80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
import argparse
|
|
|
|
import silentcipher
|
|
import torch
|
|
import torchaudio
|
|
|
|
# This watermark key is public, it is not secure.
|
|
# If using CSM 1B in another application, use a new private key and keep it secret.
|
|
CSM_1B_GH_WATERMARK = [212, 211, 146, 56, 201]
|
|
|
|
|
|
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
|
|
|
|
|
|
@torch.inference_mode()
|
|
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
|
|
|
|
|
|
@torch.inference_mode()
|
|
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_GH_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()
|