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Running
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Zero
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import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pandas as pd
import torchaudio
import random
import itertools
import numpy as np
import numpy as np
def normalize_wav(waveform):
waveform = waveform - torch.mean(waveform)
waveform = waveform / (torch.max(torch.abs(waveform)) + 1e-8)
return waveform * 0.5
def pad_wav(waveform, segment_length):
waveform_length = len(waveform)
if segment_length is None or waveform_length == segment_length:
return waveform
elif waveform_length > segment_length:
return waveform[:segment_length]
else:
padded_wav = torch.zeros(segment_length - waveform_length).to(waveform.device)
waveform = torch.cat([waveform, padded_wav])
return waveform
def read_wav_file(filename, duration_sec):
info = torchaudio.info(filename)
sample_rate = info.sample_rate
# Calculate the number of frames corresponding to the desired duration
num_frames = int(sample_rate * duration_sec)
waveform, sr = torchaudio.load(filename, num_frames=num_frames) # Faster!!!
if waveform.shape[0] == 2: ## Stereo audio
resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=44100)
resampled_waveform = resampler(waveform)
# print(resampled_waveform.shape)
padded_left = pad_wav(
resampled_waveform[0], int(44100 * duration_sec)
) ## We pad left and right seperately
padded_right = pad_wav(resampled_waveform[1], int(44100 * duration_sec))
return torch.stack([padded_left, padded_right])
else:
waveform = torchaudio.functional.resample(
waveform, orig_freq=sr, new_freq=44100
)[0]
waveform = pad_wav(waveform, int(44100 * duration_sec)).unsqueeze(0)
return waveform
class DPOText2AudioDataset(Dataset):
def __init__(
self,
dataset,
prefix,
text_column,
audio_w_column,
audio_l_column,
duration,
num_examples=-1,
):
inputs = list(dataset[text_column])
self.inputs = [prefix + inp for inp in inputs]
self.audios_w = list(dataset[audio_w_column])
self.audios_l = list(dataset[audio_l_column])
self.durations = list(dataset[duration])
self.indices = list(range(len(self.inputs)))
self.mapper = {}
for index, audio_w, audio_l, duration, text in zip(
self.indices, self.audios_w, self.audios_l, self.durations, inputs
):
self.mapper[index] = [audio_w, audio_l, duration, text]
if num_examples != -1:
self.inputs, self.audios_w, self.audios_l, self.durations = (
self.inputs[:num_examples],
self.audios_w[:num_examples],
self.audios_l[:num_examples],
self.durations[:num_examples],
)
self.indices = self.indices[:num_examples]
def __len__(self):
return len(self.inputs)
def get_num_instances(self):
return len(self.inputs)
def __getitem__(self, index):
s1, s2, s3, s4, s5 = (
self.inputs[index],
self.audios_w[index],
self.audios_l[index],
self.durations[index],
self.indices[index],
)
return s1, s2, s3, s4, s5
def collate_fn(self, data):
dat = pd.DataFrame(data)
return [dat[i].tolist() for i in dat]
class Text2AudioDataset(Dataset):
def __init__(
self, dataset, prefix, text_column, audio_column, duration, num_examples=-1
):
inputs = list(dataset[text_column])
self.inputs = [prefix + inp for inp in inputs]
self.audios = list(dataset[audio_column])
self.durations = list(dataset[duration])
self.indices = list(range(len(self.inputs)))
self.mapper = {}
for index, audio, duration, text in zip(
self.indices, self.audios, self.durations, inputs
):
self.mapper[index] = [audio, text, duration]
if num_examples != -1:
self.inputs, self.audios, self.durations = (
self.inputs[:num_examples],
self.audios[:num_examples],
self.durations[:num_examples],
)
self.indices = self.indices[:num_examples]
def __len__(self):
return len(self.inputs)
def get_num_instances(self):
return len(self.inputs)
def __getitem__(self, index):
s1, s2, s3, s4 = (
self.inputs[index],
self.audios[index],
self.durations[index],
self.indices[index],
)
return s1, s2, s3, s4
def collate_fn(self, data):
dat = pd.DataFrame(data)
return [dat[i].tolist() for i in dat]
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