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stride waveform

This commit is contained in:
shahules786 2022-10-18 15:23:07 +05:30
parent 415ed8e3d0
commit edb7f020f7
1 changed files with 35 additions and 9 deletions
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44
enhancer/data/dataset.py
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@ -132,11 +132,14 @@ class TaskDataset(pl.LightningDataModule):
for item in data: for item in data:
clean, noisy, total_dur = item.values() clean, noisy, total_dur = item.values()
if total_dur < self.duration: if total_dur < self.duration:
continue metadata.append(({"clean": clean, "noisy": noisy}, 0.0))
num_segments = round(total_dur / self.duration) else:
for index in range(num_segments): num_segments = round(total_dur / self.duration)
start_time = index * self.duration for index in range(num_segments):
metadata.append(({"clean": clean, "noisy": noisy}, start_time)) start_time = index * self.duration
metadata.append(
({"clean": clean, "noisy": noisy}, start_time)
)
return metadata return metadata
def train_dataloader(self): def train_dataloader(self):
@ -195,6 +198,7 @@ class EnhancerDataset(TaskDataset):
files: Files, files: Files,
valid_minutes=5.0, valid_minutes=5.0,
duration=1.0, duration=1.0,
stride=0.5,
sampling_rate=48000, sampling_rate=48000,
matching_function=None, matching_function=None,
batch_size=32, batch_size=32,
@ -217,6 +221,7 @@ class EnhancerDataset(TaskDataset):
self.files = files self.files = files
self.duration = max(1.0, duration) self.duration = max(1.0, duration)
self.audio = Audio(self.sampling_rate, mono=True, return_tensor=True) self.audio = Audio(self.sampling_rate, mono=True, return_tensor=True)
self.stride = stride or duration
def setup(self, stage: Optional[str] = None): def setup(self, stage: Optional[str] = None):
@ -234,9 +239,22 @@ class EnhancerDataset(TaskDataset):
weights=[file["duration"] for file in self.train_data], weights=[file["duration"] for file in self.train_data],
) )
file_duration = file_dict["duration"] file_duration = file_dict["duration"]
start_time = round(rng.uniform(0, file_duration - self.duration), 2) num_segments = self.get_num_segments(
data = self.prepare_segment(file_dict, start_time) file_duration, self.duration, self.stride
yield data )
for index in range(0, num_segments):
start_time = index * self.stride
yield self.prepare_segment(file_dict, start_time)
@staticmethod
def get_num_segments(file_duration, duration, stride):
if file_duration < duration:
num_segments = 1
else:
num_segments = math.ceil((file_duration - duration) / stride) + 1
return num_segments
def val__getitem__(self, idx): def val__getitem__(self, idx):
return self.prepare_segment(*self._validation[idx]) return self.prepare_segment(*self._validation[idx])
@ -273,8 +291,16 @@ class EnhancerDataset(TaskDataset):
return {"clean": clean_segment, "noisy": noisy_segment} return {"clean": clean_segment, "noisy": noisy_segment}
def train__len__(self): def train__len__(self):
return math.ceil( return math.ceil(
sum([file["duration"] for file in self.train_data]) / self.duration sum(
[
self.get_num_segments(
file["duration"], self.duration, self.stride
)
for file in self.train_data
]
)
) )
def val__len__(self): def val__len__(self):