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import torch |
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import torch.nn as nn |
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from audio_denoiser.modules.Permute import Permute |
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from audio_denoiser.modules.SimpleRoberta import SimpleRoberta |
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from audio_denoiser.modules.SpectrogramScaler import SpectrogramScaler |
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import json |
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class AudioNoiseModel(nn.Module): |
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def __init__(self, config: dict): |
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super(AudioNoiseModel, self).__init__() |
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self.config = config |
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scaler_dict = config["scaler"] |
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self.scaler = SpectrogramScaler.from_dict(scaler_dict) |
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self.in_channels = config.get("in_channels", 257) |
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self.roberta_hidden_size = config.get("roberta_hidden_size", 768) |
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self.model1 = nn.Sequential( |
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nn.Conv1d(self.in_channels, 1024, kernel_size=1), |
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nn.ELU(), |
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nn.Conv1d(1024, 1024, kernel_size=1), |
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nn.ELU(), |
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nn.Conv1d(1024, self.in_channels, kernel_size=1), |
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) |
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self.model2 = nn.Sequential( |
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Permute(0, 2, 1), |
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nn.Linear(self.in_channels, self.roberta_hidden_size), |
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SimpleRoberta(num_hidden_layers=5, hidden_size=self.roberta_hidden_size), |
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nn.Linear(self.roberta_hidden_size, self.in_channels), |
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Permute(0, 2, 1), |
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) |
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@property |
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def sample_rate(self) -> int: |
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return self.config.get("sample_rate", 16000) |
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@property |
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def n_fft(self) -> int: |
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return self.config.get("n_fft", 512) |
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@property |
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def num_frames(self) -> int: |
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return self.config.get("num_frames", 32) |
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def forward(self, x, use_scaler: bool = False, out_scale: float = 1.0): |
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if use_scaler: |
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x = self.scaler(x) |
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x1 = self.model1(x) |
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x2 = self.model2(x) |
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x = x1 + x2 |
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return x * out_scale |
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def load_audio_denosier_model(dir_path: str, device) -> AudioNoiseModel: |
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config = json.load(open(f"{dir_path}/config.json", "r")) |
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model = AudioNoiseModel(config) |
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model.load_state_dict(torch.load(f"{dir_path}/pytorch_model.bin")) |
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model.to(device) |
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model.model1.to(device) |
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model.model2.to(device) |
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return model |
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