Create app.py

app.py

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+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+import csv
+
+import matplotlib.pyplot as plt
+from IPython.display import Audio
+from scipy.io import wavfile
+
+# Load the model.
+model = hub.load('https://tfhub.dev/google/yamnet/1')
+
+# Find the name of the class with the top score when mean-aggregated across frames.
+def class_names_from_csv(class_map_csv_text):
+  """Returns list of class names corresponding to score vector."""
+  class_names = []
+  with tf.io.gfile.GFile(class_map_csv_text) as csvfile:
+    reader = csv.DictReader(csvfile)
+    for row in reader:
+      class_names.append(row['display_name'])
+
+  return class_names
+
+class_map_path = model.class_map_path().numpy()
+class_names = class_names_from_csv(class_map_path)
+
+
+def ensure_sample_rate(original_sample_rate, waveform,
+                       desired_sample_rate=16000):
+  """Resample waveform if required."""
+  if original_sample_rate != desired_sample_rate:
+    desired_length = int(round(float(len(waveform)) /
+                               original_sample_rate * desired_sample_rate))
+    waveform = scipy.signal.resample(waveform, desired_length)
+  return desired_sample_rate, waveform
+ 
+os.system("wget https://storage.googleapis.com/audioset/miaow_16k.wav")
+ 
+def inference(audio): 
+   # wav_file_name = 'speech_whistling2.wav'
+  wav_file_name = audio
+  sample_rate, wav_data = wavfile.read(wav_file_name, 'rb')
+  sample_rate, wav_data = ensure_sample_rate(sample_rate, wav_data)
+ 
+  waveform = wav_data / tf.int16.max
+  
+  # Run the model, check the output.
+  scores, embeddings, spectrogram = model(waveform)
+  
+  scores_np = scores.numpy()
+  spectrogram_np = spectrogram.numpy()
+  infered_class = class_names[scores_np.mean(axis=0).argmax()]  
+  
+  return  f'The main sound is: {infered_class}'
+
+gr.Interface(inference,"audio","text").launch()
+