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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()
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