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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() |