Upload app.py

app.py

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+import gradio as gr
+import librosa
+import matplotlib.pyplot as plt
+
+from train import ASR_Model 
+from model_cnn import Model
+
+def pre(audio):
+    model = ASR_Model(device='cuda',model_path='model/model.pth',pinyin_path ='pinyin.txt')
+
+    result = model.predict(audio) 
+    s = ''
+    for r in result:
+      s += r[0]+str(r[1])+' '
+    return s
+
+def visualize(audio):
+    y, sr = librosa.load(audio, sr=None)
+
+    plt.figure(figsize=(10, 4))
+    librosa.display.waveshow(y, sr=sr)
+    plt.title("Waveform of the Audio")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+
+    image_path = "./waveform.png"
+    plt.savefig(image_path, format='png')
+    plt.close()
+
+    # print(audio)
+
+    return image_path, pre(audio)
+
+#e = gr.Examples(examples=['./SSB10500228.wav'], inputs=[gr.File(type="filepath")])
+
+demo = gr.Interface(fn=visualize, inputs=gr.File(file_types=['.wav'], label="wav file"),
+                    outputs=[gr.Image(type="filepath", label="Waveform"),
+                             gr.Textbox(type="text", label="Tone Evaluation Result")],
+                    examples=["Examples/中原石化加油站.wav", "Examples/你叫什么名字你的名字.wav", "Examples/来一首许多年以后.wav"],
+                    title="Mandarin Tone Evaluation")
+
+
+demo.launch()