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Repair of ML Lib -> Transformations back to np from torch

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This commit is contained in:
Si11ium 2020-12-17 11:00:42 +01:00
parent 62d9eb6e8f
commit 93103aba01
3 changed files with 81 additions and 85 deletions
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68
audio_toolset/audio_to_mel_dataset.py
View File

@ -93,71 +93,3 @@ class LibrosaAudioToMelDataset(_AudioToMelDataset):
pass
return self.mel_file_path.exists()
import torchaudio
if sys.platform =='windows':
torchaudio.set_audio_backend('soundfile')
else:
torchaudio.set_audio_backend('sox_io')
class PyTorchAudioToMelDataset(_AudioToMelDataset):
@property
def audio_file_duration(self):
info_obj = torchaudio.info(self.audio_path)
return info_obj.num_frames / info_obj.sample_rate
@property
def sampling_rate(self):
return self.mel_kwargs['sample_rate']
def __init__(self, audio_file_path, *args, **kwargs):
super(PyTorchAudioToMelDataset, self).__init__(audio_file_path, *args, **kwargs)
audio_file_path = Path(audio_file_path)
# audio_file, sampling_rate = librosa.load(self.audio_path, sr=sampling_rate)
from torchaudio.transforms import MelSpectrogram
self._mel_transform = Compose([MelSpectrogram(**self.mel_kwargs),
MelToImage()
])
def _build_mel(self):
if self.reset:
self.mel_file_path.unlink(missing_ok=True)
if not self.mel_file_path.exists():
self.mel_file_path.parent.mkdir(parents=True, exist_ok=True)
lock_file = Path(str(self.mel_file_path).replace(self.mel_file_path.suffix, '.lock'))
lock_file.touch(exist_ok=False)
try:
audio_sample, sample_rate = torchaudio.load(self.audio_path)
except RuntimeError:
import soundfile
data, samplerate = soundfile.read(self.audio_path)
# sf.available_formats()
# sf.available_subtypes()
soundfile.write(self.audio_path, data, samplerate, subtype='PCM_32')
audio_sample, sample_rate = torchaudio.load(self.audio_path)
if sample_rate != self.sampling_rate:
resample = torchaudio.transforms.Resample(orig_freq=int(sample_rate), new_freq=int(self.sampling_rate))
audio_sample = resample(audio_sample)
if audio_sample.shape[0] > 1:
# Transform Stereo to Mono
audio_sample = audio_sample.mean(dim=0, keepdim=True)
mel_sample = self._mel_transform(audio_sample)
with self.mel_file_path.open('wb') as mel_file:
pickle.dump(mel_sample, mel_file, protocol=pickle.HIGHEST_PROTOCOL)
lock_file.unlink()
else:
# print(f"Already existed.. Skipping {filename}")
# mel_file = mel_file
pass
# with mel_file.open(mode='rb') as f:
# mel_sample = pickle.load(f, fix_imports=True)
return self.mel_file_path.exists()

31
audio_toolset/mel_augmentation.py
View File

@ -1,4 +1,3 @@
import torch
import numpy as np
from ml_lib.utils.transforms import _BaseTransformation
@ -13,10 +12,12 @@ class NoiseInjection(_BaseTransformation):
self.sigma = sigma
self.noise_factor = noise_factor
def __call__(self, x):
def __call__(self, x: np.ndarray):
if self.noise_factor:
noise = torch.normal(self.mu, self.sigma, size=x.shape, device=x.device) * self.noise_factor
noise = np.random.normal(self.mu, self.sigma, size=x.shape) * self.noise_factor
augmented_data = x + x * noise
# Cast back to same data type
augmented_data = augmented_data.astype(x.dtype)
return augmented_data
else:
return x
@ -32,7 +33,9 @@ class LoudnessManipulator(_BaseTransformation):
def __call__(self, x):
if self.max_factor:
augmented_data = x + x * (torch.rand(1, device=x.device) * self.max_factor)
augmented_data = x + x * (np.random.random() * self.max_factor)
# Cast back to same data type
augmented_data = augmented_data.astype(x.dtype)
return augmented_data
else:
return x
@ -49,18 +52,17 @@ class ShiftTime(_BaseTransformation):
self.max_shift_ratio = max_shift_ratio
self.shift_direction = shift_direction.lower()
def __call__(self, x):
def __call__(self, x: np.ndarray):
if self.max_shift_ratio:
shift = torch.randint(max(int(self.max_shift_ratio * x.shape[-1]), 1), (1,)).item()
shift = np.random.randint(max(int(self.max_shift_ratio * x.shape[-1]), 1))
if self.shift_direction == 'right':
shift = -1 * shift
elif self.shift_direction == 'any':
# The ugly pytorch alternative
# direction = [-1, 1][torch.multinomial(torch.as_tensor([1, 2]).float(), 1).item()]
direction = np.asscalar(np.random.choice([1, -1], 1))
shift = direction * shift
augmented_data = torch.roll(x, shift, dims=-1)
augmented_data = np.roll(x, shift)
# Set to silence for heading/ tailing
shift = int(shift)
if shift > 0:
augmented_data[:shift, :] = 0
else:
@ -89,20 +91,15 @@ class MaskAug(_BaseTransformation):
else (duration_ratio_max, duration_ratio_max)
def __call__(self, x):
assert x.ndim == 3, "This function was made to wotk with two-dimensional inputs"
for dim in (self.w_idx, self.h_idx):
if self.duration_ratio_max[dim]:
if dim == self.w_idx and x.shape[dim] == 0:
print(x)
start = np.asscalar(np.random.choice(x.shape[dim], 1))
v_max = int(x.shape[dim] * self.duration_ratio_max[dim])
size = torch.randint(0, v_max, (1,)).item()
size = np.asscalar(np.random.randint(0, v_max, 1))
end = int(min(start + size, x.shape[dim]))
size = end - start
if dim == self.w_idx:
mask = torch.randn(size=(x.shape[self.h_idx], size), device=x.device) if self.mask_with_noise else 0
x[:, :, start:end] = mask
x[:, start:end] = np.random.random((x.shape[self.h_idx], size)) if self.mask_with_noise else 0
else:
mask = torch.randn((size, x.shape[self.w_idx]), device=x.device) if self.mask_with_noise else 0
x[:, start:end, :] = mask
x[start:end, :] = np.random.random((size, x.shape[self.w_idx])) if self.mask_with_noise else 0
return x

67
experiments.py Normal file
View File

@ -0,0 +1,67 @@
import torchaudio
if sys.platform =='windows':
torchaudio.set_audio_backend('soundfile')
else:
torchaudio.set_audio_backend('sox_io')
class PyTorchAudioToMelDataset(_AudioToMelDataset):
@property
def audio_file_duration(self):
info_obj = torchaudio.info(self.audio_path)
return info_obj.num_frames / info_obj.sample_rate
@property
def sampling_rate(self):
return self.mel_kwargs['sample_rate']
def __init__(self, audio_file_path, *args, **kwargs):
super(PyTorchAudioToMelDataset, self).__init__(audio_file_path, *args, **kwargs)
audio_file_path = Path(audio_file_path)
# audio_file, sampling_rate = librosa.load(self.audio_path, sr=sampling_rate)
from torchaudio.transforms import MelSpectrogram
self._mel_transform = Compose([MelSpectrogram(**self.mel_kwargs),
MelToImage()
])
def _build_mel(self):
if self.reset:
self.mel_file_path.unlink(missing_ok=True)
if not self.mel_file_path.exists():
self.mel_file_path.parent.mkdir(parents=True, exist_ok=True)
lock_file = Path(str(self.mel_file_path).replace(self.mel_file_path.suffix, '.lock'))
lock_file.touch(exist_ok=False)
try:
audio_sample, sample_rate = torchaudio.load(self.audio_path)
except RuntimeError:
import soundfile
data, samplerate = soundfile.read(self.audio_path)
# sf.available_formats()
# sf.available_subtypes()
soundfile.write(self.audio_path, data, samplerate, subtype='PCM_32')
audio_sample, sample_rate = torchaudio.load(self.audio_path)
if sample_rate != self.sampling_rate:
resample = torchaudio.transforms.Resample(orig_freq=int(sample_rate), new_freq=int(self.sampling_rate))
audio_sample = resample(audio_sample)
if audio_sample.shape[0] > 1:
# Transform Stereo to Mono
audio_sample = audio_sample.mean(dim=0, keepdim=True)
mel_sample = self._mel_transform(audio_sample)
with self.mel_file_path.open('wb') as mel_file:
pickle.dump(mel_sample, mel_file, protocol=pickle.HIGHEST_PROTOCOL)
lock_file.unlink()
else:
# print(f"Already existed.. Skipping {filename}")
# mel_file = mel_file
pass
# with mel_file.open(mode='rb') as f:
# mel_sample = pickle.load(f, fix_imports=True)
return self.mel_file_path.exists()