facebookresearch--audiocraft
Alexandre Défossez (autodoc)
444 行
22 KiB
HTML
444 行
22 KiB
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<article id="content">
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<header>
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<h1 class="title">Module <code>audiocraft.models.watermark</code></h1>
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</header>
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<section id="section-intro">
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="audiocraft.models.watermark.AudioSeal"><code class="flex name class">
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<span>class <span class="ident">AudioSeal</span></span>
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<span>(</span><span>generator: torch.nn.modules.module.Module,<br>detector: torch.nn.modules.module.Module,<br>nbits: int = 0)</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">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)</code></pre>
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</details>
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<div class="desc"><p>Wrap Audioseal (<a href="https://github.com/facebookresearch/audioseal">https://github.com/facebookresearch/audioseal</a>) for the
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training and evaluation. The generator and detector are jointly trained</p>
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<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
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<h3>Ancestors</h3>
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<ul class="hlist">
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<li><a title="audiocraft.models.watermark.WMModel" href="#audiocraft.models.watermark.WMModel">WMModel</a></li>
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||
<li>abc.ABC</li>
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||
<li>torch.nn.modules.module.Module</li>
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||
</ul>
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<h3>Class variables</h3>
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<dl>
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<dt id="audiocraft.models.watermark.AudioSeal.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
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||
<dd>
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||
<div class="desc"></div>
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||
</dd>
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||
<dt id="audiocraft.models.watermark.AudioSeal.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.watermark.AudioSeal.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Static methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.models.watermark.AudioSeal.get_pretrained"><code class="name flex">
|
||
<span>def <span class="ident">get_pretrained</span></span>(<span>name='base', device=None) ‑> <a title="audiocraft.models.watermark.WMModel" href="#audiocraft.models.watermark.WMModel">WMModel</a></span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@staticmethod
|
||
def get_pretrained(name="base", device=None) -> WMModel:
|
||
if device is None:
|
||
if torch.cuda.device_count():
|
||
device = "cuda"
|
||
else:
|
||
device = "cpu"
|
||
return load_audioseal_models("facebook/audioseal", filename=name, device=device)</code></pre>
|
||
</details>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.models.watermark.AudioSeal.detect_watermark"><code class="name flex">
|
||
<span>def <span class="ident">detect_watermark</span></span>(<span>self, x: torch.Tensor) ‑> torch.Tensor</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def detect_watermark(self, x: torch.Tensor) -> torch.Tensor:
|
||
"""
|
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Detect the watermarks from the audio signal. The first two units of the output
|
||
are used for detection, the rest is used to decode the message. If the audio is
|
||
not watermarked, the message will be random.
|
||
|
||
Args:
|
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x: Audio signal, size batch x frames
|
||
Returns
|
||
torch.Tensor: Detection + decoding results of shape (B, 2+nbits, T).
|
||
"""
|
||
|
||
# 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
|
||
result[:, :2, :] = torch.softmax(result[:, :2, :], dim=1)
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return result</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Detect the watermarks from the audio signal.
|
||
The first two units of the output
|
||
are used for detection, the rest is used to decode the message. If the audio is
|
||
not watermarked, the message will be random.</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>x</code></strong></dt>
|
||
<dd>Audio signal, size batch x frames</dd>
|
||
</dl>
|
||
<p>Returns
|
||
torch.Tensor: Detection + decoding results of shape (B, 2+nbits, T).</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.watermark.AudioSeal.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self,<br>x: torch.Tensor,<br>message: torch.Tensor | None = None,<br>sample_rate: int = 16000,<br>alpha: float = 1.0) ‑> torch.Tensor</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def forward( # generator
|
||
self,
|
||
x: torch.Tensor,
|
||
message: tp.Optional[torch.Tensor] = None,
|
||
sample_rate: int = 16_000,
|
||
alpha: float = 1.0,
|
||
) -> torch.Tensor:
|
||
"""Apply the watermarking to the audio signal x with a tune-down ratio (default 1.0)"""
|
||
wm = self.get_watermark(x, message)
|
||
return x + alpha * wm</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Apply the watermarking to the audio signal x with a tune-down ratio (default 1.0)</p></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Inherited members</h3>
|
||
<ul class="hlist">
|
||
<li><code><b><a title="audiocraft.models.watermark.WMModel" href="#audiocraft.models.watermark.WMModel">WMModel</a></b></code>:
|
||
<ul class="hlist">
|
||
<li><code><a title="audiocraft.models.watermark.WMModel.get_watermark" href="#audiocraft.models.watermark.WMModel.get_watermark">get_watermark</a></code></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</dd>
|
||
<dt id="audiocraft.models.watermark.WMModel"><code class="flex name class">
|
||
<span>class <span class="ident">WMModel</span></span>
|
||
<span>(</span><span>*args, **kwargs)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class WMModel(ABC, nn.Module):
|
||
"""
|
||
A wrapper interface to different watermarking models for
|
||
training or evaluation purporses
|
||
"""
|
||
|
||
@abstractmethod
|
||
def get_watermark(
|
||
self,
|
||
x: torch.Tensor,
|
||
message: tp.Optional[torch.Tensor] = None,
|
||
sample_rate: int = 16_000,
|
||
) -> torch.Tensor:
|
||
"""Get the watermark from an audio tensor and a message.
|
||
If the input message is None, a random message of
|
||
n bits {0,1} will be generated
|
||
"""
|
||
|
||
@abstractmethod
|
||
def detect_watermark(self, x: torch.Tensor) -> torch.Tensor:
|
||
"""Detect the watermarks from the audio signal
|
||
|
||
Args:
|
||
x: Audio signal, size batch x frames
|
||
|
||
Returns:
|
||
tensor of size (B, 2+n, frames) where:
|
||
Detection results of shape (B, 2, frames)
|
||
Message decoding results of shape (B, n, frames)
|
||
"""</code></pre>
|
||
</details>
|
||
<div class="desc"><p>A wrapper interface to different watermarking models for
|
||
training or evaluation purporses</p>
|
||
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>abc.ABC</li>
|
||
<li>torch.nn.modules.module.Module</li>
|
||
</ul>
|
||
<h3>Subclasses</h3>
|
||
<ul class="hlist">
|
||
<li><a title="audiocraft.models.watermark.AudioSeal" href="#audiocraft.models.watermark.AudioSeal">AudioSeal</a></li>
|
||
</ul>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.models.watermark.WMModel.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.watermark.WMModel.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.watermark.WMModel.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.models.watermark.WMModel.detect_watermark"><code class="name flex">
|
||
<span>def <span class="ident">detect_watermark</span></span>(<span>self, x: torch.Tensor) ‑> torch.Tensor</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@abstractmethod
|
||
def detect_watermark(self, x: torch.Tensor) -> torch.Tensor:
|
||
"""Detect the watermarks from the audio signal
|
||
|
||
Args:
|
||
x: Audio signal, size batch x frames
|
||
|
||
Returns:
|
||
tensor of size (B, 2+n, frames) where:
|
||
Detection results of shape (B, 2, frames)
|
||
Message decoding results of shape (B, n, frames)
|
||
"""</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Detect the watermarks from the audio signal</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>x</code></strong></dt>
|
||
<dd>Audio signal, size batch x frames</dd>
|
||
</dl>
|
||
<h2 id="returns">Returns</h2>
|
||
<p>tensor of size (B, 2+n, frames) where:
|
||
Detection results of shape (B, 2, frames)
|
||
Message decoding results of shape (B, n, frames)</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.watermark.WMModel.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self, *input: Any) ‑> None</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def _forward_unimplemented(self, *input: Any) -> None:
|
||
r"""Defines the computation performed at every call.
|
||
|
||
Should be overridden by all subclasses.
|
||
|
||
.. note::
|
||
Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:`Module` instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.
|
||
"""
|
||
raise NotImplementedError(f"Module [{type(self).__name__}] is missing the required \"forward\" function")</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.watermark.WMModel.get_watermark"><code class="name flex">
|
||
<span>def <span class="ident">get_watermark</span></span>(<span>self,<br>x: torch.Tensor,<br>message: torch.Tensor | None = None,<br>sample_rate: int = 16000) ‑> torch.Tensor</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@abstractmethod
|
||
def get_watermark(
|
||
self,
|
||
x: torch.Tensor,
|
||
message: tp.Optional[torch.Tensor] = None,
|
||
sample_rate: int = 16_000,
|
||
) -> torch.Tensor:
|
||
"""Get the watermark from an audio tensor and a message.
|
||
If the input message is None, a random message of
|
||
n bits {0,1} will be generated
|
||
"""</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Get the watermark from an audio tensor and a message.
|
||
If the input message is None, a random message of
|
||
n bits {0,1} will be generated</p></div>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
</dl>
|
||
</section>
|
||
</article>
|
||
<nav id="sidebar">
|
||
<div class="toc">
|
||
<ul></ul>
|
||
</div>
|
||
<ul id="index">
|
||
<li><h3>Super-module</h3>
|
||
<ul>
|
||
<li><code><a title="audiocraft.models" href="index.html">audiocraft.models</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||
<ul>
|
||
<li>
|
||
<h4><code><a title="audiocraft.models.watermark.AudioSeal" href="#audiocraft.models.watermark.AudioSeal">AudioSeal</a></code></h4>
|
||
<ul class="two-column">
|
||
<li><code><a title="audiocraft.models.watermark.AudioSeal.call_super_init" href="#audiocraft.models.watermark.AudioSeal.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.AudioSeal.detect_watermark" href="#audiocraft.models.watermark.AudioSeal.detect_watermark">detect_watermark</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.AudioSeal.dump_patches" href="#audiocraft.models.watermark.AudioSeal.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.AudioSeal.forward" href="#audiocraft.models.watermark.AudioSeal.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.AudioSeal.get_pretrained" href="#audiocraft.models.watermark.AudioSeal.get_pretrained">get_pretrained</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.AudioSeal.training" href="#audiocraft.models.watermark.AudioSeal.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<h4><code><a title="audiocraft.models.watermark.WMModel" href="#audiocraft.models.watermark.WMModel">WMModel</a></code></h4>
|
||
<ul class="two-column">
|
||
<li><code><a title="audiocraft.models.watermark.WMModel.call_super_init" href="#audiocraft.models.watermark.WMModel.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.WMModel.detect_watermark" href="#audiocraft.models.watermark.WMModel.detect_watermark">detect_watermark</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.WMModel.dump_patches" href="#audiocraft.models.watermark.WMModel.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.WMModel.forward" href="#audiocraft.models.watermark.WMModel.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.WMModel.get_watermark" href="#audiocraft.models.watermark.WMModel.get_watermark">get_watermark</a></code></li>
|
||
<li><code><a title="audiocraft.models.watermark.WMModel.training" href="#audiocraft.models.watermark.WMModel.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
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<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.5</a>.</p>
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