rename module

enhancer/data/vctk.py

@@ -1,64 +0,0 @@
-
-import glob
-import math
-import numpy as np
-import os
-from scipy.io import wavfile
-from torch.utils.data import IterableDataset
-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 EnhancerDataset(IterableDataset):
-    """Dataset object for creating clean-noisy speech enhancement datasets"""
-
-    def __init__(self,name:str,clean_dir,noisy_dir,duration=1.0,sampling_rate=48000, matching_function=None):
-        
-        if not os.path.isdir(clean_dir):
-            raise ValueError(f"{clean_dir} 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_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)
-
-        fp = Fileprocessor.from_name(name,clean_dir,noisy_dir,matching_function)
-        self.valid_files = fp.prepare_matching_dict()
-
-    def __iter__(self):
-
-        rng = create_unique_rng(12) ##pass epoch number here
-        
-        while True:
-
-            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_dict,start_time)
-            yield data
-
-    def prepare_segment(self,file_dict:dict, start_time:float):
-
-        clean_segment = self.audio(file_dict.keys()[0],
-                                    offset=start_time,duration=self.duration)
-        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])))
-        return {"clean": clean_segment,"noisy":noisy_segment}
-        
-    def __len__(self):
-
-        return math.ceil(sum([file["duration"] for file in self.valid_files])/self.duration)
-
-
-