Delete watermarking.py

watermarking.py

@@ -1,78 +0,0 @@
-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
-
-
-@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_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()