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enhancer datasets

This commit is contained in:
shahules786 2022-08-26 16:58:02 +05:30
parent 22b017daea
commit 556da7c3a0
1 changed files with 20 additions and 36 deletions
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56
enhancer/data/vctk.py
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@ -9,45 +9,29 @@ import torch.nn.functional as F
from enhancer.utils.random import create_unique_rng
from enhancer.utils.io import Audio
from enhancer.utils import Fileprocessor
class Vctk(IterableDataset):
"""Dataset object for Voice Bank Corpus (VCTK) Dataset"""
class EnhancerDataset(IterableDataset):
"""Dataset object for creating clean-noisy speech enhancement datasets"""
def __init__(self,clean_path,noisy_path,duration=1.0,sampling_rate=48000):
def __init__(self,name:str,clean_dir,noisy_dir,duration=1.0,sampling_rate=48000, matching_function=None):
if not os.path.isdir(clean_path):
raise ValueError(f"{clean_path} is not a valid directory")
if not os.path.isdir(clean_dir):
raise ValueError(f"{clean_dir} is not a valid directory")
if not os.path.isdir(noisy_path):
raise ValueError(f"{clean_path} is not a valid directory")
if not os.path.isdir(noisy_dir):
raise ValueError(f"{clean_dir} is not a valid directory")
self.sampling_rate = sampling_rate
self.clean_path = clean_path
self.noisy_path = noisy_path
self.files_duration = self.get_matching_files_duration()
self.wav_samples = list(self.files_duration.keys())
self.clean_dir = clean_dir
self.noisy_dir = noisy_dir
self.duration = max(1.0,duration)
self.audio = Audio(self.sampling_rate,mono=True,return_tensor=True)
def get_matching_files_duration(self):
matching_wavfiles_dur = dict()
clean_filenames = [file.split('/')[-1] for file in glob.glob(os.path.join(self.clean_path,"*.wav"))]
noisy_filenames = [file.split('/')[-1] for file in glob.glob(os.path.join(self.noisy_path,"*.wav"))]
common_filenames = np.intersect1d(noisy_filenames,clean_filenames)
for file_name in common_filenames:
sr_clean, clean_file = wavfile.read(os.path.join(self.clean_path,file_name))
sr_noisy, noisy_file = wavfile.read(os.path.join(self.noisy_path,file_name))
if ((clean_file.shape[-1]==noisy_file.shape[-1]) and
(sr_clean==self.sampling_rate) and
(sr_noisy==self.sampling_rate)):
matching_wavfiles_dur.update({file_name:(clean_file.shape[-1]/self.sampling_rate)})
return matching_wavfiles_dur
fp = Fileprocessor.from_name(name,clean_dir,noisy_dir,matching_function)
self.valid_files = fp.prepare_matching_dict()
def __iter__(self):
@ -55,18 +39,18 @@ class Vctk(IterableDataset):
while True:
file_name,*_ = rng.choices(self.wav_samples,k=1,
weights=[self.files_duration[file] for file in self.wav_samples])
file_duration = self.files_duration.get(file_name)
file_dict,*_ = rng.choices(self.valid_files,k=1,
weights=[self.valid_files[file]['duration'] for file in self.valid_files])
file_duration = file_dict['duration']
start_time = round(rng.uniform(0,file_duration- self.duration),2)
data = self.prepare_segment(file_name,start_time)
data = self.prepare_segment(file_dict,start_time)
yield data
def prepare_segment(self,file_name:str, start_time:float):
def prepare_segment(self,file_dict:dict, start_time:float):
clean_segment = self.audio(os.path.join(self.clean_path,file_name),
clean_segment = self.audio(file_dict.keys()[0],
offset=start_time,duration=self.duration)
noisy_segment = self.audio(os.path.join(self.noisy_path,file_name),
noisy_segment = self.audio(file_dict['noisy'],
offset=start_time,duration=self.duration)
clean_segment = F.pad(clean_segment,(0,int(self.duration*self.sampling_rate-clean_segment.shape[-1])))
noisy_segment = F.pad(noisy_segment,(0,int(self.duration*self.sampling_rate-noisy_segment.shape[-1])))
@ -74,7 +58,7 @@ class Vctk(IterableDataset):
def __len__(self):
return math.ceil(sum(self.files_duration.values())/self.duration)
return math.ceil(sum([file["duration"] for file in self.valid_files])/self.duration)