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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 os
import numpy as np
import torch
import torchaudio
def save_feature(process_dir, feature_dir, item, feature, overrides=True):
"""Save features to path
Args:
process_dir (str): directory to store features
feature_dir (_type_): directory to store one type of features (mel, energy, ...)
item (str): uid
feature (tensor): feature tensor
overrides (bool, optional): whether to override existing files. Defaults to True.
"""
process_dir = os.path.join(process_dir, feature_dir)
os.makedirs(process_dir, exist_ok=True)
out_path = os.path.join(process_dir, item + ".npy")
if os.path.exists(out_path):
if overrides:
np.save(out_path, feature)
else:
np.save(out_path, feature)
def save_txt(process_dir, feature_dir, item, feature, overrides=True):
process_dir = os.path.join(process_dir, feature_dir)
os.makedirs(process_dir, exist_ok=True)
out_path = os.path.join(process_dir, item + ".txt")
if os.path.exists(out_path):
if overrides:
f = open(out_path, "w")
f.writelines(feature)
f.close()
else:
f = open(out_path, "w")
f.writelines(feature)
f.close()
def save_audio(path, waveform, fs, add_silence=False, turn_up=False, volume_peak=0.9):
if turn_up:
# continue to turn up to volume_peak
ratio = volume_peak / max(waveform.max(), abs(waveform.min()))
waveform = waveform * ratio
if add_silence:
silence_len = fs // 20
silence = np.zeros((silence_len,), dtype=waveform.dtype)
result = np.concatenate([silence, waveform, silence])
waveform = result
waveform = torch.as_tensor(waveform, dtype=torch.float32, device="cpu")
if len(waveform.size()) == 1:
waveform = waveform[None, :]
elif waveform.size(0) != 1:
# Stereo to mono
waveform = torch.mean(waveform, dim=0, keepdim=True)
torchaudio.save(path, waveform, fs, encoding="PCM_S", bits_per_sample=16)
async def async_load_audio(path, sample_rate: int = 24000):
r"""
Args:
path: The source loading path.
sample_rate: The target sample rate, will automatically resample if necessary.
Returns:
waveform: The waveform object. Should be [1 x sequence_len].
"""
async def use_torchaudio_load(path):
return torchaudio.load(path)
waveform, sr = await use_torchaudio_load(path)
waveform = torch.mean(waveform, dim=0, keepdim=True)
if sr != sample_rate:
waveform = torchaudio.functional.resample(waveform, sr, sample_rate)
if torch.any(torch.isnan(waveform) or torch.isinf(waveform)):
raise ValueError("NaN or Inf found in waveform.")
return waveform
async def async_save_audio(
path,
waveform,
sample_rate: int = 24000,
add_silence: bool = False,
volume_peak: float = 0.9,
):
r"""
Args:
path: The target saving path.
waveform: The waveform object. Should be [n_channel x sequence_len].
sample_rate: Sample rate.
add_silence: If ``true``, concat 0.05s silence to beginning and end.
volume_peak: Turn up volume for larger number, vice versa.
"""
async def use_torchaudio_save(path, waveform, sample_rate):
torchaudio.save(
path, waveform, sample_rate, encoding="PCM_S", bits_per_sample=16
)
waveform = torch.as_tensor(waveform, device="cpu", dtype=torch.float32)
shape = waveform.size()[:-1]
ratio = abs(volume_peak) / max(waveform.max(), abs(waveform.min()))
waveform = waveform * ratio
if add_silence:
silence_len = sample_rate // 20
silence = torch.zeros((*shape, silence_len), dtype=waveform.type())
waveform = torch.concatenate((silence, waveform, silence), dim=-1)
if waveform.dim() == 1:
waveform = waveform[None]
await use_torchaudio_save(path, waveform, sample_rate)
def load_mel_extrema(cfg, dataset_name, split):
dataset_dir = os.path.join(
cfg.OUTPUT_PATH,
"preprocess/{}_version".format(cfg.data.process_version),
dataset_name,
)
min_file = os.path.join(
dataset_dir,
"mel_min_max",
split.split("_")[-1],
"mel_min.npy",
)
max_file = os.path.join(
dataset_dir,
"mel_min_max",
split.split("_")[-1],
"mel_max.npy",
)
mel_min = np.load(min_file)
mel_max = np.load(max_file)
return mel_min, mel_max
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