| from transformers import pipeline | |
| from sessions import sessions | |
| import torchaudio | |
| import torchaudio.transforms as T | |
| import gradio as gr | |
| pipe = pipeline( | |
| "audio-classification", | |
| model="BilalHasan/distilhubert-finetuned-ravdess", | |
| ) | |
| audio_batch = [] | |
| def split_audio(array): | |
| len_of_each_array = 30 * 16000 | |
| arr1, arr2 = array[0: len_of_each_array], array[int(len_of_each_array / 2):] | |
| audio_batch.append(arr1) | |
| if len(arr2) > len_of_each_array: | |
| split_audio(arr2) | |
| else: | |
| audio_batch.append(arr2) | |
| return audio_batch | |
| def prediction(path): | |
| predictions = [] | |
| array, sr = torchaudio.load(path) | |
| resampler = T.Resample(sr, 16000) | |
| resampled_audio = resampler(array) | |
| audio_batch = split_audio(resampled_audio[0].numpy()) | |
| for i in range(len(audio_batch)): | |
| predictions.append(pipe(audio_batch[i])[0]['label']) | |
| mood = max(set(predictions), key = predictions.count) | |
| if mood in ['neutral', 'calm', 'happy', 'surprised']: | |
| mood = 'other' | |
| session = sessions.mood2session[mood] | |
| return mood, session | |
| demo = gr.Interface( | |
| fn=prediction, | |
| inputs=[gr.Audio(type="filepath")], | |
| outputs=[gr.Textbox(label="Mood"), gr.Textbox(label="Recommended Yoga Session")] | |
| ) | |
| demo.launch() | |