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