Create app.py

app.py

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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)