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