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import bisect
import numpy as np
import torch
def _pad_data(x, length):
_pad = 0
assert x.ndim == 1
return np.pad(x, (0, length - x.shape[0]), mode="constant", constant_values=_pad)
def prepare_data(inputs):
max_len = max((len(x) for x in inputs))
return np.stack([_pad_data(x, max_len) for x in inputs])
def _pad_tensor(x, length):
_pad = 0.0
assert x.ndim == 2
x = np.pad(x, [[0, 0], [0, length - x.shape[1]]], mode="constant", constant_values=_pad)
return x
def prepare_tensor(inputs, out_steps):
max_len = max((x.shape[1] for x in inputs))
remainder = max_len % out_steps
pad_len = max_len + (out_steps - remainder) if remainder > 0 else max_len
return np.stack([_pad_tensor(x, pad_len) for x in inputs])
def _pad_stop_target(x: np.ndarray, length: int, pad_val=1) -> np.ndarray:
"""Pad stop target array.
Args:
x (np.ndarray): Stop target array.
length (int): Length after padding.
pad_val (int, optional): Padding value. Defaults to 1.
Returns:
np.ndarray: Padded stop target array.
"""
assert x.ndim == 1
return np.pad(x, (0, length - x.shape[0]), mode="constant", constant_values=pad_val)
def prepare_stop_target(inputs, out_steps):
"""Pad row vectors with 1."""
max_len = max((x.shape[0] for x in inputs))
remainder = max_len % out_steps
pad_len = max_len + (out_steps - remainder) if remainder > 0 else max_len
return np.stack([_pad_stop_target(x, pad_len) for x in inputs])
def pad_per_step(inputs, pad_len):
return np.pad(inputs, [[0, 0], [0, 0], [0, pad_len]], mode="constant", constant_values=0.0)
def get_length_balancer_weights(items: list, num_buckets=10):
# get all durations
audio_lengths = np.array([item["audio_length"] for item in items])
# create the $num_buckets buckets classes based in the dataset max and min length
max_length = int(max(audio_lengths))
min_length = int(min(audio_lengths))
step = int((max_length - min_length) / num_buckets) + 1
buckets_classes = [i + step for i in range(min_length, (max_length - step) + num_buckets + 1, step)]
# add each sample in their respective length bucket
buckets_names = np.array(
[buckets_classes[bisect.bisect_left(buckets_classes, item["audio_length"])] for item in items]
)
# count and compute the weights_bucket for each sample
unique_buckets_names = np.unique(buckets_names).tolist()
bucket_ids = [unique_buckets_names.index(l) for l in buckets_names]
bucket_count = np.array([len(np.where(buckets_names == l)[0]) for l in unique_buckets_names])
weight_bucket = 1.0 / bucket_count
dataset_samples_weight = np.array([weight_bucket[l] for l in bucket_ids])
# normalize
dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight)
return torch.from_numpy(dataset_samples_weight).float()
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