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 from audioseal import AudioSeal import torchaudio from pydub import AudioSegment import io import librosa # import numpy # def my_read_file(audio_path, max_second): # signal, sr, audio_length_second = read_as_single_channel_16k(audio_path, default_sr) # if audio_length_second > max_second: # signal = signal[0:default_sr * max_second] # audio_length_second = max_second # return signal, sr, audio_length_second 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 def download_sample_audio(): url = "https://keithito.com/LJ-Speech-Dataset/LJ037-0171.wav" with open("test.wav", "wb") as f: resp = urllib.request.urlopen(url) f.write(resp.read()) wav, sample_rate = torchaudio.load("test.wav") return wav, sample_rate # 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) # st.markdown(tmp_input_audio_file) # with open(tmp_input_audio_file, "wb") as f: # f.write(audio_file.getbuffer()) # st.audio(tmp_input_audio_file, format="mp3/wav") #1st attempt #audio_path = " audio_file.name" # audio, sr = torchaudio.load(audio_file) # st.audio(audio_file, format="audio/mpeg") # audio= audio.unsqueeze(0) # st.markdown("SR") # st.markdown(sr) # st.markdown("after unsqueeze wav or mp3") # st.markdown(audio) #2nd attempt # Save file to local storage tmp_input_audio_file = os.path.join("/tmp/", audio_file.name) file_extension = os.path.splitext(tmp_input_audio_file)[1].lower() st.markdown(file_extension) if file_extension in [".wav", ".flac"]: with open("test.wav", "wb") as f: f.write(audio_file.getbuffer()) st.audio("test.wav", format="audio/wav") elif file_extension == ".mp3": with open("test.mp3", "wb") as f: f.write(audio_file.getbuffer()) st.audio("test.mp3", format="audio/mpeg") #Load the WAV file using torchaudio if file_extension in [".wav", ".flac"]: wav, sample_rate = torchaudio.load("test.wav") st.markdown("Before unsquueze wav") st.markdown(wav) #Unsqueeze for line 176 wav= wav.unsqueeze(0) elif file_extension == ".mp3": wav3, sample_rate = librosa.load("test.mp3") st.markdown(wav3) #RuntimeError: Could not infer dtype of numpy.float32 #wav = torch.tensor(wav3).float() / 32768.0 #RuntimeError: Numpy is not available wav = torch.from_numpy(wav3) #/32768.0 wav = wav.unsqueeze(0).unsqueeze(0) st.markdown("Before unsqueeze mp3") st.markdown(wav) #Unsqueeze for line 176 # wav= wav.unsqueeze(0) # #2nd way # # Convert the tensor to a byte-like object in WAV format # with io.BytesIO() as buffer: # # Save the audio to the buffer using torchaudio # torchaudio.save(buffer, wav, default_sr, format="wav") # # Get the byte data from the buffer # wav = buffer.getvalue() # # Play the audio file (WAV format) # st.audio(wav, format="audio/wav") # wav, sample_rate = torchaudio.load(audio_file, format="mp3/wav") # st.markdown("SR") # st.markdown(sample_rate) # st.markdown("after unsqueeze wav or mp3") # st.markdown(wav) # 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..."): #wav = my_read_file(wav,max_second_encode) #1st attempt watermark = model.get_watermark(wav, default_sr) watermarked_audio = wav + watermark print(watermarked_audio.size()) size = watermarked_audio.size() #st.markdown(size) print(watermarked_audio.squeeze()) squeeze = watermarked_audio.squeeze(1) shape = squeeze.size() #st.markdown(shape) #st.markdown(squeeze) if file_extension in [".wav", ".flac"]: torchaudio.save("output.wav", squeeze, default_sr, bits_per_sample=16) watermarked_wav = torchaudio.save("output.wav", squeeze, default_sr, bits_per_sample=16) st.audio("output.wav", format="audio/wav") with open("output.wav", "wb") as file: #file.read() #file.write(watermarked_wav.getbuffer()) binary_data = file.read() btn = st.download_button( label="Download watermarked audio", data=binary_data, file_name="output.wav", mime="audio/wav", ) elif file_extension == ".mp3": torchaudio.save("output.mp3", squeeze, default_sr) watermarked_mp3 = torchaudio.save("output.mp3", squeeze, default_sr) binary_data = watermarked_mp3.getvalue() st.audio("output.mp3", format="audio/mpeg") with open("output.mp3", "wb") as file: #file.write(watermarked_wav.getbuffer()) binary_data = file.read() st.download_button( label="Download watermarked audio", data=binary_data, file_name="output.mp3", mime="audio/mpeg", ) # st.download_button( # label="Download Watermarked audio", # data="output.wav", # file_name="output.wav", # mime="audio/wav", # ) #2nd Attempt # watermarked_audio = model(wav, sample_rate=default_sr, alpha=1) # print(watermarked_audio.size()) # size = watermarked_audio.size() # st.markdown(size) # print(watermarked_audio.squeeze()) # squeeze = watermarked_audio.squeeze(1) # shape = squeeze.size() # st.markdown(shape) # st.markdown(squeeze) # # watermarked_audio, encode_time_cost = add_watermark(tmp_input_audio_file, watermark_text) # st.write("Watermarked Audio:") # st.markdown(watermarked_audio) # print("watermarked_audio:", watermarked_audio) # watermarked_audio = torchaudio.save("output.wav", squeeze, default_sr) # st.audio(watermarked_audio, format="audio/wav") #st.write("Time Cost: %d seconds" % encode_time_cost) # # st.button("Add Watermark", disabled=False) elif action == "Detect Watermark": Detect_watermark_button = st.button("Detect Watermark", key="detect_watermark_btn") if st.button("Detect"): with st.spinner("Detecting..."): result, message = detector.detect_watermark(watermarked_audio, sample_rate=default_sr, message_threshold=0.5) print(f"\nThis is likely a watermarked audio: {result}") # Run on an unwatermarked audio result2, message2 = detector.detect_watermark(wav, sample_rate=default_sr, message_threshold=0.5) print(f"This is likely an unwatermarked audio: {result2}") 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) model = AudioSeal.load_generator("audioseal_wm_16bits") detector = AudioSeal.load_detector(("audioseal_detector_16bits")) 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)