Upload filtravimas.py

filtravimas.py

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+import os
+import torch
+import torchaudio
+import torch.nn as nn
+import torchaudio.transforms as T
+import noisereduce as nr
+import numpy as np
+from asteroid.models import DCCRNet
+
+TEMP_DIR = "temp_filtered"
+OUTPUT_PATH = os.path.join(TEMP_DIR, "ivestis.wav")
+os.makedirs(TEMP_DIR, exist_ok=True)
+
+class WaveUNet(nn.Module):
+    def __init__(self):
+        super(WaveUNet, self).__init__()
+        self.encoder = nn.Sequential(
+            nn.Conv1d(1, 16, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv1d(16, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+        )
+        self.decoder = nn.Sequential(
+            nn.ConvTranspose1d(64, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.ConvTranspose1d(32, 16, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.ConvTranspose1d(16, 1, kernel_size=3, stride=1, padding=1)
+        )
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.decoder(x)
+        return x
+
+def filtruoti_su_waveunet(input_path, output_path):
+    print("🔧 Wave-U-Net filtravimas...")
+    model = WaveUNet()
+    model.eval()
+    mixture, sr = torchaudio.load(input_path)
+    if sr != 16000:
+        print("🔁 Resample į 16kHz...")
+        resampler = T.Resample(orig_freq=sr, new_freq=16000).to(mixture.device)
+        mixture = resampler(mixture)
+    if mixture.dim() == 2:
+        mixture = mixture.unsqueeze(0)
+    with torch.no_grad():
+        output = model(mixture)
+    output = output.squeeze(0)
+    torchaudio.save(output_path, output, 16000)
+    print(f"✅ Wave-U-Net išsaugota: {output_path}")
+
+def filtruoti_su_denoiser(input_path, output_path):
+    print("🔧 Denoiser (DCCRNet)...")
+    model = DCCRNet.from_pretrained("JorisCos/DCCRNet_Libri1Mix_enhsingle_16k")
+    mixture, sr = torchaudio.load(input_path)
+    if sr != 16000:
+        print("🔁 Resample į 16kHz...")
+        resampler = T.Resample(orig_freq=sr, new_freq=16000).to(mixture.device)
+        mixture = resampler(mixture)
+    with torch.no_grad():
+        est_source = model.separate(mixture)
+    torchaudio.save(output_path, est_source[0], 16000)
+    print(f"✅ Denoiser išsaugota: {output_path}")
+
+def filtruoti_su_noisereduce(input_path, output_path):
+    print("🔧 Noisereduce filtravimas...")
+    waveform, sr = torchaudio.load(input_path)
+    audio = waveform.detach().cpu().numpy()[0]
+    reduced = nr.reduce_noise(y=audio, sr=sr)
+    reduced_tensor = torch.from_numpy(reduced).unsqueeze(0)
+    torchaudio.save(output_path, reduced_tensor, sr)
+    print(f"✅ Noisereduce išsaugota: {output_path}")
+
+def filtruoti_audio(input_path: str, metodas: str) -> str:
+    if metodas == "Denoiser":
+        filtruoti_su_denoiser(input_path, OUTPUT_PATH)
+    elif metodas == "Wave-U-Net":
+        filtruoti_su_waveunet(input_path, OUTPUT_PATH)
+    elif metodas == "Noisereduce":
+        filtruoti_su_noisereduce(input_path, OUTPUT_PATH)
+    else:
+        raise ValueError("Nepalaikomas filtravimo metodas")
+
+    return OUTPUT_PATH