Delete filtravimas.py

filtravimas.py

@@ -1,84 +0,0 @@
-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...")
-        mixture = T.Resample(orig_freq=sr, new_freq=16000)(mixture)
-    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...")
-        mixture = T.Resample(orig_freq=sr, new_freq=16000)(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.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