Upload 14 files

app.py

@@ -1,7 +1,35 @@
-import gradio as gr
+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
-def greet(name):
-    return "Hello " + name + "!!"
+# %% auto 0
+__all__ = ['learn', 'categories', 'aud', 'examples', 'intf', 'log_mel_spec_tfm', 'classify_aud']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# %% app.ipynb 1
+def log_mel_spec_tfm(fname, src_path, dst_path):
+    os.makedirs(str(dst_path), exist_ok = True)
+    y, sr = librosa.load(str(src_path/fname), mono=True)
+    D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)
+    img = librosa.display.specshow(D, y_axis='linear', x_axis='time',
+                                  sr=sr)
+    plt.savefig(str(dst_path/fname[:-4]) + '.png')
+    plt.close()
+    return img
+
+# %% app.ipynb 2
+learn = load_learner('model.pkl')
+learn.remove_cb(ProgressCallback)
+
+# %% app.ipynb 6
+categories = ('Brass', 'Flute', 'Guitar', 'Keyboard', 'Mallet', 'Reed', 'String', 'Vocal')
+
+def classify_aud(aud):
+    log_mel_spec_tfm(aud, Path('.'), Path('.'))
+    img_fname = str(aud[:-4]) + '.png'
+    pred, idx, probs = learn.predict(img_fname)
+    return dict(zip(categories, map(float, probs)))
+
+# %% app.ipynb 8
+aud = gr.Audio(source="upload", type="numpy")
+examples = ['test/' + str(f.name) for f in Path('test').iterdir()]
+
+intf = gr.Interface(fn = classify_aud, inputs = aud, outputs = "label", examples = examples)
+intf.launch(inline = False)

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c5d19de8a83fe566fad633416e35cfecd410f2372f19b940d678f0942715f33
+size 46992273

requirements.txt

@@ -0,0 +1,11 @@
+fastbook
+fastai
+librosa
+matplotlib
+numpy
+functools
+pathlib
+multiprocessing
+os
+scipy
+gradio