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https://github.com/neonbjb/tortoise-tts.git
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72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
from time import time
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import numpy as np
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class Timer():
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'''
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A simple timing helper class that measures the duration between the last time "get_split" was
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called and the present time.
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Note that the first split is created when this object is initialized
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'''
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def __init__(self):
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self.last_split = time()
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def get_split(self):
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t = time()
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split_ = t - self.last_split
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self.last_split = t
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return split_
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def get_squared_euclidean_distance_matrix_np(m1, m2):
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'''
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Using trick mentioned in: https://www.robots.ox.ac.uk/~albanie/notes/Euclidean_distance_trick.pdf
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Note that this returns the squared distance matrix (euclidean distance obtained simply by sqrt of these values)
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params::
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m1: Matrix 1 (N x M)
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m2: Matrix 2 (K x M)
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returns::
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(N X K) Matrix of pairwise distances between m1 and m2
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'''
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return np.sum(m1 ** 2, axis=1)[:, np.newaxis] - (2. * np.matmul(m1, m2.T)) + np.sum(m2 ** 2, axis=1)[np.newaxis]
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def uniform_resample(data, current_freq, target_freq):
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# Helper for uniform resampling (upsample/downsample)
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sampling_ratio = target_freq / current_freq
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data_ss_ixs = np.minimum(np.round(np.arange(0, data.shape[0], 1 / sampling_ratio)).astype(int), data.shape[0] - 1)
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resampled_data = data[data_ss_ixs]
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return resampled_data
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def rescale_range(values, in_min, in_max, out_min, out_max, return_inverse_transform_parameters=False):
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'''
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Helper for rescaling values from (in_min,in_max) to (out_min,out_max) linearly
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'''
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inverse_transform_parameters = {"sub_1": out_min,
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"div_1": out_max - out_min,
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"add_1": in_min,
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"mult_1": in_max - in_min}
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values = np.clip(values, a_min=in_min, a_max=in_max)
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values = (values - in_min) / (in_max - in_min)
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values = (values * (out_max - out_min)) + out_min
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if return_inverse_transform_parameters:
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return values, inverse_transform_parameters
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else:
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return values
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def split_clip_into_segments(clip, chunk_time=10, min_clip_time=0, fs=22050):
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dur = len(clip) / fs
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chunks = np.round(dur / chunk_time).astype(int)
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chunk_samples = chunk_time * fs
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clips = []
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for n in range(chunks):
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n_clip = clip[n * chunk_samples:(n + 1) * chunk_samples]
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if len(n_clip)/fs >= min_clip_time:
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clips.append(n_clip)
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return clips
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