import math import multiprocessing import os from pathlib import Path from typing import Optional import numpy as np import pytorch_lightning as pl import torch import torch.nn.functional as F from torch.utils.data import DataLoader, Dataset, RandomSampler from torch_audiomentations import Compose from enhancer.data.fileprocessor import Fileprocessor from enhancer.utils import check_files from enhancer.utils.config import Files from enhancer.utils.io import Audio from enhancer.utils.random import create_unique_rng LARGE_NUM = 2147483647 class TrainDataset(Dataset): def __init__(self, dataset): self.dataset = dataset def __getitem__(self, idx): return self.dataset.train__getitem__(idx) def __len__(self): return self.dataset.train__len__() class ValidDataset(Dataset): def __init__(self, dataset): self.dataset = dataset def __getitem__(self, idx): return self.dataset.val__getitem__(idx) def __len__(self): return self.dataset.val__len__() class TestDataset(Dataset): def __init__(self, dataset): self.dataset = dataset def __getitem__(self, idx): return self.dataset.test__getitem__(idx) def __len__(self): return self.dataset.test__len__() class TaskDataset(pl.LightningDataModule): def __init__( self, name: str, root_dir: str, files: Files, min_valid_minutes: float = 0.20, duration: float = 1.0, stride=None, sampling_rate: int = 48000, matching_function=None, batch_size=32, num_workers: Optional[int] = None, augmentations: Optional[Compose] = None, ): super().__init__() self.name = name self.files, self.root_dir = check_files(root_dir, files) self.duration = duration self.stride = stride or duration self.sampling_rate = sampling_rate self.batch_size = batch_size self.matching_function = matching_function self._validation = [] if num_workers is None: num_workers = multiprocessing.cpu_count() // 2 self.num_workers = num_workers if min_valid_minutes > 0.0: self.min_valid_minutes = min_valid_minutes else: raise ValueError("min_valid_minutes must be greater than 0") self.augmentations = augmentations def setup(self, stage: Optional[str] = None): """ prepare train/validation/test data splits """ if stage in ("fit", None): train_clean = os.path.join(self.root_dir, self.files.train_clean) train_noisy = os.path.join(self.root_dir, self.files.train_noisy) fp = Fileprocessor.from_name( self.name, train_clean, train_noisy, self.matching_function ) train_data = fp.prepare_matching_dict() train_data, self.val_data = self.train_valid_split( train_data, min_valid_minutes=self.min_valid_minutes, random_state=42, ) self.train_data = self.prepare_traindata(train_data) self._validation = self.prepare_mapstype(self.val_data) test_clean = os.path.join(self.root_dir, self.files.test_clean) test_noisy = os.path.join(self.root_dir, self.files.test_noisy) fp = Fileprocessor.from_name( self.name, test_clean, test_noisy, self.matching_function ) test_data = fp.prepare_matching_dict() self._test = self.prepare_mapstype(test_data) def train_valid_split( self, data, min_valid_minutes: float = 20, random_state: int = 42 ): min_valid_minutes *= 60 valid_sec_now = 0.0 valid_indices = [] all_speakers = np.unique( [Path(file["clean"]).name.split("_")[0] for file in data] ) possible_indices = list(range(0, len(all_speakers))) rng = create_unique_rng(len(all_speakers)) while valid_sec_now <= min_valid_minutes: speaker_index = rng.choice(possible_indices) possible_indices.remove(speaker_index) speaker_name = all_speakers[speaker_index] print(f"Selected f{speaker_name} for valid") file_indices = [ i for i, file in enumerate(data) if speaker_name == Path(file["clean"]).name.split("_")[0] ] for i in file_indices: valid_indices.append(i) valid_sec_now += data[i]["duration"] train_data = [ item for i, item in enumerate(data) if i not in valid_indices ] valid_data = [item for i, item in enumerate(data) if i in valid_indices] return train_data, valid_data def prepare_traindata(self, data): train_data = [] for item in data: clean, noisy, total_dur = item.values() num_segments = self.get_num_segments( total_dur, self.duration, self.stride ) samples_metadata = ({"clean": clean, "noisy": noisy}, num_segments) train_data.append(samples_metadata) return train_data @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 prepare_mapstype(self, data): metadata = [] for item in data: clean, noisy, total_dur = item.values() if total_dur < self.duration: metadata.append(({"clean": clean, "noisy": noisy}, 0.0)) else: num_segments = self.get_num_segments( total_dur, self.duration, self.duration ) for index in range(num_segments): start_time = index * self.duration metadata.append( ({"clean": clean, "noisy": noisy}, start_time) ) return metadata def train_collatefn(self, batch): output = {"clean": [], "noisy": []} for item in batch: output["clean"].append(item["clean"]) output["noisy"].append(item["noisy"]) output["clean"] = torch.stack(output["clean"], dim=0) output["noisy"] = torch.stack(output["noisy"], dim=0) if self.augmentations is not None: noise = output["noisy"] - output["clean"] output["clean"] = self.augmentations( output["clean"], sample_rate=self.sampling_rate ) self.augmentations.freeze_parameters() output["noisy"] = ( self.augmentations(noise, sample_rate=self.sampling_rate) + output["clean"] ) return output @property def generator(self): generator = torch.Generator() if hasattr(self, "model"): seed = self.model.current_epoch + LARGE_NUM else: seed = LARGE_NUM return generator.manual_seed(seed) def train_dataloader(self): dataset = TrainDataset(self) sampler = RandomSampler(dataset, generator=self.generator) return DataLoader( dataset, batch_size=self.batch_size, num_workers=self.num_workers, sampler=sampler, collate_fn=self.train_collatefn, ) def val_dataloader(self): return DataLoader( ValidDataset(self), batch_size=self.batch_size, num_workers=self.num_workers, ) def test_dataloader(self): return DataLoader( TestDataset(self), batch_size=self.batch_size, num_workers=self.num_workers, ) class EnhancerDataset(TaskDataset): """ Dataset object for creating clean-noisy speech enhancement datasets paramters: name : str name of the dataset root_dir : str root directory of the dataset containing clean/noisy folders files : Files dataclass containing train_clean, train_noisy, test_clean, test_noisy folder names (refer enhancer.utils.Files dataclass) min_valid_minutes: float minimum validation split size time in minutes algorithm randomly select n speakers (>=min_valid_minutes) from train data to form validation data. duration : float expected audio duration of single audio sample for training sampling_rate : int desired sampling rate batch_size : int batch size of each batch num_workers : int num workers to be used while training matching_function : str maching functions - (one_to_one,one_to_many). Default set to None. use one_to_one mapping for datasets with one noisy file for each clean file use one_to_many mapping for multiple noisy files for each clean file """ def __init__( self, name: str, root_dir: str, files: Files, min_valid_minutes=5.0, duration=1.0, stride=None, sampling_rate=48000, matching_function=None, batch_size=32, num_workers: Optional[int] = None, augmentations: Optional[Compose] = None, ): super().__init__( name=name, root_dir=root_dir, files=files, min_valid_minutes=min_valid_minutes, sampling_rate=sampling_rate, duration=duration, matching_function=matching_function, batch_size=batch_size, num_workers=num_workers, augmentations=augmentations, ) self.sampling_rate = sampling_rate self.files = files self.duration = max(1.0, duration) self.audio = Audio(self.sampling_rate, mono=True, return_tensor=True) self.stride = stride or duration def setup(self, stage: Optional[str] = None): super().setup(stage=stage) def train__getitem__(self, idx): for filedict, num_samples in self.train_data: if idx >= num_samples: idx -= num_samples continue else: start = 0 if self.duration is not None: start = idx * self.stride return self.prepare_segment(filedict, start) def val__getitem__(self, idx): return self.prepare_segment(*self._validation[idx]) def test__getitem__(self, idx): return self.prepare_segment(*self._test[idx]) def prepare_segment(self, file_dict: dict, start_time: float): clean_segment = self.audio( file_dict["clean"], 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 train__len__(self): _, num_examples = list(zip(*self.train_data)) return sum(num_examples) def val__len__(self): return len(self._validation) def test__len__(self): return len(self._test)