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import torch
import torchaudio
import torchvision
import numpy as np
from contextlib import suppress
import torch.nn.functional as F
def int16_to_float32(x):
return (x / 32767.0).astype(np.float32)
def float32_to_int16(x):
x = np.clip(x, a_min=-1., a_max=1.)
return (x * 32767.).astype(np.int16)
def get_mel(audio_data, audio_cfg):
# mel shape: (n_mels, T)
mel_tf = torchaudio.transforms.MelSpectrogram(
sample_rate=audio_cfg['sample_rate'],
n_fft=audio_cfg['window_size'],
win_length=audio_cfg['window_size'],
hop_length=audio_cfg['hop_size'],
center=True,
pad_mode="reflect",
power=2.0,
norm=None,
onesided=True,
n_mels=audio_cfg['mel_bins'],
f_min=audio_cfg['fmin'],
f_max=audio_cfg['fmax']
).to(audio_data.device)
mel = mel_tf(audio_data)
# we use log mel spectrogram as input
mel = torchaudio.transforms.AmplitudeToDB(top_db=None)(mel)
return mel.T # (T, n_mels)
def get_audio_features(sample, audio_data, max_len, data_truncating, data_filling, audio_cfg, require_grad=False):
"""
Calculate and add audio features to sample.
Sample: a dict containing all the data of current sample.
audio_data: a tensor of shape (T) containing audio data.
max_len: the maximum length of audio data.
data_truncating: the method of truncating data.
data_filling: the method of filling data.
audio_cfg: a dict containing audio configuration. Comes from model_cfg['audio_cfg'].
require_grad: whether to require gradient for audio data.
This is useful when we want to apply gradient-based classifier-guidance.
"""
grad_fn = suppress if require_grad else torch.no_grad
with grad_fn():
if len(audio_data) > max_len:
if data_truncating == "rand_trunc":
longer = torch.tensor([True])
elif data_truncating == "fusion":
# fusion
mel = get_mel(audio_data, audio_cfg)
# split to three parts
chunk_frames = max_len // audio_cfg['hop_size'] + 1 # the +1 related to how the spectrogram is computed
total_frames = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is
# larger than max_len but smaller than max_len+hop_size.
# In this case, we just use the whole audio.
mel_fusion = torch.stack([mel, mel, mel, mel], dim=0)
sample["mel_fusion"] = mel_fusion
longer = torch.tensor([False])
else:
ranges = np.array_split(list(range(0, total_frames - chunk_frames + 1)), 3)
if len(ranges[1]) == 0:
# if the audio is too short, we just use the first chunk
ranges[1] = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
ranges[2] = [0]
# randomly choose index for each part
idx_front = np.random.choice(ranges[0])
idx_middle = np.random.choice(ranges[1])
idx_back = np.random.choice(ranges[2])
# select mel
mel_chunk_front = mel[idx_front:idx_front + chunk_frames, :]
mel_chunk_middle = mel[idx_middle:idx_middle + chunk_frames, :]
mel_chunk_back = mel[idx_back:idx_back + chunk_frames, :]
# shrink the mel
mel_shrink = torchvision.transforms.Resize(size=[chunk_frames, audio_cfg['mel_bins']])(mel[None])[0]
# stack
mel_fusion = torch.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back], dim=0)
sample["mel_fusion"] = mel_fusion
longer = torch.tensor([True])
else:
raise NotImplementedError(
f"data_truncating {data_truncating} not implemented"
)
# random crop to max_len (for compatibility)
overflow = len(audio_data) - max_len
idx = np.random.randint(0, overflow + 1)
audio_data = audio_data[idx: idx + max_len]
else: # padding if too short
if len(audio_data) < max_len: # do nothing if equal
if data_filling == "repeatpad":
n_repeat = int(max_len / len(audio_data))
audio_data = audio_data.repeat(n_repeat)
audio_data = F.pad(
audio_data,
(0, max_len - len(audio_data)),
mode="constant",
value=0,
)
elif data_filling == "pad":
audio_data = F.pad(
audio_data,
(0, max_len - len(audio_data)),
mode="constant",
value=0,
)
elif data_filling == "repeat":
n_repeat = int(max_len / len(audio_data))
audio_data = audio_data.repeat(n_repeat + 1)[:max_len]
else:
raise NotImplementedError(
f"data_filling {data_filling} not implemented"
)
if data_truncating == 'fusion':
mel = get_mel(audio_data, audio_cfg)
mel_fusion = torch.stack([mel, mel, mel, mel], dim=0)
sample["mel_fusion"] = mel_fusion
longer = torch.tensor([False])
sample["longer"] = longer
sample["waveform"] = audio_data
return sample
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