Added prediction logic

app.py

@@ -1,15 +1,53 @@
 import gradio as gr
 import librosa
+import tensorflow as tf 
+from huggingface_hub import from_pretrained_keras
 
 def load_audio_to_tensor(filename):
     audio, sampling_rate = librosa.load(filename, sr=None, mono=True)  # load audio and convert to mono
     wave = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)  # resample to 16KHz
     return wave
 
+def preprocess_mp3(sample, index):
+    sample = sample[0]
+    sample = tf.cast(sample, tf.float32)
+    zero_padding = tf.zeros([16000] - tf.shape(sample), dtype=tf.float32)
+    wave = tf.concat([zero_padding, sample], 0)
+    spectrogram = tf.signal.stft(wave, frame_length=320, frame_step=32)
+    spectrogram = tf.abs(spectrogram)
+    spectrogram = tf.expand_dims(spectrogram, axis=2)
+    return spectrogram
+
 def greet(name):
     wave = load_audio_to_tensor(name)
-    # return "Hello " + str(name[1]) + "!!"
-    return wave
+    power = sum(wave * 2) / len(wave)   # audio signal power
+    SNR = 3.5                           # signal-to-noise ratio
+    SNR_linear = 10 ** (SNR / 10)       # convert SNR to linear scale
+    noise_power = power / SNR_linear    # noise power
+
+    # add noise to audio to simulate environment
+    noise = np.random.normal(0, noise_power ** 0.5, wave.shape)  # generate noise
+    wave = (wave + noise) * 32768.0     # add noise to the audio signal
+    tensor_wave = tf.convert_to_tensor(wave, dtype=tf.float32)  # convert to tensor
+    min_wave = min(wave)  
+    if len(wave) > 16000:
+        sequence_stride = 16000
+    else:
+        sequence_stride = 16000-1
+
+# create audio slices
+    audio_slices = tf.keras.utils.timeseries_dataset_from_array(wave, wave, sequence_length=16000, sequence_stride=sequence_stride, batch_size=1)
+    samples, index = audio_slices.as_numpy_iterator().next()   
+    
+    audio_slices = audio_slices.map(preprocess_mp3)
+    audio_slices = audio_slices.batch(64)   
+
+    model = from_pretrained_keras("CXDJY/snore_ai")
+
+    yhat = model.predict(audio_slices)
+    yhat = [1 if prediction > 0.99 else 0 for prediction in yhat]
+    yhat1 = [key for key, group in groupby(yhat)]
+    return yhat1
 
 iface = gr.Interface(fn=greet, inputs="file", outputs="text")
 # iface = gr.Interface(fn=greet, inputs="audio", outputs="text")

requirements.txt

@@ -1 +1,5 @@
-librosa==0.10.1
+librosa==0.10.1
+huggingface_hub==0.20.1
+numpy==1.26.4
+tensorflow==2.15.0
+tensorflow_intel==2.15.0