(audio_orig) # (16000, 1)
audio_scaled = transforms.LC2CL()(audio_scaled) # (1, 16000)
sample_rate = 16000
n_mfcc = 40
n_mels = 128
mfcc_transform = torchaudio.transforms.MFCC(sr=sample_rate,
n_mfcc=n_mfcc,
norm='ortho')
# check defaults
torch_mfcc = mfcc_transform(audio_scaled)
self.assertTrue(torch_mfcc.dim() == 3)
self.assertTrue(torch_mfcc.shape[2] == n_mfcc)
self.assertTrue(torch_mfcc.shape[1] == 321)
# check melkwargs are passed through
melkwargs = {'ws': 200}
mfcc_transform2 = torchaudio.transforms.MFCC(sr=sample_rate,
n_mfcc=n_mfcc,
norm='ortho',
melkwargs=melkwargs)
torch_mfcc2 = mfcc_transform2(audio_scaled)
self.assertTrue(torch_mfcc2.shape[1] == 641)
# check norms work correctly
mfcc_transform_norm_none = torchaudio.transforms.MFCC(sr=sample_rate,
n_mfcc=n_mfcc,
norm=None)
torch_mfcc_norm_none = mfcc_transform_norm_none(audio_scaled)
norm_check = torch_mfcc.clone()
norm_check[:, :, 0] *= math.sqrt(n_mels) * 2
norm_check[:, :, 1:] *= math.sqrt(n_mels / 2) * 2
self.assertTrue(torch_mfcc_norm_none.allclose(norm_check))
# import math
## Setting seed
import random
param.seed = param.seed or random.randint(1, 10000)
print("Random Seed: " + str(param.seed))
print("Random Seed: " + str(param.seed), file=log_output)
random.seed(param.seed)
torch.manual_seed(param.seed)
if param.cuda:
torch.cuda.manual_seed_all(param.seed)
## Transforming audio files
trans = transf.Compose([
transf.Scale(), # This makes it into [-1,1]
# transf.ToTensor(),
transf.PadTrim(max_len=param.audio_size), # I don't know if this is needed
# This makes it into [-1,1] so tanh will work properly
# transf.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
def load_sound(path):
tensor_to_load_into = None
import torchaudio
out, sample_rate = torchaudio.load(path, tensor_to_load_into)
return out
## Importing dataset
seq_M = args.seq_M
batch_size = args.batch_size
depth = args.depth
radixs = [2] * depth
N = np.prod(radixs)
channels = args.channels
lr = args.lr
steps = args.steps
c = args.c
generation_time = args.file_size
filename = args.outfile
maxlen = 50000
print('==> Downloading YesNo Dataset..')
transform = transforms.Compose(
[transforms.Scale(),
transforms.PadTrim(maxlen),
transforms.MuLawEncoding(quantization_channels=channels)])
data = torchaudio.datasets.YESNO('./data', download=True, transform=transform)
data_loader = DataLoader(data, batch_size=batch_size, num_workers=4, shuffle=True)
print('==> Building model..')
net = general_FFTNet(radixs, 128, channels).cuda()
print(sum(p.numel() for p in net.parameters() if p.requires_grad), "of parameters.")
optimizer = optim.Adam(net.parameters(), lr=lr)
criterion = torch.nn.CrossEntropyLoss()
print("Start Training.")
a = datetime.now().replace(microsecond=0)
seq_M = args.seq_M
batch_size = args.batch_size
depth = args.depth
radixs = [2] * depth
N = np.prod(radixs)
channels = args.channels
lr = args.lr
steps = args.steps
c = args.c
generation_time = args.file_size
filename = args.outfile
features_size = args.feature_size
print('==> Downloading YesNo Dataset..')
transform = transforms.Compose([transforms.Scale()])
data = torchaudio.datasets.YESNO('./data',
download=True,
transform=transform)
data_loader = DataLoader(data, batch_size=1, num_workers=2)
print('==> Extracting features..')
train_wav = []
train_features = []
train_targets = []
for batch_idx, (inputs, _) in enumerate(data_loader):
inputs = inputs.view(-1).numpy()
targets = np.roll(inputs, shift=-1)
#h = mfcc(inputs, sr, winlen=winlen, winstep=winstep, numcep=features_size - 1, winfunc=np.hamming)
x = inputs.astype(float)
import torch
import torchaudio.datasets as dset
from torchaudio import transforms
transform = transforms.Compose(
[transforms.Scale(), transforms.PadTrim(100000)])
train_dataset = dset.YESNO("data", transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=10,
)
for i, (input, target) in enumerate(train_loader):
import ipdb
ipdb.set_trace(context=21)
print("HI")
""" Vision MNIST test"""
"""
import torchvision.datasets as vdset
from torchvision import transforms as vtransforms
transform = vtransforms.Compose([
vtransforms.ToTensor()
])
mnist = vdset.MNIST("data", transform=transform, download=True)
mnist_loader = torch.utils.data.DataLoader(
mnist,
batch_size=10,
def main():
# Init logger
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
log = open(os.path.join(args.save_path, 'log_seed_{}.txt'.format(args.manualSeed)), 'w')
print_log('save path : {}'.format(args.save_path), log)
state = {k: v for k, v in args._get_kwargs()}
print_log(state, log)
print_log("Random Seed: {}".format(args.manualSeed), log)
print_log("python version : {}".format(sys.version.replace('\n', ' ')), log)
print_log("torch version : {}".format(torch.__version__), log)
print_log("cudnn version : {}".format(torch.backends.cudnn.version()), log)
# Data loading code
# Any other preprocessings? http://pytorch.org/audio/transforms.html
sample_length = 10000
scale = transforms.Scale()
padtrim = transforms.PadTrim(sample_length)
downmix = transforms.DownmixMono()
transforms_audio = transforms.Compose([
scale, padtrim, downmix
])
if not os.path.isdir(args.data_path):
os.makedirs(args.data_path)
train_dir = os.path.join(args.data_path, 'train')
val_dir = os.path.join(args.data_path, 'val')
#Choose dataset to use
if args.dataset == 'arctic':
# TODO No ImageFolder equivalent for audio. Need to create a Dataset manually
train_dataset = Arctic(train_dir, transform=transforms_audio, download=True)
val_dataset = Arctic(val_dir, transform=transforms_audio, download=True)
num_classes = 4
elif args.dataset == 'vctk':
train_dataset = dset.VCTK(train_dir, transform=transforms_audio, download=True)
val_dataset = dset.VCTK(val_dir, transform=transforms_audio, download=True)
num_classes = 10
elif args.dataset == 'yesno':
train_dataset = dset.YESNO(train_dir, transform=transforms_audio, download=True)
val_dataset = dset.YESNO(val_dir, transform=transforms_audio, download=True)
num_classes = 2
else:
assert False, 'Dataset is incorrect'
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
# pin_memory=True, # What is this?
# sampler=None # What is this?
)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
#Feed in respective model file to pass into model (alexnet.py)
print_log("=> creating model '{}'".format(args.arch), log)
# Init model, criterion, and optimizer
# net = models.__dict__[args.arch](num_classes)
net = AlexNet(num_classes)
#
print_log("=> network :\n {}".format(net), log)
# net = torch.nn.DataParallel(net, device_ids=list(range(args.ngpu)))
# define loss function (criterion) and optimizer
criterion = torch.nn.CrossEntropyLoss()
# Define stochastic gradient descent as optimizer (run backprop on random small batch)
optimizer = torch.optim.SGD(net.parameters(), state['learning_rate'], momentum=state['momentum'],
weight_decay=state['decay'], nesterov=True)
#Sets use for GPU if available
if args.use_cuda:
net.cuda()
criterion.cuda()
recorder = RecorderMeter(args.epochs)
# optionally resume from a checkpoint
# Need same python vresion that the resume was in
if args.resume:
if os.path.isfile(args.resume):
print_log("=> loading checkpoint '{}'".format(args.resume), log)
if args.ngpu == 0:
checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage)
else:
checkpoint = torch.load(args.resume)
recorder = checkpoint['recorder']
args.start_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print_log("=> loaded checkpoint '{}' (epoch {})" .format(args.resume, checkpoint['epoch']), log)
else:
print_log("=> no checkpoint found at '{}'".format(args.resume), log)
else:
print_log("=> do not use any checkpoint for {} model".format(args.arch), log)
if args.evaluate:
validate(val_loader, net, criterion, 0, log, val_dataset)
return
# Main loop
start_time = time.time()
epoch_time = AverageMeter()
# Training occurs here
for epoch in range(args.start_epoch, args.epochs):
current_learning_rate = adjust_learning_rate(optimizer, epoch, args.gammas, args.schedule)
need_hour, need_mins, need_secs = convert_secs2time(epoch_time.avg * (args.epochs-epoch))
need_time = '[Need: {:02d}:{:02d}:{:02d}]'.format(need_hour, need_mins, need_secs)
print_log('\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} [learning_rate={:6.4f}]'.format(time_string(), epoch, args.epochs, need_time, current_learning_rate) \
+ ' [Best : Accuracy={:.2f}, Error={:.2f}]'.format(recorder.max_accuracy(False), 100-recorder.max_accuracy(False)), log)
print("One epoch")
# train for one epoch
# Call to train (note that our previous net is passed into the model argument)
train_acc, train_los = train(train_loader, net, criterion, optimizer, epoch, log, train_dataset)
# evaluate on validation set
#val_acc, val_los = extract_features(test_loader, net, criterion, log)
val_acc, val_los = validate(val_loader, net, criterion, epoch, log, val_dataset)
is_best = recorder.update(epoch, train_los, train_acc, val_los, val_acc)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': net.state_dict(),
'recorder': recorder,
'optimizer' : optimizer.state_dict(),
}, is_best, args.save_path, 'checkpoint.pth.tar')
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
recorder.plot_curve( os.path.join(args.save_path, 'curve.png') )
log.close()
def evaluate():
num_classes = 4
# Init logger
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
log = open(os.path.join(args.save_path, 'log_seed_{}.txt'.format(args.manualSeed)), 'w')
print_log('save path : {}'.format(args.save_path), log)
state = {k: v for k, v in args._get_kwargs()}
print_log(state, log)
print_log("Random Seed: {}".format(args.manualSeed), log)
print_log("python version : {}".format(sys.version.replace('\n', ' ')), log)
print_log("torch version : {}".format(torch.__version__), log)
print_log("cudnn version : {}".format(torch.backends.cudnn.version()), log)
# Any other preprocessings? http://pytorch.org/audio/transforms.html
sample_length = 10000
scale = transforms.Scale()
padtrim = transforms.PadTrim(sample_length)
transforms_audio = transforms.Compose([
scale, padtrim
])
# Data loading
fs, data = wavfile.read(args.file_name)
data = torch.from_numpy(data).float()
data = data.unsqueeze(1)
audio = transforms_audio(data)
audio = Variable(audio)
audio = audio.view(1, -1)
audio = audio.unsqueeze(0)
#Feed in respective model file to pass into model (alexnet.py)
print_log("=> creating model '{}'".format(args.arch), log)
# Init model, criterion, and optimizer
# net = models.__dict__[args.arch](num_classes)
net = AlexNet(num_classes)
print_log("=> network :\n {}".format(net), log)
#Sets use for GPU if available
if args.use_cuda:
net.cuda()
# optionally resume from a checkpoint
# Need same python version that the resume was in
if args.resume:
if os.path.isfile(args.resume):
print_log("=> loading checkpoint '{}'".format(args.resume), log)
if args.ngpu == 0:
checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage)
else:
checkpoint = torch.load(args.resume)
recorder = checkpoint['recorder']
args.start_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['state_dict'])
print_log("=> loaded checkpoint '{}' (epoch {})" .format(args.resume, checkpoint['epoch']), log)
else:
print_log("=> no checkpoint found at '{}'".format(args.resume), log)
else:
print_log("=> do not use any checkpoint for {} model".format(args.arch), log)
net.eval()
if args.use_cuda:
audio = audio.cuda()
output = net(audio)
print(output)
# TODO postprocess output to a string representing the person speaking
# ouptut = val_dataset.postprocess_target(output)
return
n_coefficients = 12
low_mel_freq = 0
high_freq_mel = (2595 * np.log10(1 + (sr / 2) / 700))
mel_pts = np.linspace(low_mel_freq, high_freq_mel, n_filterbanks + 2)
hz_pts = np.floor(700 * (10**(mel_pts / 2595) - 1))
bins = np.floor((n_fft + 1) * hz_pts / sr)
# data transformations
td = {
"RfftPow": RfftPow(n_fft),
"FilterBanks": FilterBanks(n_filterbanks, bins),
"MFCC": MFCC(n_filterbanks, n_coefficients),
}
transforms = tat.Compose([
tat.Scale(),
tat.PadTrim(58000, fill_value=1e-8),
Preemphasis(),
Sig2Features(ws, hs, td),
])
# set network parameters
use_cuda = torch.cuda.is_available()
batch_size = args.batch_size
input_features = 26
hidden_size = 100
output_size = 3
#output_length = (8 + 7 + 2) # with "blanks"
output_length = 8 # without blanks
n_layers = 1
attn_modus = "dot"
