370 lines
11 KiB
Python
370 lines
11 KiB
Python
import math
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import multiprocessing
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import os
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import random
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from itertools import chain, cycle
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from typing import Optional
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import pytorch_lightning as pl
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import torch
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import torch.nn.functional as F
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from torch.utils.data import DataLoader, Dataset, IterableDataset
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from enhancer.data.fileprocessor import Fileprocessor
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from enhancer.utils import check_files
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from enhancer.utils.config import Files
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from enhancer.utils.io import Audio
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from enhancer.utils.random import create_unique_rng
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class TrainDataset(IterableDataset):
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def __init__(self, dataset):
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self.dataset = dataset
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def __iter__(self):
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return self.dataset.train__iter__()
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def __len__(self):
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return self.dataset.train__len__()
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class ValidDataset(Dataset):
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def __init__(self, dataset):
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self.dataset = dataset
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def __getitem__(self, idx):
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return self.dataset.val__getitem__(idx)
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def __len__(self):
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return self.dataset.val__len__()
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class TestDataset(Dataset):
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def __init__(self, dataset):
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self.dataset = dataset
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def __getitem__(self, idx):
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return self.dataset.test__getitem__(idx)
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def __len__(self):
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return self.dataset.test__len__()
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class TaskDataset(pl.LightningDataModule):
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def __init__(
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self,
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name: str,
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root_dir: str,
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files: Files,
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valid_minutes: float = 0.20,
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duration: float = 1.0,
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stride=None,
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sampling_rate: int = 48000,
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matching_function=None,
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batch_size=32,
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num_workers: Optional[int] = None,
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):
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super().__init__()
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self.name = name
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self.files, self.root_dir = check_files(root_dir, files)
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self.duration = duration
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self.stride = stride or duration
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self.sampling_rate = sampling_rate
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self.batch_size = batch_size
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self.matching_function = matching_function
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self._validation = []
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if num_workers is None:
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num_workers = multiprocessing.cpu_count() // 2
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self.num_workers = num_workers
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print("num_workers-main", self.num_workers)
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if valid_minutes > 0.0:
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self.valid_minutes = valid_minutes
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else:
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raise ValueError("valid_minutes must be greater than 0")
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def setup(self, stage: Optional[str] = None):
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"""
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prepare train/validation/test data splits
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"""
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if stage in ("fit", None):
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train_clean = os.path.join(self.root_dir, self.files.train_clean)
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train_noisy = os.path.join(self.root_dir, self.files.train_noisy)
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fp = Fileprocessor.from_name(
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self.name, train_clean, train_noisy, self.matching_function
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)
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train_data = fp.prepare_matching_dict()
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train_data, self.val_data = self.train_valid_split(
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train_data, valid_minutes=self.valid_minutes, random_state=42
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)
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self.train_data = self.prepare_traindata(train_data)
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self._validation = self.prepare_mapstype(self.val_data)
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print(
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"train_data_size", sum([len(item) for item in self.train_data])
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)
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test_clean = os.path.join(self.root_dir, self.files.test_clean)
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test_noisy = os.path.join(self.root_dir, self.files.test_noisy)
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fp = Fileprocessor.from_name(
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self.name, test_clean, test_noisy, self.matching_function
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)
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test_data = fp.prepare_matching_dict()
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self._test = self.prepare_mapstype(test_data)
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def train_valid_split(
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self, data, valid_minutes: float = 20, random_state: int = 42
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):
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valid_minutes *= 60
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valid_min_now = 0.0
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valid_indices = []
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random_indices = list(range(0, len(data)))
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rng = create_unique_rng(random_state)
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rng.shuffle(random_indices)
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i = 0
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while valid_min_now <= valid_minutes:
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valid_indices.append(random_indices[i])
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valid_min_now += data[random_indices[i]]["duration"]
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i += 1
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train_data = [
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item for i, item in enumerate(data) if i not in valid_indices
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]
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valid_data = [item for i, item in enumerate(data) if i in valid_indices]
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return train_data, valid_data
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def prepare_traindata(self, data):
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train_data = []
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for item in data:
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samples_metadata = []
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clean, noisy, total_dur = item.values()
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num_segments = self.get_num_segments(
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total_dur, self.duration, self.stride
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)
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for index in range(num_segments):
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start = index * self.stride
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samples_metadata.append(
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({"clean": clean, "noisy": noisy}, start)
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)
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train_data.append(samples_metadata)
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return train_data[:25]
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@staticmethod
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def get_num_segments(file_duration, duration, stride):
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if file_duration < duration:
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num_segments = 1
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else:
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num_segments = math.ceil((file_duration - duration) / stride) + 1
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return num_segments
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def prepare_mapstype(self, data):
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metadata = []
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for item in data:
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clean, noisy, total_dur = item.values()
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if total_dur < self.duration:
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metadata.append(({"clean": clean, "noisy": noisy}, 0.0))
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else:
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num_segments = round(total_dur / self.duration)
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for index in range(num_segments):
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start_time = index * self.duration
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metadata.append(
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({"clean": clean, "noisy": noisy}, start_time)
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)
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return metadata
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def train_collatefn(self, batch):
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names = []
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output = {"noisy": [], "clean": []}
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for item in batch:
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output["noisy"].append(item["noisy"])
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output["clean"].append(item["clean"])
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names.append(item["name"])
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print(names)
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output["clean"] = torch.stack(output["clean"], dim=0)
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output["noisy"] = torch.stack(output["noisy"], dim=0)
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return output
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def worker_init_fn(self, _):
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worker_info = torch.utils.data.get_worker_info()
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dataset = worker_info.dataset
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worker_id = worker_info.id
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split_size = len(dataset.dataset.train_data) // worker_info.num_workers
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dataset.data = dataset.dataset.train_data[
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worker_id * split_size : (worker_id + 1) * split_size
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]
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def train_dataloader(self):
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return DataLoader(
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TrainDataset(self),
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batch_size=None,
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num_workers=self.num_workers,
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collate_fn=self.train_collatefn,
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worker_init_fn=self.worker_init_fn,
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)
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def val_dataloader(self):
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return DataLoader(
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ValidDataset(self),
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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)
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def test_dataloader(self):
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return DataLoader(
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TestDataset(self),
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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)
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class EnhancerDataset(TaskDataset):
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"""
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Dataset object for creating clean-noisy speech enhancement datasets
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paramters:
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name : str
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name of the dataset
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root_dir : str
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root directory of the dataset containing clean/noisy folders
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files : Files
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dataclass containing train_clean, train_noisy, test_clean, test_noisy
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folder names (refer enhancer.utils.Files dataclass)
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duration : float
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expected audio duration of single audio sample for training
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sampling_rate : int
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desired sampling rate
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batch_size : int
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batch size of each batch
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num_workers : int
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num workers to be used while training
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matching_function : str
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maching functions - (one_to_one,one_to_many). Default set to None.
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use one_to_one mapping for datasets with one noisy file for each clean file
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use one_to_many mapping for multiple noisy files for each clean file
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"""
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def __init__(
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self,
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name: str,
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root_dir: str,
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files: Files,
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valid_minutes=5.0,
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duration=1.0,
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stride=0.5,
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sampling_rate=48000,
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matching_function=None,
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batch_size=32,
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num_workers: Optional[int] = None,
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):
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super().__init__(
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name=name,
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root_dir=root_dir,
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files=files,
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valid_minutes=valid_minutes,
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sampling_rate=sampling_rate,
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duration=duration,
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matching_function=matching_function,
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batch_size=batch_size,
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num_workers=num_workers,
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)
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self.sampling_rate = sampling_rate
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self.files = files
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self.duration = max(1.0, duration)
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self.audio = Audio(self.sampling_rate, mono=True, return_tensor=True)
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self.stride = stride or duration
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def setup(self, stage: Optional[str] = None):
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super().setup(stage=stage)
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def random_sample(self, train_data):
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return random.sample(train_data, len(train_data))
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def train__iter__(self):
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rng = create_unique_rng(self.model.current_epoch)
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train_data = rng.sample(self.train_data, len(self.train_data))
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return zip(
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*[
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self.get_stream(self.random_sample(train_data))
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for i in range(self.batch_size)
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]
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)
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def get_stream(self, data):
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return chain.from_iterable(map(self.process_data, cycle(data)))
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def process_data(self, data):
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for item in data:
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yield self.prepare_segment(*item)
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@staticmethod
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def get_num_segments(file_duration, duration, stride):
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if file_duration < duration:
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num_segments = 1
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else:
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num_segments = math.ceil((file_duration - duration) / stride) + 1
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return num_segments
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def val__getitem__(self, idx):
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return self.prepare_segment(*self._validation[idx])
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def test__getitem__(self, idx):
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return self.prepare_segment(*self._test[idx])
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def prepare_segment(self, file_dict: dict, start_time: float):
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clean_segment = self.audio(
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file_dict["clean"], offset=start_time, duration=self.duration
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)
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noisy_segment = self.audio(
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file_dict["noisy"], offset=start_time, duration=self.duration
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)
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clean_segment = F.pad(
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clean_segment,
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(
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0,
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int(
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self.duration * self.sampling_rate - clean_segment.shape[-1]
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),
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),
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)
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noisy_segment = F.pad(
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noisy_segment,
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(
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0,
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int(
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self.duration * self.sampling_rate - noisy_segment.shape[-1]
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),
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),
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)
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return {
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"clean": clean_segment,
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"noisy": noisy_segment,
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"name": file_dict["clean"].split("/")[-1] + "->" + str(start_time),
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}
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def train__len__(self):
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worker_info = torch.utils.data.get_worker_info()
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if worker_info is None:
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train_data = self.train_data
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else:
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train_data = worker_info.dataset.data
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return sum([len(item) for item in train_data]) // (self.batch_size)
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def val__len__(self):
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return len(self._validation)
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def test__len__(self):
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return len(self._test)
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