# MIT License # # Copyright (c) 2021 CNRS # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from functools import lru_cache from typing import Optional import torch import torch.nn as nn from torchaudio.transforms import MFCC from pyannote.audio.core.model import Model from pyannote.audio.core.task import Task from pyannote.audio.models.blocks.pooling import StatsPool from pyannote.audio.models.blocks.sincnet import SincNet from pyannote.audio.utils.params import merge_dict from pyannote.audio.utils.receptive_field import ( multi_conv_num_frames, multi_conv_receptive_field_center, multi_conv_receptive_field_size, ) class XVectorMFCC(Model): MFCC_DEFAULTS = {"n_mfcc": 40, "dct_type": 2, "norm": "ortho", "log_mels": False} def __init__( self, sample_rate: int = 16000, num_channels: int = 1, mfcc: Optional[dict] = None, dimension: int = 512, task: Optional[Task] = None, ): super().__init__(sample_rate=sample_rate, num_channels=num_channels, task=task) mfcc = merge_dict(self.MFCC_DEFAULTS, mfcc) mfcc["sample_rate"] = sample_rate self.save_hyperparameters("mfcc", "dimension") self.mfcc = MFCC(**self.hparams.mfcc) self.tdnns = nn.ModuleList() in_channel = self.hparams.mfcc["n_mfcc"] out_channels = [512, 512, 512, 512, 1500] self.kernel_size = [5, 3, 3, 1, 1] self.dilation = [1, 2, 3, 1, 1] self.padding = [0, 0, 0, 0, 0] self.stride = [1, 1, 1, 1, 1] for out_channel, kernel_size, dilation in zip( out_channels, self.kernel_size, self.dilation ): self.tdnns.extend( [ nn.Conv1d( in_channels=in_channel, out_channels=out_channel, kernel_size=kernel_size, dilation=dilation, ), nn.LeakyReLU(), nn.BatchNorm1d(out_channel), ] ) in_channel = out_channel self.stats_pool = StatsPool() self.embedding = nn.Linear(in_channel * 2, self.hparams.dimension) @property def dimension(self) -> int: """Dimension of output""" return self.hparams.dimension @lru_cache def num_frames(self, num_samples: int) -> int: """Compute number of output frames Parameters ---------- num_samples : int Number of input samples. Returns ------- num_frames : int Number of output frames. """ hop_length = self.mfcc.MelSpectrogram.spectrogram.hop_length n_fft = self.mfcc.MelSpectrogram.spectrogram.n_fft center = self.mfcc.MelSpectrogram.spectrogram.center if center: num_frames = 1 + num_samples // hop_length else: num_frames = 1 + (num_samples - n_fft) // hop_length return multi_conv_num_frames( num_frames, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, dilation=self.dilation, ) def receptive_field_size(self, num_frames: int = 1) -> int: """Compute size of receptive field Parameters ---------- num_frames : int, optional Number of frames in the output signal Returns ------- receptive_field_size : int Receptive field size. """ receptive_field_size = multi_conv_receptive_field_size( num_frames, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, dilation=self.dilation, ) hop_length = self.mfcc.MelSpectrogram.spectrogram.hop_length n_fft = self.mfcc.MelSpectrogram.spectrogram.n_fft return n_fft + (receptive_field_size - 1) * hop_length def receptive_field_center(self, frame: int = 0) -> int: """Compute center of receptive field Parameters ---------- frame : int, optional Frame index Returns ------- receptive_field_center : int Index of receptive field center. """ receptive_field_center = multi_conv_receptive_field_center( frame, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, dilation=self.dilation, ) hop_length = self.mfcc.MelSpectrogram.spectrogram.hop_length n_fft = self.mfcc.MelSpectrogram.spectrogram.n_fft center = self.mfcc.MelSpectrogram.spectrogram.center if center: return receptive_field_center * hop_length else: return receptive_field_center * hop_length + n_fft // 2 def forward( self, waveforms: torch.Tensor, weights: Optional[torch.Tensor] = None ) -> torch.Tensor: """ Parameters ---------- waveforms : torch.Tensor Batch of waveforms with shape (batch, channel, sample) weights : torch.Tensor, optional Batch of weights with shape (batch, frame). """ outputs = self.mfcc(waveforms).squeeze(dim=1) for block in self.tdnns: outputs = block(outputs) outputs = self.stats_pool(outputs, weights=weights) return self.embedding(outputs) class XVectorSincNet(Model): SINCNET_DEFAULTS = {"stride": 10} def __init__( self, sample_rate: int = 16000, num_channels: int = 1, sincnet: Optional[dict] = None, dimension: int = 512, task: Optional[Task] = None, ): super().__init__(sample_rate=sample_rate, num_channels=num_channels, task=task) sincnet = merge_dict(self.SINCNET_DEFAULTS, sincnet) sincnet["sample_rate"] = sample_rate self.save_hyperparameters("sincnet", "dimension") self.sincnet = SincNet(**self.hparams.sincnet) in_channel = 60 self.tdnns = nn.ModuleList() out_channels = [512, 512, 512, 512, 1500] self.kernel_size = [5, 3, 3, 1, 1] self.dilation = [1, 2, 3, 1, 1] self.padding = [0, 0, 0, 0, 0] self.stride = [1, 1, 1, 1, 1] for out_channel, kernel_size, dilation in zip( out_channels, self.kernel_size, self.dilation ): self.tdnns.extend( [ nn.Conv1d( in_channels=in_channel, out_channels=out_channel, kernel_size=kernel_size, dilation=dilation, ), nn.LeakyReLU(), nn.BatchNorm1d(out_channel), ] ) in_channel = out_channel self.stats_pool = StatsPool() self.embedding = nn.Linear(in_channel * 2, self.hparams.dimension) @property def dimension(self) -> int: """Dimension of output""" return self.hparams.dimension @lru_cache def num_frames(self, num_samples: int) -> int: """Compute number of output frames Parameters ---------- num_samples : int Number of input samples. Returns ------- num_frames : int Number of output frames. """ num_frames = self.sincnet.num_frames(num_samples) return multi_conv_num_frames( num_frames, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, dilation=self.dilation, ) def receptive_field_size(self, num_frames: int = 1) -> int: """Compute size of receptive field Parameters ---------- num_frames : int, optional Number of frames in the output signal Returns ------- receptive_field_size : int Receptive field size. """ receptive_field_size = multi_conv_receptive_field_size( num_frames, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, dilation=self.dilation, ) return self.sincnet.receptive_field_size(num_frames=receptive_field_size) def receptive_field_center(self, frame: int = 0) -> int: """Compute center of receptive field Parameters ---------- frame : int, optional Frame index Returns ------- receptive_field_center : int Index of receptive field center. """ receptive_field_center = multi_conv_receptive_field_center( frame, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, dilation=self.dilation, ) return self.sincnet.receptive_field_center(frame=receptive_field_center) def forward( self, waveforms: torch.Tensor, weights: Optional[torch.Tensor] = None ) -> torch.Tensor: """ Parameters ---------- waveforms : torch.Tensor Batch of waveforms with shape (batch, channel, sample) weights : torch.Tensor, optional Batch of weights with shape (batch, frame). """ outputs = self.sincnet(waveforms).squeeze(dim=1) for tdnn in self.tdnns: outputs = tdnn(outputs) outputs = self.stats_pool(outputs, weights=weights) return self.embedding(outputs)