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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # In[1]: | |
| 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[9]: | |
| ref_file = hf_hub_download("gputrain/UrbanSound8K-model", "UrbanSound8K.csv") | |
| model_file = hf_hub_download("gputrain/UrbanSound8K-model", "model.pkl") | |
| # In[10]: | |
| 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) | |
| EXAMPLES_PATH = Path("./examples") | |
| labels = model.dls.vocab | |
| # In[11]: | |
| with open("article.md") as f: | |
| article = f.read() | |
| # In[12]: | |
| interface_options = { | |
| "title": "Urban Sound 8K Classification", | |
| "description": "Fast AI example of using a pre-trained Resnet34 vision model for an audio classification task on the [Urban Sounds](https://urbansounddataset.weebly.com/urbansound8k.html) dataset. ", | |
| "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[13]: | |
| 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[14]: | |
| def predict(): | |
| img = PILImage.create('temp.png') | |
| pred,pred_idx,probs = model.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| return labels_probs | |
| # In[20]: | |
| def end2endpipeline(filename): | |
| convert_sounds_melspectogram(filename) | |
| return predict() | |
| # In[16]: | |
| demo = gradio.Interface( | |
| fn=end2endpipeline, | |
| inputs=gradio.inputs.Audio(source="upload", type="filepath"), | |
| outputs=gradio.outputs.Label(num_top_classes=10), | |
| **interface_options, | |
| ) | |
| # In[19]: | |
| launch_options = { | |
| "enable_queue": True, | |
| "share": False, | |
| #"cache_examples": True, | |
| } | |
| demo.launch(**launch_options) | |
| # In[ ]: | |