add yamnet

app.py

@@ -1,7 +1,74 @@
+
+# https://tfhub.dev/google/lite-model/yamnet/classification/tflite/1
+
+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
+
+import os
+
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="audio", outputs="text")
-iface.launch()
+# 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}'
+
+
+examples = [['miaow_16k.wav']]
+title = "yamnet"
+description = "An audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology."
+gr.Interface(inference, gr.inputs.Audio(type="filepath"), "text", examples=examples, title=title,
+             description=description).launch(enable_queue=True)
+