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import time
import streamlit as st
# from transformers import pipeline
import os
import torch
import datetime
import numpy as np
import soundfile
from wavmark.utils import file_reader
# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
# st.title("Hot Dog? Or Not?")
# file_name = st.file_uploader("Upload a hot dog candidate image")
# if file_name is not None:
# col1, col2 = st.columns(2)
# image = Image.open(file_name)
# col1.image(image, use_column_width=True)
# predictions = pipeline(image)
# col2.header("Probabilities")
# for p in predictions:
# col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
def create_default_value():
if "def_value" not in st.session_state:
def_val_npy = np.random.choice([0, 1], size=32 - len_start_bit)
def_val_str = "".join([str(i) for i in def_val_npy])
st.session_state.def_value = def_val_str
# Main web app
def main():
create_default_value()
# st.title("MDS07")
# st.write("https://github.com/wavmark/wavmark")
markdown_text = """
# MDS07
[AudioSeal](https://github.com/jcha0155/AudioSealEnhanced) is the next-generation watermarking tool driven by AI.
You can upload an audio file and encode a custom 16-bit watermark or perform decoding from a watermarked audio.
This page is for demonstration usage and only process **the first minute** of the audio.
If you have longer files for processing, we recommend using [our python toolkit](https://github.com/jcha0155/AudioSealEnhanced).
"""
# 使用st.markdown渲染Markdown文本
st.markdown(markdown_text)
audio_file = st.file_uploader("Upload Audio", type=["wav", "mp3"], accept_multiple_files=False)
if audio_file:
# 保存文件到本地：
tmp_input_audio_file = os.path.join("/tmp/", audio_file.name)
with open(tmp_input_audio_file, "wb") as f:
f.write(audio_file.getbuffer())
# 展示文件到页面上
# st.audio(tmp_input_audio_file, format="audio/wav")
action = st.selectbox("Select Action", ["Add Watermark", "Decode Watermark"])
if action == "Add Watermark":
watermark_text = st.text_input("The watermark (0, 1 list of length-16):", value=st.session_state.def_value)
add_watermark_button = st.button("Add Watermark", key="add_watermark_btn")
if add_watermark_button: # 点击按钮后执行的
if audio_file and watermark_text:
with st.spinner("Adding Watermark..."):
# watermarked_audio, encode_time_cost = add_watermark(tmp_input_audio_file, watermark_text)
# st.write("Watermarked Audio:")
# print("watermarked_audio:", watermarked_audio)
# st.audio(watermarked_audio, format="audio/wav")
# st.write("Time Cost: %d seconds" % encode_time_cost)
# # st.button("Add Watermark", disabled=False)
# elif action == "Decode Watermark":
# if st.button("Decode"):
# with st.spinner("Decoding..."):
# decode_watermark(tmp_input_audio_file)
if __name__ == "__main__":
default_sr = 16000
max_second_encode = 60
max_second_decode = 30
len_start_bit = 16
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# model = wavmark.load_model().to(device)
main()
# audio_path = "/Users/my/Library/Mobile Documents/com~apple~CloudDocs/CODE/PycharmProjects/4_语音水印/419_huggingface水印/WavMark/example.wav"
# decoded_watermark, decode_cost = decode_watermark(audio_path)
# print(decoded_watermark) |