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e857f97 d2d0f6f e857f97 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | import cv2
import numpy as np
import torch
import torchaudio
from torchvision import transforms
from transformers import ProcessorMixin, BatchEncoding
from transformers.image_processing_utils import BatchFeature
from torch.nn import functional as F
def make_list_of_images(x):
if not isinstance(x, list):
return [x]
return x
# torchaudio.set_audio_backend was deprecated and later removed; it is a no-op
# on modern torchaudio, so only call it when present (older versions).
if hasattr(torchaudio, "set_audio_backend"):
torchaudio.set_audio_backend("soundfile")
def torchaudio_loader(path):
return torchaudio.load(path)
def int16_to_float32_torch(x):
return (x / 32767.0).type(torch.float32)
def float32_to_int16_torch(x):
x = torch.clamp(x, min=-1., max=1.)
return (x * 32767.).type(torch.int16)
DEFAULT_AUDIO_FRAME_SHIFT_MS = 10
class AudioTransform:
def __init__(self, args):
self.sample_rate = args.audio_sample_rate
self.num_mel_bins = args.num_mel_bins
self.target_length = args.target_length
self.audio_mean = args.audio_mean
self.audio_std = args.audio_std
self.mean = []
self.std = []
# mean=-4.2677393
# std=4.5689974
# self.norm = transforms.Normalize(mean=self.audio_mean, std=self.audio_std)
def __call__(self, audio_data_and_origin_sr):
audio_data, origin_sr = audio_data_and_origin_sr
if self.sample_rate != origin_sr:
# print(audio_data.shape, origin_sr)
audio_data = torchaudio.functional.resample(audio_data, orig_freq=origin_sr, new_freq=self.sample_rate)
waveform_melspec = self.waveform2melspec(audio_data)
return waveform_melspec
def waveform2melspec(self, audio_data):
mel = self.get_mel(audio_data)
if mel.shape[0] > self.target_length:
# split to three parts
chunk_frames = self.target_length
total_frames = mel.shape[0]
ranges = np.array_split(list(range(0, total_frames - chunk_frames + 1)), 3)
# print('total_frames-chunk_frames:', total_frames-chunk_frames,
# 'len(audio_data):', len(audio_data),
# 'chunk_frames:', chunk_frames,
# 'total_frames:', total_frames)
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])
# idx_front = ranges[0][0] # fixed
# idx_middle = ranges[1][0]
# idx_back = ranges[2][0]
# 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, :]
# print(total_frames, idx_front, idx_front + chunk_frames, idx_middle, idx_middle + chunk_frames, idx_back, idx_back + chunk_frames)
# stack
mel_fusion = torch.stack([mel_chunk_front, mel_chunk_middle, mel_chunk_back], dim=0)
elif mel.shape[0] < self.target_length: # padding if too short
n_repeat = int(self.target_length / mel.shape[0]) + 1
# print(self.target_length, mel.shape[0], n_repeat)
mel = mel.repeat(n_repeat, 1)[:self.target_length, :]
mel_fusion = torch.stack([mel, mel, mel], dim=0)
else: # if equal
mel_fusion = torch.stack([mel, mel, mel], dim=0)
mel_fusion = mel_fusion.transpose(1, 2) # [3, target_length, mel_bins] -> [3, mel_bins, target_length]
# self.mean.append(mel_fusion.mean())
# self.std.append(mel_fusion.std())
mel_fusion = (mel_fusion - self.audio_mean) / (self.audio_std * 2)
return mel_fusion
def get_mel(self, audio_data):
# mel shape: (n_mels, T)
audio_data -= audio_data.mean()
mel = torchaudio.compliance.kaldi.fbank(
audio_data,
htk_compat=True,
sample_frequency=self.sample_rate,
use_energy=False,
window_type="hanning",
num_mel_bins=self.num_mel_bins,
dither=0.0,
frame_length=25,
frame_shift=DEFAULT_AUDIO_FRAME_SHIFT_MS,
)
return mel # (T, n_mels)
def get_audio_transform(config):
config = config.vision_config
return AudioTransform(config)
def load_and_transform_audio(
audio_path,
transform,
):
waveform_and_sr = torchaudio_loader(audio_path)
audio_outputs = transform(waveform_and_sr)
return audio_outputs
class LanguageBindAudioProcessor(ProcessorMixin):
attributes = []
tokenizer_class = ("LanguageBindAudioTokenizer")
def __init__(self, config, tokenizer=None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.transform = get_audio_transform(config)
self.image_processor = load_and_transform_audio
self.tokenizer = tokenizer
def __call__(self, images=None, text=None, context_length=77, return_tensors=None, **kwargs):
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.")
if text is not None:
encoding = self.tokenizer(text, max_length=context_length, padding='max_length',
truncation=True, return_tensors=return_tensors, **kwargs)
if images is not None:
images = make_list_of_images(images)
image_features = [self.image_processor(image, self.transform) for image in images]
image_features = torch.stack(image_features)
if text is not None and images is not None:
encoding["pixel_values"] = image_features
return encoding
elif text is not None:
return encoding
else:
return {"pixel_values": image_features}
def batch_decode(self, skip_special_tokens=True, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, skip_special_tokens=skip_special_tokens, **kwargs)
def decode(self, skip_special_tokens=True, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, skip_special_tokens=skip_special_tokens, **kwargs)
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