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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("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, config):
self.sample_rate = config.audio_sample_rate
self.num_mel_bins = config.num_mel_bins
self.target_length = config.target_length
self.audio_mean = config.audio_mean
self.audio_std = config.audio_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[0])
return self.norm(waveform_melspec)
def waveform2melspec(self, audio_data):
max_len = self.target_length * self.sample_rate // 100
if audio_data.shape[-1] > max_len:
mel = self.get_mel(audio_data)
# 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, :]
# stack
mel_fusion = torch.stack([mel_chunk_front, mel_chunk_middle, mel_chunk_back], dim=0)
elif audio_data.shape[-1] < max_len: # padding if too short
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,
)
mel = self.get_mel(audio_data)
mel_fusion = torch.stack([mel, mel, mel], dim=0)
else: # if equal
mel = self.get_mel(audio_data)
mel_fusion = torch.stack([mel, mel, mel], dim=0)
# twice check
p = self.target_length - mel_fusion.shape[1]
# if abs(p) / self.target_length > 0.2:
# logging.warning(
# "Large gap between audio n_frames(%d) and "
# "target_length (%d). Is the audio_target_length "
# "setting correct?",
# mel_fusion.shape[1],
# self.target_length,
# )
# cut and pad
if p > 0:
m = torch.nn.ZeroPad2d((0, 0, 0, p))
mel_fusion = m(mel_fusion)
elif p < 0:
mel_fusion = mel_fusion[:, 0: self.target_length, :]
mel_fusion = mel_fusion.transpose(1, 2) # [3, target_length, mel_bins] -> [3, mel_bins, target_length]
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.unsqueeze(0),
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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