# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Iterable
import torch
import numpy as np
import torch.utils.data
from torch.nn.utils.rnn import pad_sequence
from utils.data_utils import *
from torch.utils.data import ConcatDataset, Dataset
class CodecDataset(torch.utils.data.Dataset):
def __init__(self, cfg, dataset, is_valid=False):
"""
Args:
cfg: config
dataset: dataset name
is_valid: whether to use train or valid dataset
"""
assert isinstance(dataset, str)
processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset)
meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file
self.metafile_path = os.path.join(processed_data_dir, meta_file)
self.metadata = self.get_metadata()
self.data_root = processed_data_dir
self.cfg = cfg
if cfg.preprocess.use_audio:
self.utt2audio_path = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
self.utt2audio_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.audio_dir,
uid + ".npy",
)
elif cfg.preprocess.use_label:
self.utt2label_path = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
self.utt2label_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.label_dir,
uid + ".npy",
)
elif cfg.preprocess.use_one_hot:
self.utt2one_hot_path = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
self.utt2one_hot_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.one_hot_dir,
uid + ".npy",
)
if cfg.preprocess.use_mel:
self.utt2mel_path = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
self.utt2mel_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.mel_dir,
uid + ".npy",
)
if cfg.preprocess.use_frame_pitch:
self.utt2frame_pitch_path = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
self.utt2frame_pitch_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.pitch_dir,
uid + ".npy",
)
if cfg.preprocess.use_uv:
self.utt2uv_path = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
self.utt2uv_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.uv_dir,
uid + ".npy",
)
if cfg.preprocess.use_amplitude_phase:
self.utt2logamp_path = {}
self.utt2pha_path = {}
self.utt2rea_path = {}
self.utt2imag_path = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
self.utt2logamp_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.log_amplitude_dir,
uid + ".npy",
)
self.utt2pha_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.phase_dir,
uid + ".npy",
)
self.utt2rea_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.real_dir,
uid + ".npy",
)
self.utt2imag_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.imaginary_dir,
uid + ".npy",
)
def __getitem__(self, index):
utt_info = self.metadata[index]
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
single_feature = dict()
if self.cfg.preprocess.use_mel:
mel = np.load(self.utt2mel_path[utt])
assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T]
if "target_len" not in single_feature.keys():
single_feature["target_len"] = mel.shape[1]
single_feature["mel"] = mel
if self.cfg.preprocess.use_frame_pitch:
frame_pitch = np.load(self.utt2frame_pitch_path[utt])
if "target_len" not in single_feature.keys():
single_feature["target_len"] = len(frame_pitch)
aligned_frame_pitch = align_length(
frame_pitch, single_feature["target_len"]
)
single_feature["frame_pitch"] = aligned_frame_pitch
if self.cfg.preprocess.use_audio:
audio = np.load(self.utt2audio_path[utt])
single_feature["audio"] = audio
return single_feature
def get_metadata(self):
with open(self.metafile_path, "r", encoding="utf-8") as f:
metadata = json.load(f)
return metadata
def get_dataset_name(self):
return self.metadata[0]["Dataset"]
def __len__(self):
return len(self.metadata)
class CodecConcatDataset(ConcatDataset):
def __init__(self, datasets: Iterable[Dataset], full_audio_inference=False):
"""Concatenate a series of datasets with their random inference audio merged."""
super().__init__(datasets)
self.cfg = self.datasets[0].cfg
self.metadata = []
# Merge metadata
for dataset in self.datasets:
self.metadata += dataset.metadata
# Merge random inference features
if full_audio_inference:
self.eval_audios = []
self.eval_dataset_names = []
if self.cfg.preprocess.use_mel:
self.eval_mels = []
if self.cfg.preprocess.use_frame_pitch:
self.eval_pitchs = []
for dataset in self.datasets:
self.eval_audios.append(dataset.eval_audio)
self.eval_dataset_names.append(dataset.get_dataset_name())
if self.cfg.preprocess.use_mel:
self.eval_mels.append(dataset.eval_mel)
if self.cfg.preprocess.use_frame_pitch:
self.eval_pitchs.append(dataset.eval_pitch)
class CodecCollator(object):
"""Zero-pads model inputs and targets based on number of frames per step"""
def __init__(self, cfg):
self.cfg = cfg
def __call__(self, batch):
packed_batch_features = dict()
# mel: [b, n_mels, frame]
# frame_pitch: [b, frame]
# audios: [b, frame * hop_size]
for key in batch[0].keys():
if key == "target_len":
packed_batch_features["target_len"] = torch.LongTensor(
[b["target_len"] for b in batch]
)
masks = [
torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch
]
packed_batch_features["mask"] = pad_sequence(
masks, batch_first=True, padding_value=0
)
elif key == "mel":
values = [torch.from_numpy(b[key]).T for b in batch]
packed_batch_features[key] = pad_sequence(
values, batch_first=True, padding_value=0
)
else:
values = [torch.from_numpy(b[key]) for b in batch]
packed_batch_features[key] = pad_sequence(
values, batch_first=True, padding_value=0
)
return packed_batch_features
