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| from tensorflow import keras | |
| import os | |
| import soundfile as sf | |
| import numpy as np | |
| import librosa | |
| import gradio as gr | |
| import seaborn as sns | |
| import pandas as pd | |
| import plotly.express as px | |
| model = keras.models.load_model('emotion.h5') | |
| labels = ['Angry', 'Disgusted', 'Fearful', 'Happy', 'Neutral', 'Sad', 'Suprised'] | |
| def predict(audio): | |
| wave, sr = librosa.load(audio, sr=None) | |
| segment_dur_secs = 3 | |
| segment_length = sr * segment_dur_secs | |
| num_sections = int(np.ceil(len(wave) / segment_length)) | |
| split = [] | |
| paths =[] | |
| for i in range(num_sections): | |
| t = wave[i * segment_length: (i + 1) * segment_length] | |
| split.append(t) | |
| out_dir = ('audio_data/splits/') | |
| os.makedirs(out_dir, exist_ok=True) | |
| for i in range(num_sections): | |
| recording_name = os.path.basename(audio[:-4]) | |
| out_file = f"{recording_name}_{str(i)}.wav" | |
| sf.write(os.path.join(out_dir, out_file), split[i], sr) | |
| paths.append(os.path.join(out_dir, out_file)) | |
| predicted_features = pd.DataFrame(columns=['features']) | |
| counter=0 | |
| for path in paths: | |
| X, sample_rate = librosa.load(path | |
| ,duration=2.5 | |
| ,sr=44100 | |
| ,offset=0.5 | |
| ) | |
| sample_rate = np.array(sample_rate) | |
| mfccs = np.mean(librosa.feature.mfcc(y=X, | |
| sr=sample_rate, | |
| n_mfcc=13), | |
| axis=0) | |
| predicted_features.loc[counter] = [mfccs] | |
| counter=counter+1 | |
| predicted_features = pd.DataFrame(predicted_features['features'].values.tolist()) | |
| predicted_features.dropna(inplace=True) | |
| preds = model.predict(predicted_features) | |
| preds=preds.argmax(axis=1) | |
| df_preds = pd.DataFrame(preds,columns = ['prediction']) | |
| emotions = [] | |
| for i in df_preds['prediction']: | |
| emotion = labels[int(i)] | |
| emotions.append(emotion) | |
| df_preds['emotion'] = emotions | |
| df_preds = df_preds.reset_index() | |
| fig = px.line(df_preds, x="index", y="emotion", title='Life expectancy in Canada') | |
| return fig | |
| outputs = gr.Plot() | |
| title = "Emotion recognition" | |
| description = "This model can shows how speaker emotion changes over the speech" | |
| infr = gr.Interface(fn=predict, | |
| inputs=gr.Audio(type="filepath"), | |
| examples=['audio_samples/1.mp3','audio_samples/2.mp3','audio_samples/3.mp3','audio_samples/4.mp3'] | |
| cache_examples=True, | |
| outputs=outputs, | |
| title=title,description=description,interpretation='default',) | |
| infr.launch() |