369 lines
15 KiB
Python
369 lines
15 KiB
Python
# Copyright (c) 2019, 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 torch
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import torch.nn as nn
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import math
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import librosa
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from .perturb import AudioAugmentor
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from .segment import AudioSegment
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from apex import amp
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def audio_from_file(file_path, offset=0, duration=0, trim=False, target_sr=16000,
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device=torch.device('cuda')):
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audio = AudioSegment.from_file(file_path,
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target_sr=target_sr,
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int_values=False,
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offset=offset, duration=duration, trim=trim)
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samples=torch.tensor(audio.samples, dtype=torch.float, device=device)
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num_samples = torch.tensor(samples.shape[0], device=device).int()
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return (samples.unsqueeze(0), num_samples.unsqueeze(0))
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class WaveformFeaturizer(object):
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def __init__(self, input_cfg, augmentor=None):
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self.augmentor = augmentor if augmentor is not None else AudioAugmentor()
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self.cfg = input_cfg
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def max_augmentation_length(self, length):
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return self.augmentor.max_augmentation_length(length)
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def process(self, file_path, offset=0, duration=0, trim=False):
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audio = AudioSegment.from_file(file_path,
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target_sr=self.cfg['sample_rate'],
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int_values=self.cfg.get('int_values', False),
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offset=offset, duration=duration, trim=trim)
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return self.process_segment(audio)
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def process_segment(self, audio_segment):
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self.augmentor.perturb(audio_segment)
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return torch.tensor(audio_segment.samples, dtype=torch.float)
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@classmethod
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def from_config(cls, input_config, perturbation_configs=None):
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if perturbation_configs is not None:
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aa = AudioAugmentor.from_config(perturbation_configs)
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else:
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aa = None
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return cls(input_config, augmentor=aa)
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# @torch.jit.script
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# def normalize_batch_per_feature(x, seq_len):
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# x_mean = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device)
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# x_std = torch.zeros((seq_len.shape[0], x.shape[1]), 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, :, :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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# @torch.jit.script
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# def normalize_batch_all_features(x, seq_len):
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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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@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 SpectrogramFeatures(nn.Module):
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# For JIT. See https://pytorch.org/docs/stable/jit.html#python-defined-constants
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__constants__ = ["dither", "preemph", "n_fft", "hop_length", "win_length", "log", "frame_splicing", "window", "normalize", "pad_to", "max_duration", "do_normalize"]
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def __init__(self, sample_rate=8000, window_size=0.02, window_stride=0.01,
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n_fft=None,
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window="hamming", normalize="per_feature", log=True,
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dither=1e-5, pad_to=8, max_duration=16.7,
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frame_splicing=1):
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super(SpectrogramFeatures, self).__init__()
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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.win_length = int(sample_rate * window_size)
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self.hop_length = int(sample_rate * window_stride)
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self.n_fft = n_fft or 2 ** math.ceil(math.log2(self.win_length))
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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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self.window = window_tensor
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self.normalize = normalize
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self.log = log
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self.dither = dither
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self.pad_to = pad_to
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self.frame_splicing = frame_splicing
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max_length = 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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max_pad = 16 - (max_length % 16)
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self.max_length = max_length + max_pad
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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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@torch.no_grad()
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def forward(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 hasattr(self,'preemph') and self.preemph is not None:
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x = torch.cat((x[:, 0].unsqueeze(1), x[:, 1:] - self.preemph * x[:, :-1]),
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dim=1)
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# get spectrogram
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x = 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(torch.float))
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x = torch.sqrt(x.pow(2).sum(-1))
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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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x = splice_frames(x, self.frame_splicing)
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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, pad to multiple of `pad_to` (for efficiency)
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max_len = x.size(-1)
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mask = torch.arange(max_len, dtype=seq_len.dtype).to(seq_len.device).expand(x.size(0), max_len) >= seq_len.unsqueeze(1)
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x = x.masked_fill(mask.unsqueeze(1).to(device=x.device), 0)
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# TORCHSCRIPT: Is this del important? It breaks scripting
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#del mask
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pad_to = self.pad_to
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# TORCHSCRIPT: Cant have mixed types. Using pad_to < 0 for "max"
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if pad_to < 0:
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x = nn.functional.pad(x, (0, self.max_length - x.size(-1)))
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elif pad_to > 0:
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pad_amt = x.size(-1) % pad_to
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if pad_amt != 0:
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x = nn.functional.pad(x, (0, pad_to - pad_amt))
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return x.to(dtype)
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@classmethod
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def from_config(cls, cfg, log=False):
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return cls(sample_rate=cfg['sample_rate'], window_size=cfg['window_size'],
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window_stride=cfg['window_stride'],
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n_fft=cfg['n_fft'], window=cfg['window'],
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normalize=cfg['normalize'],
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max_duration=cfg.get('max_duration', 16.7),
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dither=cfg.get('dither', 1e-5), pad_to=cfg.get("pad_to", 0),
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frame_splicing=cfg.get("frame_splicing", 1), log=log)
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constant=1e-5
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class FilterbankFeatures(nn.Module):
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# For JIT. See https://pytorch.org/docs/stable/jit.html#python-defined-constants
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__constants__ = ["dither", "preemph", "n_fft", "hop_length", "win_length", "center", "log", "frame_splicing", "window", "normalize", "pad_to", "max_duration", "max_length"]
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def __init__(self, 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,
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nfilt=64, lowfreq=0, highfreq=None, log=True, dither=constant,
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pad_to=8,
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max_duration=16.7,
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frame_splicing=1):
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super(FilterbankFeatures, self).__init__()
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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.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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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.nfilt = nfilt
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self.preemph = preemph
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self.pad_to = pad_to
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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=nfilt, fmin=lowfreq,
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fmax=highfreq), dtype=torch.float).unsqueeze(0)
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# self.fb = filterbanks
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# self.window = window_tensor
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self.register_buffer("fb", filterbanks)
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self.register_buffer("window", window_tensor)
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# Calculate maximum sequence length (# frames)
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max_length = 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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max_pad = 16 - (max_length % 16)
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self.max_length = max_length + max_pad
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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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def forward(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((x[:, 0].unsqueeze(1), x[:, 1:] - self.preemph * x[:, :-1]),
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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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x = splice_frames(x, self.frame_splicing)
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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, pad to multiple of `pad_to` (for efficiency)
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max_len = x.size(-1)
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mask = torch.arange(max_len, dtype=seq_len.dtype).to(x.device).expand(x.size(0),
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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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# TORCHSCRIPT: Cant have mixed types. Using pad_to < 0 for "max"
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if self.pad_to < 0:
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x = nn.functional.pad(x, (0, self.max_length - x.size(-1)))
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elif self.pad_to > 0:
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pad_amt = x.size(-1) % self.pad_to
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# if pad_amt != 0:
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x = nn.functional.pad(x, (0, self.pad_to - pad_amt))
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return x # .to(dtype)
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@classmethod
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def from_config(cls, cfg, log=False):
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return cls(sample_rate=cfg['sample_rate'], window_size=cfg['window_size'],
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window_stride=cfg['window_stride'], n_fft=cfg['n_fft'],
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nfilt=cfg['features'], window=cfg['window'],
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normalize=cfg['normalize'],
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max_duration=cfg.get('max_duration', 16.7),
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dither=cfg['dither'], pad_to=cfg.get("pad_to", 0),
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frame_splicing=cfg.get("frame_splicing", 1), log=log)
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class FeatureFactory(object):
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featurizers = {
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"logfbank": FilterbankFeatures,
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"fbank": FilterbankFeatures,
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"stft": SpectrogramFeatures,
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"logspect": SpectrogramFeatures,
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"logstft": SpectrogramFeatures
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}
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def __init__(self):
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pass
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@classmethod
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def from_config(cls, cfg):
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feat_type = cfg.get('feat_type', "logspect")
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featurizer = cls.featurizers[feat_type]
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#return featurizer.from_config(cfg, log="log" in cfg['feat_type'])
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return featurizer.from_config(cfg, log="log" in feat_type)
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