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pipeline.py

@@ -1,53 +1,5 @@
-import os, sys, torch, joblib, importlib
-import pandas as pd
-import torchaudio
-import torchaudio.transforms as T
+import torch
 
 class BioacousticEngine:
-    def __init__(self, repo_dir="tiny-bird-diffusion"):
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.repo_dir = repo_dir
-        
-        # Dynamic path loading
-        sys.path.append(os.path.abspath(repo_dir))
-        from cvt import cvt13
-        
-        # Standardized audio processor loading
-        mel_module = importlib.import_module("mel_spectrogram")
-        self.preprocessor = mel_module.MelSpectrogramProcessor(device=self.device)
-        
-        # Initialize architecture and map weights
-        self.model = cvt13()
-        self.model.load_state_dict(torch.load(f"{repo_dir}/protoclr.pth", map_location="cpu"))
-        self.model = self.model.to(self.device).eval()
-        
-        # Load compressed spatial boundaries
-        brain_data = joblib.load(f"{repo_dir}/trained_cluster_brain.joblib")
-        self.reducer = brain_data['umap']
-        self.df = pd.read_csv(f"{repo_dir}/acoustic_atlas_metadata.csv")
-
-    def process_waveform(self, waveform, sample_rate):
-        """Standardizes incoming audio channels and samples."""
-        if sample_rate != 16000:
-            waveform = T.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
-        if waveform.shape[0] > 1:
-            waveform = torch.mean(waveform, dim=0, keepdim=True)
-            
-        # 3-Second peak activity selection
-        total_samples = waveform.shape[-1]
-        target_samples = 3 * 16000
-        if total_samples > target_samples:
-            step = 4000
-            max_energy = -1
-            best_start = 0
-            for start in range(0, total_samples - target_samples + 1, step):
-                chunk = waveform[:, start:start + target_samples]
-                energy = chunk.abs().mean().item()
-                if energy > max_energy:
-                    max_energy = energy
-                    best_start = start
-            waveform = waveform[:, best_start:best_start + target_samples]
-            
-        if waveform.abs().max() > 0.02:
-            waveform = waveform / waveform.abs().max()
-        return waveform.to(self.device)
+    def __init__(self):
+        self.device = "cpu"