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| from typing import Iterable
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| import torch
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| import numpy as np
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| import torch.utils.data
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| from torch.nn.utils.rnn import pad_sequence
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| from utils.data_utils import *
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| from torch.utils.data import ConcatDataset, Dataset
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| class CodecDataset(torch.utils.data.Dataset):
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| def __init__(self, cfg, dataset, is_valid=False):
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| """
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| Args:
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| cfg: config
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| dataset: dataset name
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| is_valid: whether to use train or valid dataset
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| """
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| assert isinstance(dataset, str)
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| processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset)
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| meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file
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| self.metafile_path = os.path.join(processed_data_dir, meta_file)
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| self.metadata = self.get_metadata()
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| self.data_root = processed_data_dir
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| self.cfg = cfg
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| if cfg.preprocess.use_audio:
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| self.utt2audio_path = {}
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| for utt_info in self.metadata:
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| dataset = utt_info["Dataset"]
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| uid = utt_info["Uid"]
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| utt = "{}_{}".format(dataset, uid)
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| self.utt2audio_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.audio_dir,
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| uid + ".npy",
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| )
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| elif cfg.preprocess.use_label:
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| self.utt2label_path = {}
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| for utt_info in self.metadata:
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| dataset = utt_info["Dataset"]
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| uid = utt_info["Uid"]
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| utt = "{}_{}".format(dataset, uid)
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| self.utt2label_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.label_dir,
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| uid + ".npy",
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| )
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| elif cfg.preprocess.use_one_hot:
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| self.utt2one_hot_path = {}
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| for utt_info in self.metadata:
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| dataset = utt_info["Dataset"]
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| uid = utt_info["Uid"]
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| utt = "{}_{}".format(dataset, uid)
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| self.utt2one_hot_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.one_hot_dir,
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| uid + ".npy",
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| )
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| if cfg.preprocess.use_mel:
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| self.utt2mel_path = {}
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| for utt_info in self.metadata:
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| dataset = utt_info["Dataset"]
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| uid = utt_info["Uid"]
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| utt = "{}_{}".format(dataset, uid)
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| self.utt2mel_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.mel_dir,
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| uid + ".npy",
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| )
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| if cfg.preprocess.use_frame_pitch:
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| self.utt2frame_pitch_path = {}
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| for utt_info in self.metadata:
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| dataset = utt_info["Dataset"]
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| uid = utt_info["Uid"]
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| utt = "{}_{}".format(dataset, uid)
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| self.utt2frame_pitch_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.pitch_dir,
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| uid + ".npy",
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| )
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| if cfg.preprocess.use_uv:
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| self.utt2uv_path = {}
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| for utt_info in self.metadata:
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| dataset = utt_info["Dataset"]
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| uid = utt_info["Uid"]
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| utt = "{}_{}".format(dataset, uid)
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| self.utt2uv_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.uv_dir,
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| uid + ".npy",
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| )
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| if cfg.preprocess.use_amplitude_phase:
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| self.utt2logamp_path = {}
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| self.utt2pha_path = {}
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| self.utt2rea_path = {}
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| self.utt2imag_path = {}
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| for utt_info in self.metadata:
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| dataset = utt_info["Dataset"]
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| uid = utt_info["Uid"]
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| utt = "{}_{}".format(dataset, uid)
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| self.utt2logamp_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.log_amplitude_dir,
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| uid + ".npy",
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| )
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| self.utt2pha_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.phase_dir,
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| uid + ".npy",
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| )
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| self.utt2rea_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.real_dir,
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| uid + ".npy",
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| )
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| self.utt2imag_path[utt] = os.path.join(
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| cfg.preprocess.processed_dir,
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| dataset,
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| cfg.preprocess.imaginary_dir,
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| uid + ".npy",
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| )
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|
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| def __getitem__(self, index):
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| utt_info = self.metadata[index]
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| dataset = utt_info["Dataset"]
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| uid = utt_info["Uid"]
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| utt = "{}_{}".format(dataset, uid)
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| single_feature = dict()
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| if self.cfg.preprocess.use_mel:
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| mel = np.load(self.utt2mel_path[utt])
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| assert mel.shape[0] == self.cfg.preprocess.n_mel
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| if "target_len" not in single_feature.keys():
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| single_feature["target_len"] = mel.shape[1]
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| single_feature["mel"] = mel
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| if self.cfg.preprocess.use_frame_pitch:
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| frame_pitch = np.load(self.utt2frame_pitch_path[utt])
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| if "target_len" not in single_feature.keys():
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| single_feature["target_len"] = len(frame_pitch)
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| aligned_frame_pitch = align_length(
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| frame_pitch, single_feature["target_len"]
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| )
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| single_feature["frame_pitch"] = aligned_frame_pitch
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| if self.cfg.preprocess.use_audio:
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| audio = np.load(self.utt2audio_path[utt])
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| single_feature["audio"] = audio
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| return single_feature
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|
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| def get_metadata(self):
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| with open(self.metafile_path, "r", encoding="utf-8") as f:
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| metadata = json.load(f)
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| return metadata
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| def get_dataset_name(self):
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| return self.metadata[0]["Dataset"]
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|
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| def __len__(self):
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| return len(self.metadata)
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| class CodecConcatDataset(ConcatDataset):
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| def __init__(self, datasets: Iterable[Dataset], full_audio_inference=False):
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| """Concatenate a series of datasets with their random inference audio merged."""
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| super().__init__(datasets)
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| self.cfg = self.datasets[0].cfg
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| self.metadata = []
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| for dataset in self.datasets:
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| self.metadata += dataset.metadata
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| if full_audio_inference:
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| self.eval_audios = []
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| self.eval_dataset_names = []
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| if self.cfg.preprocess.use_mel:
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| self.eval_mels = []
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| if self.cfg.preprocess.use_frame_pitch:
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| self.eval_pitchs = []
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| for dataset in self.datasets:
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| self.eval_audios.append(dataset.eval_audio)
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| self.eval_dataset_names.append(dataset.get_dataset_name())
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| if self.cfg.preprocess.use_mel:
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| self.eval_mels.append(dataset.eval_mel)
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| if self.cfg.preprocess.use_frame_pitch:
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| self.eval_pitchs.append(dataset.eval_pitch)
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|
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| class CodecCollator(object):
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| """Zero-pads model inputs and targets based on number of frames per step"""
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|
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| def __init__(self, cfg):
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| self.cfg = cfg
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|
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| def __call__(self, batch):
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| packed_batch_features = dict()
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| for key in batch[0].keys():
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| if key == "target_len":
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| packed_batch_features["target_len"] = torch.LongTensor(
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| [b["target_len"] for b in batch]
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| )
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| masks = [
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| torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch
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| ]
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| packed_batch_features["mask"] = pad_sequence(
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| masks, batch_first=True, padding_value=0
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| )
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| elif key == "mel":
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| values = [torch.from_numpy(b[key]).T for b in batch]
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| packed_batch_features[key] = pad_sequence(
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| values, batch_first=True, padding_value=0
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| )
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| else:
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| values = [torch.from_numpy(b[key]) for b in batch]
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| packed_batch_features[key] = pad_sequence(
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| values, batch_first=True, padding_value=0
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| )
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|
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| return packed_batch_features
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|
