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import gradio as gr
import numpy as np
import os
import datetime
import torch
import soundfile
from wavmark.utils import file_reader
import wavmark
def my_read_file(audio_path, max_second, default_sr=16000):
signal, sr, audio_length_second = file_reader.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 add_watermark(audio_path, watermark_text, max_second_encode=60):
assert len(watermark_text) == 16
watermark_npy = np.array([int(i) for i in watermark_text])
signal, sr, audio_length_second = my_read_file(audio_path, max_second_encode)
watermarked_signal, _ = wavmark.encode_watermark(model, signal, watermark_npy, show_progress=False)
tmp_file_name = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + "_" + watermark_text + ".wav"
tmp_file_path = '/tmp/' + tmp_file_name
soundfile.write(tmp_file_path, watermarked_signal, sr)
return tmp_file_path
def decode_watermark(audio_path, max_second_decode=30):
assert os.path.exists(audio_path)
signal, sr, audio_length_second = my_read_file(audio_path, max_second_decode)
payload_decoded, _ = wavmark.decode_watermark(model, signal, show_progress=False)
if payload_decoded is None:
return "No Watermark"
return "".join([str(i) for i in payload_decoded])
def create_default_value(len_start_bit=16):
def_val_npy = np.random.choice([0, 1], size=32 - len_start_bit)
return "".join([str(i) for i in def_val_npy])
def main():
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown("# Audio WaterMarking")
with gr.Row():
gr.Markdown("You can upload an audio file and encode a custom 16-bit watermark or perform decoding from a watermarked audio. See [WaveMark toolkit](https://github.com/wavmark/wavmark) for further details.")
with gr.Row():
audio_file = gr.Audio(label="Upload Audio", type="filepath")
action = gr.Radio(["Add Watermark", "Decode Watermark"], label="Select Action")
watermark_text = gr.Textbox(label="The watermark (0, 1 list of length-16):", value=create_default_value())
submit_button = gr.Button("Submit")
with gr.Row():
output = gr.Audio(label="Processed Audio")
decode_output = gr.Textbox(label="Decoded Watermark")
def process_audio(audio_file, action, watermark_text):
if action == "Add Watermark" and audio_file:
return add_watermark(audio_file, watermark_text), None
elif action == "Decode Watermark" and audio_file:
return None, decode_watermark(audio_file)
else:
return None, None
submit_button.click(process_audio, inputs=[audio_file, action, watermark_text], outputs=[output, decode_output])
demo.launch()
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()
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