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import os
import sys
import pandas as pd
import altair as alt
import io
import streamlit as st
from fake_audio_detection.model import predict_audio_blocks
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(parent_dir)
st.title("🔎 DeepVoice Detection")
APP_DIR = os.path.dirname(os.path.abspath(__file__))
# if you want to code your training part
DATASET_DIR = os.path.join(APP_DIR, "dataset/")
MODEL_PATH = os.path.join(APP_DIR, "model/noma-1")
REAL_DIR = os.path.join(APP_DIR, "audios/real")
FAKE_DIR = os.path.join(APP_DIR, "audios/fake")
# Then continue as before
real_audio = {
f"Real - {f}": os.path.join(REAL_DIR, f)
for f in os.listdir(REAL_DIR)
if f.endswith((".wav", ".mp3"))
}
fake_audio = {
f"Fake - {f}": os.path.join(FAKE_DIR, f)
for f in os.listdir(FAKE_DIR)
if f.endswith((".wav", ".mp3"))
}
all_audio = {**real_audio, **fake_audio}
selected_label = st.radio("Select an audio file to play:", list(all_audio.keys()))
selected_path = all_audio[selected_label]
st.write("#### Try with your audios")
uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "ogg"])
selected_label = "Default Audio"
if uploaded_file is not None:
st.markdown(f"**Now Playing:** `{uploaded_file.name}`")
audio_bytes = uploaded_file.read()
file_extension = uploaded_file.name.split(".")[-1].lower()
st.audio(audio_bytes, format=f"audio/{file_extension}")
else:
st.markdown(f"**Now Playing:** `{selected_label}`")
with open(selected_path, "rb") as audio_file:
audio_bytes = audio_file.read()
st.audio(audio_bytes, format="audio/wav")
if st.button("Run Prediction") and os.path.exists(MODEL_PATH):
audio_bytes = None
if uploaded_file:
bytes_data = uploaded_file.getvalue()
audio_bytes = io.BytesIO(bytes_data)
with st.spinner("Analyzing audio..."):
times, probas = predict_audio_blocks(MODEL_PATH, selected_path, audio_bytes)
preds = probas.argmax(axis=1)
confidences = probas.max(axis=1)
preds_as_string = ["Fake" if i == 0 else "Real" for i in preds]
df = pd.DataFrame(
{"Seconds": times, "Prediction": preds_as_string, "Confidence": confidences}
)
def get_color(row):
if row["Confidence"] < 0.3:
return "Uncertain"
return row["Prediction"]
df["Confidence Level"] = df.apply(get_color, axis=1)
# Plot
st.markdown("### Prediction by 1s Blocks")
st.markdown(
"Hover above each bar to see the confidence level of each prediction."
)
chart = (
alt.Chart(df)
.mark_bar()
.encode(
x=alt.X("Seconds:O", title="Seconds"),
y=alt.value(30),
color=alt.Color(
"Confidence Level:N",
scale=alt.Scale(
domain=["Fake", "Real", "Uncertain"],
range=["steelblue", "green", "gray"],
),
),
tooltip=["Seconds", "Prediction", "Confidence"],
)
.properties(width=700, height=150)
)
text = (
alt.Chart(df)
.mark_text(
align="right",
baseline="top",
dy=10,
color="white",
xOffset=10,
yOffset=-20,
fontSize=14,
)
.encode(x=alt.X("Seconds:O"), y=alt.value(15), text="Prediction:N")
)
st.altair_chart(chart + text, use_container_width=True)
st.markdown("### Overall prediction")
if all(element == "Real" for element in preds_as_string):
st.markdown("The audio is **Real**")
elif all(element == "Fake" for element in preds_as_string):
st.markdown("The audio is **Fake**")
else:
st.markdown("Some parts of the audio have been detected as **Fake**")
elif not os.path.exists(MODEL_PATH):
st.warning(f"Missing model: {MODEL_PATH}")
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