facebookresearch--audiocraft
Tuan Tran
112 行
3.5 KiB
Python
112 行
3.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import typing as tp
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from abc import ABC, abstractmethod
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import torch
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import torch.nn as nn
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from audiocraft.models.loaders import load_audioseal_models
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class WMModel(ABC, nn.Module):
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"""
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A wrapper interface to different watermarking models for
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training or evaluation purporses
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"""
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@abstractmethod
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def get_watermark(
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self,
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x: torch.Tensor,
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message: tp.Optional[torch.Tensor] = None,
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sample_rate: int = 16_000,
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) -> torch.Tensor:
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"""Get the watermark from an audio tensor and a message.
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If the input message is None, a random message of
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n bits {0,1} will be generated
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"""
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@abstractmethod
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def detect_watermark(self, x: torch.Tensor) -> torch.Tensor:
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"""Detect the watermarks from the audio signal
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Args:
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x: Audio signal, size batch x frames
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Returns:
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tensor of size (B, 2+n, frames) where:
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Detection results of shape (B, 2, frames)
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Message decoding results of shape (B, n, frames)
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"""
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class AudioSeal(WMModel):
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"""Wrap Audioseal (https://github.com/facebookresearch/audioseal) for the
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training and evaluation. The generator and detector are jointly trained
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"""
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def __init__(
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self,
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generator: nn.Module,
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detector: nn.Module,
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nbits: int = 0,
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):
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super().__init__()
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self.generator = generator # type: ignore
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self.detector = detector # type: ignore
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# Allow to re-train an n-bit model with new 0-bit message
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self.nbits = nbits if nbits else self.generator.msg_processor.nbits
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def get_watermark(
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self,
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x: torch.Tensor,
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message: tp.Optional[torch.Tensor] = None,
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sample_rate: int = 16_000,
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) -> torch.Tensor:
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return self.generator.get_watermark(x, message=message, sample_rate=sample_rate)
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def detect_watermark(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Detect the watermarks from the audio signal. The first two units of the output
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are used for detection, the rest is used to decode the message. If the audio is
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not watermarked, the message will be random.
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Args:
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x: Audio signal, size batch x frames
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Returns
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torch.Tensor: Detection + decoding results of shape (B, 2+nbits, T).
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"""
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# Getting the direct decoded message from the detector
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result = self.detector.detector(x) # b x 2+nbits
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# hardcode softmax on 2 first units used for detection
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result[:, :2, :] = torch.softmax(result[:, :2, :], dim=1)
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return result
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def forward( # generator
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self,
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x: torch.Tensor,
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message: tp.Optional[torch.Tensor] = None,
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sample_rate: int = 16_000,
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alpha: float = 1.0,
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) -> torch.Tensor:
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"""Apply the watermarking to the audio signal x with a tune-down ratio (default 1.0)"""
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wm = self.get_watermark(x, message)
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return x + alpha * wm
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@staticmethod
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def get_pretrained(name="base", device=None) -> WMModel:
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if device is None:
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if torch.cuda.device_count():
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device = "cuda"
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else:
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device = "cpu"
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return load_audioseal_models("facebook/audioseal", filename=name, device=device)
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