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| import numpy as np | |
| import skorch | |
| import torch | |
| import torch.nn as nn | |
| import gradio as gr | |
| import librosa | |
| from joblib import dump, load | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.preprocessing import LabelEncoder | |
| from resnet import ResNet | |
| from gradio_utils import load_as_librosa, predict_gradio | |
| from dataloading import uniformize, to_numpy | |
| from preprocessing import MfccTransformer, TorchTransform | |
| SEED : int = 42 | |
| np.random.seed(SEED) | |
| torch.manual_seed(SEED) | |
| model = load('./model/model.joblib') | |
| only_mffc_transform = load('./model/only_mffc_transform.joblib') | |
| label_encoder = load('./model/label_encoder.joblib') | |
| SAMPLE_RATE = load("./model/SAMPLE_RATE.joblib") | |
| METHOD = load("./model/METHOD.joblib") | |
| MAX_TIME = load("./model/MAX_TIME.joblib") | |
| N_MFCC = load("./model/N_MFCC.joblib") | |
| HOP_LENGHT = load("./model/HOP_LENGHT.joblib") | |
| sklearn_model = Pipeline( | |
| steps=[ | |
| ("mfcc", only_mffc_transform), | |
| ("model", model) | |
| ] | |
| ) | |
| uniform_lambda = lambda y, sr: uniformize(y, sr, METHOD, MAX_TIME) | |
| title = r"ResNet 9" | |
| description = r""" | |
| <center> | |
| The resnet9 model was trained to classify drone speech command. | |
| <img src="http://zeus.blanchon.cc/dropshare/modia.png" width=200px> | |
| </center> | |
| """ | |
| article = r""" | |
| - [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385) | |
| """ | |
| demo_men = gr.Interface( | |
| title = title, | |
| description = description, | |
| article = article, | |
| fn=lambda data: predict_gradio( | |
| data=data, | |
| uniform_lambda=uniform_lambda, | |
| sklearn_model=sklearn_model, | |
| label_transform=label_encoder, | |
| target_sr=SAMPLE_RATE), | |
| inputs = gr.Audio(source="microphone", type="numpy"), | |
| outputs = gr.Label(), | |
| # allow_flagging = "manual", | |
| # flagging_options = ['recule', 'tournedroite', 'arretetoi', 'tournegauche', 'gauche', 'avance', 'droite'], | |
| # flagging_dir = "./flag/men" | |
| ) | |
| demo_men.launch() | |