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reuse monotonic_align lib
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build
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core.c
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*.so
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# Copied from
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# https://github.com/shivammehta25/Matcha-TTS/blob/main/matcha/utils/monotonic_align/__init__.py
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import numpy as np
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import torch
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from matcha.monotonic_align.core import maximum_path_c
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def maximum_path(value, mask):
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"""Cython optimised version.
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value: [b, t_x, t_y]
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mask: [b, t_x, t_y]
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"""
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value = value * mask
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device = value.device
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dtype = value.dtype
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value = value.data.cpu().numpy().astype(np.float32)
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path = np.zeros_like(value).astype(np.int32)
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mask = mask.data.cpu().numpy()
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t_x_max = mask.sum(1)[:, 0].astype(np.int32)
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t_y_max = mask.sum(2)[:, 0].astype(np.int32)
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maximum_path_c(path, value, t_x_max, t_y_max)
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return torch.from_numpy(path).to(device=device, dtype=dtype)
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# Copied from
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# https://github.com/shivammehta25/Matcha-TTS/blob/main/matcha/utils/monotonic_align/core.pyx
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import numpy as np
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cimport cython
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cimport numpy as np
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from cython.parallel import prange
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil:
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cdef int x
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cdef int y
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cdef float v_prev
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cdef float v_cur
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cdef float tmp
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cdef int index = t_x - 1
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for y in range(t_y):
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for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
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if x == y:
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v_cur = max_neg_val
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else:
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v_cur = value[x, y-1]
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if x == 0:
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if y == 0:
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v_prev = 0.
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else:
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v_prev = max_neg_val
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else:
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v_prev = value[x-1, y-1]
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value[x, y] = max(v_cur, v_prev) + value[x, y]
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for y in range(t_y - 1, -1, -1):
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path[index, y] = 1
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if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]):
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index = index - 1
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil:
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cdef int b = values.shape[0]
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cdef int i
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for i in prange(b, nogil=True):
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maximum_path_each(paths[i], values[i], t_xs[i], t_ys[i], max_neg_val)
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# Copied from
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# https://github.com/shivammehta25/Matcha-TTS/blob/main/matcha/utils/monotonic_align/setup.py
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from distutils.core import setup
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import numpy
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from Cython.Build import cythonize
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setup(
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name="monotonic_align",
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ext_modules=cythonize("core.pyx"),
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include_dirs=[numpy.get_include()],
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)
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