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import torch |
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from torch import nn |
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from TTS.encoder.models.base_encoder import BaseEncoder |
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class LSTMWithProjection(nn.Module): |
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def __init__(self, input_size, hidden_size, proj_size): |
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super().__init__() |
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self.input_size = input_size |
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self.hidden_size = hidden_size |
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self.proj_size = proj_size |
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self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True) |
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self.linear = nn.Linear(hidden_size, proj_size, bias=False) |
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def forward(self, x): |
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self.lstm.flatten_parameters() |
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o, (_, _) = self.lstm(x) |
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return self.linear(o) |
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class LSTMWithoutProjection(nn.Module): |
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def __init__(self, input_dim, lstm_dim, proj_dim, num_lstm_layers): |
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super().__init__() |
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self.lstm = nn.LSTM(input_size=input_dim, hidden_size=lstm_dim, num_layers=num_lstm_layers, batch_first=True) |
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self.linear = nn.Linear(lstm_dim, proj_dim, bias=True) |
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self.relu = nn.ReLU() |
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def forward(self, x): |
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_, (hidden, _) = self.lstm(x) |
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return self.relu(self.linear(hidden[-1])) |
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class LSTMSpeakerEncoder(BaseEncoder): |
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def __init__( |
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self, |
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input_dim, |
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proj_dim=256, |
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lstm_dim=768, |
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num_lstm_layers=3, |
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use_lstm_with_projection=True, |
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use_torch_spec=False, |
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audio_config=None, |
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): |
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super().__init__() |
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self.use_lstm_with_projection = use_lstm_with_projection |
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self.use_torch_spec = use_torch_spec |
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self.audio_config = audio_config |
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self.proj_dim = proj_dim |
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layers = [] |
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if use_lstm_with_projection: |
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layers.append(LSTMWithProjection(input_dim, lstm_dim, proj_dim)) |
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for _ in range(num_lstm_layers - 1): |
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layers.append(LSTMWithProjection(proj_dim, lstm_dim, proj_dim)) |
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self.layers = nn.Sequential(*layers) |
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else: |
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self.layers = LSTMWithoutProjection(input_dim, lstm_dim, proj_dim, num_lstm_layers) |
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self.instancenorm = nn.InstanceNorm1d(input_dim) |
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if self.use_torch_spec: |
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self.torch_spec = self.get_torch_mel_spectrogram_class(audio_config) |
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else: |
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self.torch_spec = None |
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self._init_layers() |
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def _init_layers(self): |
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for name, param in self.layers.named_parameters(): |
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if "bias" in name: |
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nn.init.constant_(param, 0.0) |
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elif "weight" in name: |
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nn.init.xavier_normal_(param) |
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def forward(self, x, l2_norm=True): |
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"""Forward pass of the model. |
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Args: |
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x (Tensor): Raw waveform signal or spectrogram frames. If input is a waveform, `torch_spec` must be `True` |
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to compute the spectrogram on-the-fly. |
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l2_norm (bool): Whether to L2-normalize the outputs. |
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Shapes: |
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- x: :math:`(N, 1, T_{in})` or :math:`(N, D_{spec}, T_{in})` |
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""" |
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with torch.no_grad(): |
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with torch.cuda.amp.autocast(enabled=False): |
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if self.use_torch_spec: |
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x.squeeze_(1) |
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x = self.torch_spec(x) |
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x = self.instancenorm(x).transpose(1, 2) |
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d = self.layers(x) |
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if self.use_lstm_with_projection: |
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d = d[:, -1] |
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if l2_norm: |
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d = torch.nn.functional.normalize(d, p=2, dim=1) |
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return d |
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