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import math
from typing import Union, List
import torch
import torchaudio
from omegaconf import OmegaConf
from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler, RandomMultiClipSampler
from torch import Tensor
from torch_time_stretch import time_stretch
from imagebind.data.data_utils import waveform2melspec, get_constant_clip_timepoints, \
get_random_clip_timepoints
from bubogpt.datasets.data_utils import move_to_cuda, move_to_cpu
from bubogpt.processors.base_processor import BaseProcessor
from torchvision import transforms
from bubogpt.common.registry import registry
from bubogpt.processors.audio_augment import SpecAugmentation
class ImageBindAudioBaseProcessor(BaseProcessor):
def __init__(self, mean=None, std=None, target_sr=None, clip_duration=None, clips_per_video=None,
num_mel_bins=None, target_length=None, clip_sample_method="Random", use_global=False):
super().__init__()
self.mean = -4.268 if mean is None else mean
self.std = 9.138 if std is None else std
self.target_sr = 16000 if target_sr is None else target_sr
self.num_mel_bins = num_mel_bins
self.target_length = target_length
self.clip_duration = clip_duration
self.clip_sampler = self._construct_clip_sampler(clip_duration, clips_per_video, clip_sample_method)
self.normalize = transforms.Normalize(self.mean, self.std)
self.use_global = use_global
def _construct_clip_sampler(self, clip_duration, clips_per_video, clip_sample_method):
if clip_duration is None or clips_per_video is None:
return None
if clip_sample_method == "Constant":
return ConstantClipsPerVideoSampler(
clip_duration=clip_duration, clips_per_video=clips_per_video
)
elif clip_sample_method == "Random":
return RandomMultiClipSampler(clip_duration=clip_duration, num_clips=clips_per_video)
else:
raise NotImplementedError
def waveform_resample(self, waveform: Tensor, origin_sr: int) -> Tensor:
waveform = torchaudio.functional.resample(waveform, orig_freq=origin_sr, new_freq=self.target_sr)
all_duration = waveform.size(1) / self.target_sr
num_repeat = self.clip_duration / all_duration
if num_repeat < 1: # all duration > clip duration
return waveform
flatten_waves = torch.tile(waveform, dims=[1, int(num_repeat) + 1]) # [1, N * L]
return flatten_waves[:, :self.clip_duration * self.target_sr]
def global_stretching(self, waveform: Tensor) -> Tensor:
# NOTE: directly applying "waveform[:, ::shrink_ratio]" is FORBIDDEN!
# NOTE: May be Deprecated, TOO SLOW.
# shrink_ratio = self.clip_duration * self.target_sr / waveform.size(1)
# return move_to_cpu(time_stretch(move_to_cuda(waveform.unsqueeze(0)), shrink_ratio, self.target_sr)[0])
return waveform
def clip_sample(self, waveform: Tensor) -> List[Tensor]:
if self.clip_sampler is None:
return [waveform]
elif isinstance(self.clip_sampler, ConstantClipsPerVideoSampler):
all_clips_timepoints = get_constant_clip_timepoints(self.clip_sampler, waveform.size(1) / self.target_sr)
elif isinstance(self.clip_sampler, RandomMultiClipSampler):
all_clips_timepoints = get_random_clip_timepoints(self.clip_sampler, waveform.size(1) / self.target_sr)
else:
raise NotImplementedError
all_clips = []
for clip_timepoints in all_clips_timepoints:
start_pos = int(clip_timepoints[0] * self.target_sr)
end_pos = int(clip_timepoints[1] * self.target_sr)
waveform_clip = waveform[:, start_pos: end_pos]
all_clips.append(waveform_clip)
return all_clips
def waveform_melspec(self, waveforms: Union[List[Tensor], Tensor]) -> List[Tensor]:
if isinstance(waveforms, Tensor):
return waveform2melspec(waveforms, self.target_sr, self.num_mel_bins, self.target_length)
else:
return [waveform2melspec(waveform, self.target_sr, self.num_mel_bins, self.target_length)
for waveform in waveforms]
@registry.register_processor("imagebind_audio_train")
class ImageBindAudioTrainProcessor(ImageBindAudioBaseProcessor):
def __init__(self, mean=None, std=None, target_sr=None, clip_duration=None, clips_per_video=None,
clip_sample_method="Random", use_global=False, num_mel_bins=None, target_length=None,
time_drop_width=13, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2,
mask_type='mixture'):
super().__init__(mean=mean, std=std, target_sr=target_sr,
clip_duration=clip_duration, clips_per_video=clips_per_video,
num_mel_bins=num_mel_bins, target_length=target_length,
clip_sample_method=clip_sample_method, use_global=use_global)
self.spec_augment = SpecAugmentation(time_drop_width, time_stripes_num,
freq_drop_width, freq_stripes_num, mask_type)
def __call__(self, item):
# item: Tuple[Tensor, int]
waveform, origin_sr = item[0], item[1]
waveform = self.waveform_resample(waveform, origin_sr)
waveform_clips = self.clip_sample(waveform)
if self.use_global:
waveform_clips.append(self.global_stretching(waveform))
melspec_clips = self.waveform_melspec(waveform_clips)
normed_melspecs = [self.normalize(clip) for clip in melspec_clips]
all_clips = torch.stack(normed_melspecs, dim=0)
# all_clips: [clips_per_video, channel, mel_bins, time_steps]
# augment: [batch_size, channel, time_steps, freq_bins]
augmented_clips = self.spec_augment(all_clips.transpose(-2, -1)).transpose(-2, -1)
return augmented_clips
@classmethod
def from_config(cls, cfg=None):
if cfg is None:
cfg = OmegaConf.create()
target_sr = cfg.get("target_sr", 16000)
clip_duration = cfg.get("clip_duration", None)
clips_per_video = cfg.get("clips_per_video", None)
num_mel_bins = cfg.get("num_mel_bins", 128)
target_length = cfg.get("target_length", 204)
time_drop_width = cfg.get("time_drop_width", 13)
time_stripes_num = cfg.get("time_stripes_num", 2)
# 13 * 2 / 204 = 12.75% Time Mask
freq_drop_width = cfg.get("freq_drop_width", 8)
freq_stripes_num = cfg.get("freq_stripes_num", 2)
# 8 * 2 / 128 = 12.5% Freq Mask
mask_type = cfg.get("mask_type", 'mixture')
use_global = cfg.get("use_global", False)
mean = cfg.get("mean", None)
std = cfg.get("std", None)
return cls(
mean=mean, std=std, target_sr=target_sr,
clip_duration=clip_duration, clips_per_video=clips_per_video,
num_mel_bins=num_mel_bins, target_length=target_length,
time_drop_width=time_drop_width, time_stripes_num=time_stripes_num,
freq_drop_width=freq_drop_width, freq_stripes_num=freq_stripes_num,
mask_type=mask_type, use_global=use_global
)
@registry.register_processor("imagebind_audio_eval")
class ImageBindAudioEvalProcessor(ImageBindAudioBaseProcessor):
def __init__(self, mean=None, std=None, target_sr=None, clip_duration=None, clips_per_video=None,
clip_sample_method="Constant", use_global=False, num_mel_bins=None, target_length=None):
super().__init__(mean=mean, std=std, target_sr=target_sr,
clip_duration=clip_duration, clips_per_video=clips_per_video,
num_mel_bins=num_mel_bins, target_length=target_length,
clip_sample_method=clip_sample_method, use_global=use_global)
def __call__(self, item):
# item: Tuple[Tensor, int]
waveform, origin_sr = item[0], item[1]
waveform = self.waveform_resample(waveform, origin_sr)
waveform_clips = self.clip_sample(waveform)
if self.use_global:
waveform_clips.append(self.global_stretching(waveform))
melspec_clips = self.waveform_melspec(waveform_clips)
normed_melspecs = [self.normalize(clip) for clip in melspec_clips]
all_clips = torch.stack(normed_melspecs, dim=0)
# all_clips: [clips_per_video, channel, mel_bins, time_steps]
return all_clips
@classmethod
def from_config(cls, cfg=None):
if cfg is None:
cfg = OmegaConf.create()
target_sr = cfg.get("target_sr", 16000)
clip_duration = cfg.get("clip_duration", None)
clips_per_video = cfg.get("clips_per_video", None)
num_mel_bins = cfg.get("num_mel_bins", 128)
target_length = cfg.get("target_length", 204)
mean = cfg.get("mean", None)
std = cfg.get("std", None)
return cls(
mean=mean, std=std, target_sr=target_sr,
clip_duration=clip_duration, clips_per_video=clips_per_video,
num_mel_bins=num_mel_bins, target_length=target_length
)
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