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on
Zero
Running
on
Zero
import os | |
import argparse | |
import silentcipher | |
import torch | |
import torchaudio | |
CSM_1B_HF_WATERMARK = list(map(int, os.getenv("WATERMARK_KEY").split(" "))) | |
def cli_check_audio() -> None: | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--audio_path", type=str, required=True) | |
args = parser.parse_args() | |
check_audio_from_file(args.audio_path) | |
def load_watermarker(device: str = "cuda") -> silentcipher.server.Model: | |
model = silentcipher.get_model( | |
model_type="44.1k", | |
device=device, | |
) | |
return model | |
def watermark( | |
watermarker: silentcipher.server.Model, | |
audio_array: torch.Tensor, | |
sample_rate: int, | |
watermark_key: list[int], | |
) -> tuple[torch.Tensor, int]: | |
audio_array_44khz = torchaudio.functional.resample(audio_array, orig_freq=sample_rate, new_freq=44100) | |
encoded, _ = watermarker.encode_wav(audio_array_44khz, 44100, watermark_key, calc_sdr=False, message_sdr=36) | |
output_sample_rate = min(44100, sample_rate) | |
encoded = torchaudio.functional.resample(encoded, orig_freq=44100, new_freq=output_sample_rate) | |
return encoded, output_sample_rate | |
def verify( | |
watermarker: silentcipher.server.Model, | |
watermarked_audio: torch.Tensor, | |
sample_rate: int, | |
watermark_key: list[int], | |
) -> bool: | |
watermarked_audio_44khz = torchaudio.functional.resample(watermarked_audio, orig_freq=sample_rate, new_freq=44100) | |
result = watermarker.decode_wav(watermarked_audio_44khz, 44100, phase_shift_decoding=True) | |
is_watermarked = result["status"] | |
if is_watermarked: | |
is_csm_watermarked = result["messages"][0] == watermark_key | |
else: | |
is_csm_watermarked = False | |
return is_watermarked and is_csm_watermarked | |
def check_audio_from_file(audio_path: str) -> None: | |
watermarker = load_watermarker(device="cuda") | |
audio_array, sample_rate = load_audio(audio_path) | |
is_watermarked = verify(watermarker, audio_array, sample_rate, CSM_1B_HF_WATERMARK) | |
outcome = "Watermarked" if is_watermarked else "Not watermarked" | |
print(f"{outcome}: {audio_path}") | |
def load_audio(audio_path: str) -> tuple[torch.Tensor, int]: | |
audio_array, sample_rate = torchaudio.load(audio_path) | |
audio_array = audio_array.mean(dim=0) | |
return audio_array, int(sample_rate) | |
if __name__ == "__main__": | |
cli_check_audio() | |