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# 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.
import torch
import random
import numpy as np
from torch.nn import functional as F
from torch.nn.utils.rnn import pad_sequence
from utils.data_utils import *
from models.vocoders.vocoder_dataset import VocoderDataset
class GANVocoderDataset(VocoderDataset):
def __init__(self, cfg, dataset, is_valid=False):
"""
Args:
cfg: config
dataset: dataset name
is_valid: whether to use train or valid dataset
"""
super().__init__(cfg, dataset, is_valid)
eval_index = random.randint(0, len(self.metadata) - 1)
eval_utt_info = self.metadata[eval_index]
eval_utt = "{}_{}".format(eval_utt_info["Dataset"], eval_utt_info["Uid"])
self.eval_audio = np.load(self.utt2audio_path[eval_utt])
if cfg.preprocess.use_mel:
self.eval_mel = np.load(self.utt2mel_path[eval_utt])
if cfg.preprocess.use_frame_pitch:
self.eval_pitch = np.load(self.utt2frame_pitch_path[eval_utt])
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
if "target_len" not in single_feature.keys():
single_feature["target_len"] = mel.shape[1]
if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame:
mel = np.pad(
mel,
((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])),
mode="constant",
)
else:
if "start" not in single_feature.keys():
start = random.randint(
0, mel.shape[-1] - self.cfg.preprocess.cut_mel_frame
)
end = start + self.cfg.preprocess.cut_mel_frame
single_feature["start"] = start
single_feature["end"] = end
mel = mel[:, single_feature["start"] : single_feature["end"]]
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"]
)
if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame:
aligned_frame_pitch = np.pad(
aligned_frame_pitch,
(
(
0,
self.cfg.preprocess.cut_mel_frame
* self.cfg.preprocess.hop_size
- audio.shape[-1],
)
),
mode="constant",
)
else:
if "start" not in single_feature.keys():
start = random.randint(
0,
aligned_frame_pitch.shape[-1]
- self.cfg.preprocess.cut_mel_frame,
)
end = start + self.cfg.preprocess.cut_mel_frame
single_feature["start"] = start
single_feature["end"] = end
aligned_frame_pitch = aligned_frame_pitch[
single_feature["start"] : single_feature["end"]
]
single_feature["frame_pitch"] = aligned_frame_pitch
if self.cfg.preprocess.use_audio:
audio = np.load(self.utt2audio_path[utt])
assert "target_len" in single_feature.keys()
if (
audio.shape[-1]
<= self.cfg.preprocess.cut_mel_frame * self.cfg.preprocess.hop_size
):
audio = np.pad(
audio,
(
(
0,
self.cfg.preprocess.cut_mel_frame
* self.cfg.preprocess.hop_size
- audio.shape[-1],
)
),
mode="constant",
)
else:
if "start" not in single_feature.keys():
audio = audio[
0 : self.cfg.preprocess.cut_mel_frame
* self.cfg.preprocess.hop_size
]
else:
audio = audio[
single_feature["start"]
* self.cfg.preprocess.hop_size : single_feature["end"]
* self.cfg.preprocess.hop_size,
]
single_feature["audio"] = audio
if self.cfg.preprocess.use_amplitude_phase:
logamp = np.load(self.utt2logamp_path[utt])
pha = np.load(self.utt2pha_path[utt])
rea = np.load(self.utt2rea_path[utt])
imag = np.load(self.utt2imag_path[utt])
assert "target_len" in single_feature.keys()
if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame:
logamp = np.pad(
logamp,
((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])),
mode="constant",
)
pha = np.pad(
pha,
((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])),
mode="constant",
)
rea = np.pad(
rea,
((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])),
mode="constant",
)
imag = np.pad(
imag,
((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])),
mode="constant",
)
else:
logamp = logamp[:, single_feature["start"] : single_feature["end"]]
pha = pha[:, single_feature["start"] : single_feature["end"]]
rea = rea[:, single_feature["start"] : single_feature["end"]]
imag = imag[:, single_feature["start"] : single_feature["end"]]
single_feature["logamp"] = logamp
single_feature["pha"] = pha
single_feature["rea"] = rea
single_feature["imag"] = imag
return single_feature
class GANVocoderCollator(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 in ["target_len", "start", "end"]:
continue
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
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