app

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[3]:
+
+
+import gradio
+
+from fastai.vision.all import *
+from fastai.data.all import *
+from pathlib import Path
+import pandas as pd
+from matplotlib.pyplot import specgram
+import librosa
+import librosa.display
+from huggingface_hub import hf_hub_download
+from fastai.learner import load_learner
+
+
+# In[10]:
+
+
+ref_file = hf_hub_download("gputrain/UrbanSound8K", "UrbanSound8K.csv")
+
+model_file = hf_hub_download(
+    hf_hub_download("gputrain/UrbanSound8K", "export.pkl")
+)
+
+
+# In[3]:
+
+
+# Local test
+# ref_file = "UrbanSound8K.csv"
+# model_file = "export.pkl"
+
+
+# In[4]:
+
+
+df = pd.read_csv(ref_file) 
+df['fname'] = df[['slice_file_name','fold']].apply (lambda x: str(x['slice_file_name'][:-4])+'.png'.strip(),axis=1 )
+my_dict = dict(zip(df.fname,df['class']))
+def label_func(f_name):
+    f_name = str(f_name).split('/')[-1:][0]
+    return my_dict[f_name]
+model = load_learner (model_file)
+labels = model.dls.vocab
+
+
+# In[5]:
+
+
+with open("article.md") as f:
+    article = f.read()
+
+
+# In[6]:
+
+
+interface_options = {
+    "title": "Urban Sound 8K Classification",
+    "description": "A Fast AI example with ResNet34 image classification of a sound wav file transformed to a Mel Spectrogram ",
+    #"article": article,
+    "interpretation": "default",
+    "layout": "horizontal",
+    # Audio from validation file
+    "examples": ["dog_bark.wav", "children_playing.wav", "air_conditioner.wav", "street_music.wav", "engine_idling.wav",
+                "jackhammer.wav", "drilling.wav", "siren.wav","car_horn.wav","gun_shot.wav"],
+    "allow_flagging": "never"
+}
+
+
+# In[7]:
+
+
+def convert_sounds_melspectogram (audio_file):
+
+    samples, sample_rate = librosa.load(audio_file)  #create onces with librosa
+
+    fig = plt.figure(figsize=[0.72,0.72])
+    ax = fig.add_subplot(111)
+    ax.axes.get_xaxis().set_visible(False)
+    ax.axes.get_yaxis().set_visible(False)
+    ax.set_frame_on(False)
+    melS = librosa.feature.melspectrogram(y=samples, sr=sample_rate)
+    librosa.display.specshow(librosa.power_to_db(melS, ref=np.max))
+    filename  = 'temp.png'
+    plt.savefig(filename, dpi=400, bbox_inches='tight',pad_inches=0)
+    plt.close('all')
+    
+    return None
+
+
+# In[8]:
+
+
+def predict():
+    img = PILImage.create('temp.png')
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    return labels_probs
+
+
+# In[9]:
+
+
+def end2endpipeline(filename):
+    create_image(filename)
+    return predict("temp.png")
+
+
+# In[10]:
+
+
+demo = gradio.Interface(
+    fn=end2endpipeline,
+    inputs=gradio.inputs.Audio(source="upload", type="filepath"),
+    outputs=gradio.outputs.Label(num_top_classes=10),
+    **interface_options,
+)
+
+
+# In[ ]:
+
+
+launch_options = {
+    "enable_queue": True,
+    "share": False,
+    # thanks Alex for pointing this option to cache examples
+    "cache_examples": True,
+}
+
+demo.launch(**launch_options)
+
+
+# In[ ]:
+
+
+
+