Initial commit with LFS

.gitattributes

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,131 @@
+import os
+import numpy as np
+import librosa
+import scipy.signal
+from scipy.stats import kurtosis
+import gradio as gr
+import tensorflow as tf
+import warnings
+
+warnings.filterwarnings('ignore', category=UserWarning)
+
+MAX_TIME_FRAMES = 313
+N_MELS = 128
+N_1D_FEATURES = 22
+
+LABEL_MAP_INVERSE = {
+    0: 'Machine 1_Normal', 1: 'Machine 1_Abnormal',
+    2: 'Machine 2_Normal', 3: 'Machine 2_Abnormal',
+    4: 'Machine 3_Normal', 5: 'Machine 3_Abnormal'
+}
+
+class MachineListenerPreprocessor:
+    def __init__(self, target_sr=16000, n_fft=2048, hop_length=512, n_mels=N_MELS, n_mfcc=20):
+        self.sr = target_sr
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.n_mels = n_mels
+        self.n_mfcc = n_mfcc
+        
+    def _apply_highpass_filter(self, y, cutoff=60.0):
+        nyquist = 0.5 * self.sr
+        normal_cutoff = cutoff / nyquist
+        if normal_cutoff >= 1.0:
+            return y
+        b, a = scipy.signal.butter(4, normal_cutoff, btype='high', analog=False)
+        return scipy.signal.filtfilt(b, a, y)
+
+    def _truncate_silence(self, y, top_db=25):
+        y_trimmed, _ = librosa.effects.trim(y, top_db=top_db, frame_length=self.n_fft, hop_length=self.hop_length)
+        return y_trimmed
+
+    def _mean_variance_normalize(self, y):
+        return (y - np.mean(y)) / (np.std(y) + 1e-8)
+
+    def process_audio(self, file_path):
+        y, _ = librosa.load(file_path, sr=self.sr)
+        y = self._apply_highpass_filter(y)
+        y = self._truncate_silence(y, top_db=25)
+        
+        if len(y) == 0:
+            raise ValueError(f"Silence only: {file_path}")
+            
+        y = self._mean_variance_normalize(y)
+        
+        # 2D Features
+        mel_spec = librosa.feature.melspectrogram(y=y, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels)
+        log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max)
+        
+        # 1D Features
+        mfccs_mean = np.mean(librosa.feature.mfcc(S=log_mel_spec, n_mfcc=self.n_mfcc), axis=1)
+        centroid_mean = np.mean(librosa.feature.spectral_centroid(y=y, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length))
+        
+        stft_mag = np.abs(librosa.stft(y, n_fft=self.n_fft, hop_length=self.hop_length))
+        frame_kurtosis = np.nan_to_num(kurtosis(stft_mag, axis=0, fisher=True, bias=False))
+        kurtosis_mean = np.mean(frame_kurtosis)
+        
+        return {
+            "2d_spectrogram": log_mel_spec,
+            "1d_statistics": np.hstack([mfccs_mean, centroid_mean, kurtosis_mean]),
+        }
+
+def pad_or_truncate(spectrogram, max_frames):
+    if spectrogram.shape[1] > max_frames:
+        return spectrogram[:, :max_frames]
+    elif spectrogram.shape[1] < max_frames:
+        pad_width = max_frames - spectrogram.shape[1]
+        return np.pad(spectrogram, pad_width=((0, 0), (0, pad_width)), mode='constant')
+    return spectrogram
+
+# Load the model
+try:
+    model = tf.keras.models.load_model('best_v2f_generalist.keras')
+except Exception as e:
+    print("Warning: Could not load model. Ensure the path is correct.", e)
+    model = None
+
+preprocessor = MachineListenerPreprocessor()
+
+def predict(audio_filepath):
+    if model is None:
+        return "Model not loaded properly."
+    if audio_filepath is None:
+        return "Please upload an audio file."
+    
+    try:
+        # Extract features
+        features = preprocessor.process_audio(audio_filepath)
+        
+        spec_2d = pad_or_truncate(features["2d_spectrogram"], MAX_TIME_FRAMES)
+        stat_1d = features["1d_statistics"]
+        
+        # Add batch dimensions
+        spec_2d_batch = np.expand_dims(spec_2d, axis=0)
+        # Note: If your model expects a specific shape, e.g., (batch, channels, height, width), adjust dimensions below.
+        spec_2d_batch = np.expand_dims(spec_2d_batch, axis=-1)  
+        stat_1d_batch = np.expand_dims(stat_1d, axis=0)
+        
+        # Predict
+        predictions = model.predict([spec_2d_batch, stat_1d_batch])
+        
+        predicted_class_idx = np.argmax(predictions, axis=-1)[0]
+        predicted_label = LABEL_MAP_INVERSE.get(predicted_class_idx, "Unknown")
+        
+        confidence = float(np.max(predictions))
+        
+        return f"Prediction: {predicted_label} (Confidence: {confidence:.2f})"
+        
+    except Exception as e:
+        return f"Error processing file: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Audio(type="filepath", label="Upload Machine Audio"),
+    outputs="text",
+    title="Machine Listener Diagnosis",
+    description="Upload a sound from a machine to predict whether it is Normal or Abnormal."
+)
+
+if __name__ == "__main__":
+    iface.launch()

best_v2f_generalist.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6b374074a6eaa9c1588263282b63536f09ed8b2cc75439cdbca0492949ecb9e
+size 3069230

requirements.txt

@@ -0,0 +1,5 @@
+numpy
+scipy
+librosa
+gradio
+tensorflow