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import gradio as gr
import torch
import torchaudio
from timeit import default_timer as timer
from data_setups import audio_preprocess, resample
import gdown
url = 'https://drive.google.com/uc?id=1X5CR18u0I-ZOi_8P0cNptCe5JGk9Ro0C'
output = 'piano.wav'
gdown.download(url, output, quiet=False)
url = 'https://drive.google.com/uc?id=1W-8HwmGR5SiyDbUcGAZYYDKdCIst07__'
output= 'torch_efficientnet_fold2_CNN.pth'
gdown.download(url, output, quiet=False)
device = "cuda" if torch.cuda.is_available() else "cpu"
SAMPLE_RATE = 44100
AUDIO_LEN = 2.90
model = torch.load("torch_efficientnet_fold2_CNN.pth", map_location=torch.device('cpu'))
LABELS = [
"Cello", "Clarinet", "Flute", "Acoustic Guitar", "Electric Guitar", "Organ", "Piano", "Saxophone", "Trumpet", "Violin", "Voice"
]
example_list = [
["piano.wav"]
]
def predict(audio_path):
start_time = timer()
wavform, sample_rate = torchaudio.load(audio_path)
wav = resample(wavform, sample_rate, SAMPLE_RATE)
if len(wav) > int(AUDIO_LEN * SAMPLE_RATE):
wav = wav[:int(AUDIO_LEN * SAMPLE_RATE)]
else:
print(f"input length {len(wav)} too small!, need over {int(AUDIO_LEN * SAMPLE_RATE)}")
return
img = audio_preprocess(wav, SAMPLE_RATE).unsqueeze(0)
model.eval()
with torch.inference_mode():
pred_probs = torch.softmax(model(img), dim=1)
pred_labels_and_probs = {LABELS[i]: float(pred_probs[0][i]) for i in range(len(LABELS))}
pred_time = round(timer() - start_time, 5)
return pred_labels_and_probs, pred_time
demo = gr.Interface(fn=predict,
inputs=gr.Audio(type="filepath"),
outputs=[gr.Label(num_top_classes=11, label="Predictions"),
gr.Number(label="Prediction time (s)")],
examples=example_list,
cache_examples=False
)
demo.launch(debug=False)
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