302 lines
11 KiB
Python
302 lines
11 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import random
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import librosa
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import torch
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import torch.nn as nn
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class BaseFeatures(nn.Module):
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"""Base class for GPU accelerated audio preprocessing."""
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__constants__ = ["pad_align", "pad_to_max_duration", "max_len"]
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def __init__(self, pad_align, pad_to_max_duration, max_duration,
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sample_rate, window_size, window_stride, spec_augment=None,
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cutout_augment=None):
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super(BaseFeatures, self).__init__()
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self.pad_align = pad_align
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self.pad_to_max_duration = pad_to_max_duration
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self.win_length = int(sample_rate * window_size) # frame size
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self.hop_length = int(sample_rate * window_stride)
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# Calculate maximum sequence length (# frames)
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if pad_to_max_duration:
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self.max_len = 1 + math.ceil(
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(max_duration * sample_rate - self.win_length) / self.hop_length
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)
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if spec_augment is not None:
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self.spec_augment = SpecAugment(**spec_augment)
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else:
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self.spec_augment = None
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if cutout_augment is not None:
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self.cutout_augment = CutoutAugment(**cutout_augment)
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else:
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self.cutout_augment = None
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@torch.no_grad()
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def calculate_features(self, audio, audio_lens):
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return audio, audio_lens
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def __call__(self, audio, audio_lens):
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dtype = audio.dtype
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audio = audio.float()
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feat, feat_lens = self.calculate_features(audio, audio_lens)
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feat = self.apply_padding(feat)
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if self.cutout_augment is not None:
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feat = self.cutout_augment(feat)
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if self.spec_augment is not None:
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feat = self.spec_augment(feat)
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feat = feat.to(dtype)
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return feat, feat_lens
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def apply_padding(self, x):
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if self.pad_to_max_duration:
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x_size = max(x.size(-1), self.max_len)
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else:
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x_size = x.size(-1)
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if self.pad_align > 0:
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pad_amt = x_size % self.pad_align
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else:
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pad_amt = 0
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padded_len = x_size + (self.pad_align - pad_amt if pad_amt > 0 else 0)
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return nn.functional.pad(x, (0, padded_len - x.size(-1)))
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class SpecAugment(nn.Module):
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"""Spec augment. refer to https://arxiv.org/abs/1904.08779
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"""
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def __init__(self, freq_masks=0, min_freq=0, max_freq=10, time_masks=0,
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min_time=0, max_time=10):
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super(SpecAugment, self).__init__()
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assert 0 <= min_freq <= max_freq
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assert 0 <= min_time <= max_time
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self.freq_masks = freq_masks
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self.min_freq = min_freq
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self.max_freq = max_freq
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self.time_masks = time_masks
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self.min_time = min_time
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self.max_time = max_time
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@torch.no_grad()
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def forward(self, x):
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sh = x.shape
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mask = torch.zeros(x.shape, dtype=torch.bool, device=x.device)
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for idx in range(sh[0]):
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for _ in range(self.freq_masks):
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w = torch.randint(self.min_freq, self.max_freq + 1, size=(1,)).item()
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f0 = torch.randint(0, max(1, sh[1] - w), size=(1,))
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mask[idx, f0:f0+w] = 1
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for _ in range(self.time_masks):
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w = torch.randint(self.min_time, self.max_time + 1, size=(1,)).item()
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t0 = torch.randint(0, max(1, sh[2] - w), size=(1,))
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mask[idx, :, t0:t0+w] = 1
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return x.masked_fill(mask, 0)
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class CutoutAugment(nn.Module):
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"""Cutout. refer to https://arxiv.org/pdf/1708.04552.pdf
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"""
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def __init__(self, masks=0, min_freq=20, max_freq=20, min_time=5, max_time=5):
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super(CutoutAugment, self).__init__()
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assert 0 <= min_freq <= max_freq
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assert 0 <= min_time <= max_time
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self.masks = masks
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self.min_freq = min_freq
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self.max_freq = max_freq
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self.min_time = min_time
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self.max_time = max_time
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@torch.no_grad()
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def forward(self, x):
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sh = x.shape
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mask = torch.zeros(x.shape, dtype=torch.bool, device=x.device)
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for idx in range(sh[0]):
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for i in range(self.masks):
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w = torch.randint(self.min_freq, self.max_freq + 1, size=(1,)).item()
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h = torch.randint(self.min_time, self.max_time + 1, size=(1,)).item()
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f0 = int(random.uniform(0, sh[1] - w))
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t0 = int(random.uniform(0, sh[2] - h))
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mask[idx, f0:f0+w, t0:t0+h] = 1
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return x.masked_fill(mask, 0)
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@torch.jit.script
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def normalize_batch(x, seq_len, normalize_type: str):
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# print ("normalize_batch: x, seq_len, shapes: ", x.shape, seq_len, seq_len.shape)
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if normalize_type == "per_feature":
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x_mean = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype,
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device=x.device)
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x_std = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype,
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device=x.device)
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for i in range(x.shape[0]):
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x_mean[i, :] = x[i, :, :seq_len[i]].mean(dim=1)
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x_std[i, :] = x[i, :, :seq_len[i]].std(dim=1)
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# make sure x_std is not zero
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x_std += 1e-5
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return (x - x_mean.unsqueeze(2)) / x_std.unsqueeze(2)
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elif normalize_type == "all_features":
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x_mean = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device)
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x_std = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device)
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for i in range(x.shape[0]):
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x_mean[i] = x[i, :, :int(seq_len[i])].mean()
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x_std[i] = x[i, :, :int(seq_len[i])].std()
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# make sure x_std is not zero
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x_std += 1e-5
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return (x - x_mean.view(-1, 1, 1)) / x_std.view(-1, 1, 1)
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else:
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return x
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@torch.jit.script
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def splice_frames(x, frame_splicing: int):
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""" Stacks frames together across feature dim
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input is batch_size, feature_dim, num_frames
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output is batch_size, feature_dim*frame_splicing, num_frames
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"""
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seq = [x]
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# TORCHSCRIPT: JIT doesnt like range(start, stop)
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for n in range(frame_splicing - 1):
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seq.append(torch.cat([x[:, :, :n + 1], x[:, :, n + 1:]], dim=2))
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return torch.cat(seq, dim=1)
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class FilterbankFeatures(BaseFeatures):
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# For JIT, https://pytorch.org/docs/stable/jit.html#python-defined-constants
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__constants__ = ["dither", "preemph", "n_fft", "hop_length", "win_length",
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"log", "frame_splicing", "normalize"]
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# torchscript: "center" removed due to a bug
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def __init__(self, spec_augment=None, cutout_augment=None,
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sample_rate=8000, window_size=0.02, window_stride=0.01,
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window="hamming", normalize="per_feature", n_fft=None,
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preemph=0.97, n_filt=64, lowfreq=0, highfreq=None, log=True,
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dither=1e-5, pad_align=8, pad_to_max_duration=False,
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max_duration=float('inf'), frame_splicing=1):
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super(FilterbankFeatures, self).__init__(
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pad_align=pad_align, pad_to_max_duration=pad_to_max_duration,
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max_duration=max_duration, sample_rate=sample_rate,
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window_size=window_size, window_stride=window_stride,
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spec_augment=spec_augment, cutout_augment=cutout_augment)
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torch_windows = {
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'hann': torch.hann_window,
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'hamming': torch.hamming_window,
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'blackman': torch.blackman_window,
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'bartlett': torch.bartlett_window,
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'none': None,
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}
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self.n_fft = n_fft or 2 ** math.ceil(math.log2(self.win_length))
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self.normalize = normalize
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self.log = log
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#TORCHSCRIPT: Check whether or not we need this
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self.dither = dither
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self.frame_splicing = frame_splicing
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self.n_filt = n_filt
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self.preemph = preemph
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highfreq = highfreq or sample_rate / 2
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window_fn = torch_windows.get(window, None)
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window_tensor = window_fn(self.win_length,
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periodic=False) if window_fn else None
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filterbanks = torch.tensor(
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librosa.filters.mel(sample_rate, self.n_fft, n_mels=n_filt,
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fmin=lowfreq, fmax=highfreq),
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dtype=torch.float).unsqueeze(0)
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# torchscript
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self.register_buffer("fb", filterbanks)
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self.register_buffer("window", window_tensor)
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def get_seq_len(self, seq_len):
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return torch.ceil(seq_len.to(dtype=torch.float) / self.hop_length).to(
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dtype=torch.int)
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# do stft
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# TORCHSCRIPT: center removed due to bug
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def stft(self, x):
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return torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length,
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win_length=self.win_length,
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window=self.window.to(dtype=torch.float))
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@torch.no_grad()
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def calculate_features(self, x, seq_len):
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dtype = x.dtype
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seq_len = self.get_seq_len(seq_len)
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# dither
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if self.dither > 0:
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x += self.dither * torch.randn_like(x)
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# do preemphasis
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if self.preemph is not None:
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x = torch.cat(
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(x[:, 0].unsqueeze(1), x[:, 1:] - self.preemph * x[:, :-1]), dim=1)
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x = self.stft(x)
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# get power spectrum
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x = x.pow(2).sum(-1)
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# dot with filterbank energies
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x = torch.matmul(self.fb.to(x.dtype), x)
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# log features if required
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if self.log:
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x = torch.log(x + 1e-20)
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# frame splicing if required
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if self.frame_splicing > 1:
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raise ValueError('Frame splicing not supported')
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# normalize if required
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x = normalize_batch(x, seq_len, normalize_type=self.normalize)
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# mask to zero any values beyond seq_len in batch,
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# pad to multiple of `pad_align` (for efficiency)
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max_len = x.size(-1)
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mask = torch.arange(max_len, dtype=seq_len.dtype, device=x.device)
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mask = mask.expand(x.size(0), max_len) >= seq_len.unsqueeze(1)
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x = x.masked_fill(mask.unsqueeze(1), 0)
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# TORCHSCRIPT: Is this del important? It breaks scripting
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# del mask
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return x.to(dtype), seq_len
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