facebookresearch--audiocraft
Alexandre Défossez (autodoc)
594 行
33 KiB
HTML
594 行
33 KiB
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combines all the required components to run inference with pretrained audio
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<article id="content">
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<header>
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<h1 class="title">Module <code>audiocraft.models.genmodel</code></h1>
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</header>
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<section id="section-intro">
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<p>Base implementation for audio generative models. This base implementation
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combines all the required components to run inference with pretrained audio
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generative models. It can be easily inherited by downstream model classes to
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provide easy access to the generation API.</p>
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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.genmodel.BaseGenModel"><code class="flex name class">
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<span>class <span class="ident">BaseGenModel</span></span>
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<span>(</span><span>name: str,<br>compression_model: <a title="audiocraft.models.encodec.CompressionModel" href="encodec.html#audiocraft.models.encodec.CompressionModel">CompressionModel</a>,<br>lm: <a title="audiocraft.models.lm.LMModel" href="lm.html#audiocraft.models.lm.LMModel">LMModel</a>,<br>max_duration: float | None = None)</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 BaseGenModel(ABC):
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"""Base generative model with convenient generation API.
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Args:
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name (str): name of the model.
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compression_model (CompressionModel): Compression model
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used to map audio to invertible discrete representations.
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lm (LMModel): Language model over discrete representations.
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max_duration (float, optional): maximum duration the model can produce,
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otherwise, inferred from the training params.
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"""
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def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
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max_duration: tp.Optional[float] = None):
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self.name = name
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self.compression_model = compression_model
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self.lm = lm
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self.cfg: tp.Optional[omegaconf.DictConfig] = None
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# Just to be safe, let's put everything in eval mode.
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self.compression_model.eval()
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self.lm.eval()
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if hasattr(lm, 'cfg'):
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cfg = lm.cfg
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assert isinstance(cfg, omegaconf.DictConfig)
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self.cfg = cfg
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if self.cfg is not None:
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self.compression_model = get_wrapped_compression_model(self.compression_model, self.cfg)
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if max_duration is None:
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if self.cfg is not None:
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max_duration = lm.cfg.dataset.segment_duration # type: ignore
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else:
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raise ValueError("You must provide max_duration when building directly your GenModel")
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assert max_duration is not None
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self.max_duration: float = max_duration
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self.duration = self.max_duration
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# self.extend_stride is the length of audio extension when generating samples longer
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# than self.max_duration. NOTE: the derived class must set self.extend_stride to a
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# positive float value when generating with self.duration > self.max_duration.
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self.extend_stride: tp.Optional[float] = None
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self.device = next(iter(lm.parameters())).device
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self.generation_params: dict = {}
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self._progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None
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if self.device.type == 'cpu':
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self.autocast = TorchAutocast(enabled=False)
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else:
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self.autocast = TorchAutocast(
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enabled=True, device_type=self.device.type, dtype=torch.float16)
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@property
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def frame_rate(self) -> float:
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"""Roughly the number of AR steps per seconds."""
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return self.compression_model.frame_rate
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@property
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def sample_rate(self) -> int:
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"""Sample rate of the generated audio."""
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return self.compression_model.sample_rate
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@property
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def audio_channels(self) -> int:
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"""Audio channels of the generated audio."""
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return self.compression_model.channels
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def set_custom_progress_callback(self, progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None):
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"""Override the default progress callback."""
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self._progress_callback = progress_callback
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@abstractmethod
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def set_generation_params(self, *args, **kwargs):
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"""Set the generation parameters."""
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raise NotImplementedError("No base implementation for setting generation params.")
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@staticmethod
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@abstractmethod
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def get_pretrained(name: str, device=None):
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raise NotImplementedError("No base implementation for getting pretrained model")
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@torch.no_grad()
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def _prepare_tokens_and_attributes(
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self,
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descriptions: tp.Sequence[tp.Optional[str]],
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prompt: tp.Optional[torch.Tensor],
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) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]:
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"""Prepare model inputs.
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Args:
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descriptions (list of str): A list of strings used as text conditioning.
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prompt (torch.Tensor): A batch of waveforms used for continuation.
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"""
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attributes = [
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ConditioningAttributes(text={'description': description})
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for description in descriptions]
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if prompt is not None:
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if descriptions is not None:
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assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match"
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prompt = prompt.to(self.device)
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prompt_tokens, scale = self.compression_model.encode(prompt)
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assert scale is None
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else:
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prompt_tokens = None
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return attributes, prompt_tokens
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def generate_unconditional(self, num_samples: int, progress: bool = False,
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return_tokens: bool = False) -> tp.Union[torch.Tensor,
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tp.Tuple[torch.Tensor, torch.Tensor]]:
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"""Generate samples in an unconditional manner.
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Args:
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num_samples (int): Number of samples to be generated.
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progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
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"""
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descriptions: tp.List[tp.Optional[str]] = [None] * num_samples
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attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
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tokens = self._generate_tokens(attributes, prompt_tokens, progress)
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if return_tokens:
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return self.generate_audio(tokens), tokens
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return self.generate_audio(tokens)
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def generate(self, descriptions: tp.List[str], progress: bool = False, return_tokens: bool = False) \
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-> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, torch.Tensor]]:
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"""Generate samples conditioned on text.
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Args:
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descriptions (list of str): A list of strings used as text conditioning.
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progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
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"""
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attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
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assert prompt_tokens is None
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tokens = self._generate_tokens(attributes, prompt_tokens, progress)
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if return_tokens:
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return self.generate_audio(tokens), tokens
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return self.generate_audio(tokens)
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def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
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descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
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progress: bool = False, return_tokens: bool = False) \
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-> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, torch.Tensor]]:
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"""Generate samples conditioned on audio prompts and an optional text description.
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Args:
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prompt (torch.Tensor): A batch of waveforms used for continuation.
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Prompt should be [B, C, T], or [C, T] if only one sample is generated.
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prompt_sample_rate (int): Sampling rate of the given audio waveforms.
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descriptions (list of str, optional): A list of strings used as text conditioning. Defaults to None.
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progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
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"""
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if prompt.dim() == 2:
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prompt = prompt[None]
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if prompt.dim() != 3:
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raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
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prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels)
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if descriptions is None:
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descriptions = [None] * len(prompt)
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attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
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assert prompt_tokens is not None
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tokens = self._generate_tokens(attributes, prompt_tokens, progress)
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if return_tokens:
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return self.generate_audio(tokens), tokens
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return self.generate_audio(tokens)
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def _generate_tokens(self, attributes: tp.List[ConditioningAttributes],
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prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor:
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"""Generate discrete audio tokens given audio prompt and/or conditions.
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Args:
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attributes (list of ConditioningAttributes): Conditions used for generation (here text).
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prompt_tokens (torch.Tensor, optional): Audio prompt used for continuation.
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progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
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Returns:
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torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params.
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"""
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total_gen_len = int(self.duration * self.frame_rate)
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max_prompt_len = int(min(self.duration, self.max_duration) * self.frame_rate)
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current_gen_offset: int = 0
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def _progress_callback(generated_tokens: int, tokens_to_generate: int):
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generated_tokens += current_gen_offset
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if self._progress_callback is not None:
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# Note that total_gen_len might be quite wrong depending on the
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# codebook pattern used, but with delay it is almost accurate.
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self._progress_callback(generated_tokens, tokens_to_generate)
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else:
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print(f'{generated_tokens: 6d} / {tokens_to_generate: 6d}', end='\r')
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if prompt_tokens is not None:
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assert max_prompt_len >= prompt_tokens.shape[-1], \
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"Prompt is longer than audio to generate"
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callback = None
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if progress:
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callback = _progress_callback
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if self.duration <= self.max_duration:
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# generate by sampling from LM, simple case.
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with self.autocast:
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gen_tokens = self.lm.generate(
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prompt_tokens, attributes,
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callback=callback, max_gen_len=total_gen_len, **self.generation_params)
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else:
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assert self.extend_stride is not None, "Stride should be defined to generate beyond max_duration"
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assert self.extend_stride < self.max_duration, "Cannot stride by more than max generation duration."
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all_tokens = []
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if prompt_tokens is None:
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prompt_length = 0
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else:
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all_tokens.append(prompt_tokens)
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prompt_length = prompt_tokens.shape[-1]
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stride_tokens = int(self.frame_rate * self.extend_stride)
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while current_gen_offset + prompt_length < total_gen_len:
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time_offset = current_gen_offset / self.frame_rate
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chunk_duration = min(self.duration - time_offset, self.max_duration)
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max_gen_len = int(chunk_duration * self.frame_rate)
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with self.autocast:
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gen_tokens = self.lm.generate(
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prompt_tokens, attributes,
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callback=callback, max_gen_len=max_gen_len, **self.generation_params)
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if prompt_tokens is None:
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all_tokens.append(gen_tokens)
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else:
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all_tokens.append(gen_tokens[:, :, prompt_tokens.shape[-1]:])
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prompt_tokens = gen_tokens[:, :, stride_tokens:]
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prompt_length = prompt_tokens.shape[-1]
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current_gen_offset += stride_tokens
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gen_tokens = torch.cat(all_tokens, dim=-1)
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return gen_tokens
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def generate_audio(self, gen_tokens: torch.Tensor) -> torch.Tensor:
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"""Generate Audio from tokens."""
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assert gen_tokens.dim() == 3
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with torch.no_grad():
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gen_audio = self.compression_model.decode(gen_tokens, None)
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return gen_audio</code></pre>
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</details>
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<div class="desc"><p>Base generative model with convenient generation API.</p>
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<h2 id="args">Args</h2>
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<dl>
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<dt><strong><code>name</code></strong> : <code>str</code></dt>
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<dd>name of the model.</dd>
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<dt><strong><code>compression_model</code></strong> : <code>CompressionModel</code></dt>
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<dd>Compression model
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used to map audio to invertible discrete representations.</dd>
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<dt><strong><code>lm</code></strong> : <code>LMModel</code></dt>
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<dd>Language model over discrete representations.</dd>
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<dt><strong><code>max_duration</code></strong> : <code>float</code>, optional</dt>
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<dd>maximum duration the model can produce,
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otherwise, inferred from the training params.</dd>
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</dl></div>
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<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>abc.ABC</li>
|
||
</ul>
|
||
<h3>Subclasses</h3>
|
||
<ul class="hlist">
|
||
<li><a title="audiocraft.models.audiogen.AudioGen" href="audiogen.html#audiocraft.models.audiogen.AudioGen">AudioGen</a></li>
|
||
<li><a title="audiocraft.models.jasco.JASCO" href="jasco.html#audiocraft.models.jasco.JASCO">JASCO</a></li>
|
||
<li><a title="audiocraft.models.magnet.MAGNeT" href="magnet.html#audiocraft.models.magnet.MAGNeT">MAGNeT</a></li>
|
||
<li><a title="audiocraft.models.musicgen.MusicGen" href="musicgen.html#audiocraft.models.musicgen.MusicGen">MusicGen</a></li>
|
||
</ul>
|
||
<h3>Static methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.get_pretrained"><code class="name flex">
|
||
<span>def <span class="ident">get_pretrained</span></span>(<span>name: str, device=None)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@staticmethod
|
||
@abstractmethod
|
||
def get_pretrained(name: str, device=None):
|
||
raise NotImplementedError("No base implementation for getting pretrained model")</code></pre>
|
||
</details>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Instance variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.audio_channels"><code class="name">prop <span class="ident">audio_channels</span> : int</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@property
|
||
def audio_channels(self) -> int:
|
||
"""Audio channels of the generated audio."""
|
||
return self.compression_model.channels</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Audio channels of the generated audio.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.frame_rate"><code class="name">prop <span class="ident">frame_rate</span> : float</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@property
|
||
def frame_rate(self) -> float:
|
||
"""Roughly the number of AR steps per seconds."""
|
||
return self.compression_model.frame_rate</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Roughly the number of AR steps per seconds.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.sample_rate"><code class="name">prop <span class="ident">sample_rate</span> : int</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@property
|
||
def sample_rate(self) -> int:
|
||
"""Sample rate of the generated audio."""
|
||
return self.compression_model.sample_rate</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Sample rate of the generated audio.</p></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.generate"><code class="name flex">
|
||
<span>def <span class="ident">generate</span></span>(<span>self,<br>descriptions: List[str],<br>progress: bool = False,<br>return_tokens: bool = False) ‑> torch.Tensor | Tuple[torch.Tensor, torch.Tensor]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def generate(self, descriptions: tp.List[str], progress: bool = False, return_tokens: bool = False) \
|
||
-> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, torch.Tensor]]:
|
||
"""Generate samples conditioned on text.
|
||
|
||
Args:
|
||
descriptions (list of str): A list of strings used as text conditioning.
|
||
progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
|
||
"""
|
||
attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
|
||
assert prompt_tokens is None
|
||
tokens = self._generate_tokens(attributes, prompt_tokens, progress)
|
||
if return_tokens:
|
||
return self.generate_audio(tokens), tokens
|
||
return self.generate_audio(tokens)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Generate samples conditioned on text.</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>descriptions</code></strong> : <code>list</code> of <code>str</code></dt>
|
||
<dd>A list of strings used as text conditioning.</dd>
|
||
<dt><strong><code>progress</code></strong> : <code>bool</code>, optional</dt>
|
||
<dd>Flag to display progress of the generation process. Defaults to False.</dd>
|
||
</dl></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.generate_audio"><code class="name flex">
|
||
<span>def <span class="ident">generate_audio</span></span>(<span>self, gen_tokens: torch.Tensor) ‑> torch.Tensor</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def generate_audio(self, gen_tokens: torch.Tensor) -> torch.Tensor:
|
||
"""Generate Audio from tokens."""
|
||
assert gen_tokens.dim() == 3
|
||
with torch.no_grad():
|
||
gen_audio = self.compression_model.decode(gen_tokens, None)
|
||
return gen_audio</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Generate Audio from tokens.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.generate_continuation"><code class="name flex">
|
||
<span>def <span class="ident">generate_continuation</span></span>(<span>self,<br>prompt: torch.Tensor,<br>prompt_sample_rate: int,<br>descriptions: List[str | None] | None = None,<br>progress: bool = False,<br>return_tokens: bool = False) ‑> torch.Tensor | Tuple[torch.Tensor, torch.Tensor]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
|
||
descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
|
||
progress: bool = False, return_tokens: bool = False) \
|
||
-> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, torch.Tensor]]:
|
||
"""Generate samples conditioned on audio prompts and an optional text description.
|
||
|
||
Args:
|
||
prompt (torch.Tensor): A batch of waveforms used for continuation.
|
||
Prompt should be [B, C, T], or [C, T] if only one sample is generated.
|
||
prompt_sample_rate (int): Sampling rate of the given audio waveforms.
|
||
descriptions (list of str, optional): A list of strings used as text conditioning. Defaults to None.
|
||
progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
|
||
"""
|
||
if prompt.dim() == 2:
|
||
prompt = prompt[None]
|
||
if prompt.dim() != 3:
|
||
raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
|
||
prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels)
|
||
if descriptions is None:
|
||
descriptions = [None] * len(prompt)
|
||
attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
|
||
assert prompt_tokens is not None
|
||
tokens = self._generate_tokens(attributes, prompt_tokens, progress)
|
||
if return_tokens:
|
||
return self.generate_audio(tokens), tokens
|
||
return self.generate_audio(tokens)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Generate samples conditioned on audio prompts and an optional text description.</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>prompt</code></strong> : <code>torch.Tensor</code></dt>
|
||
<dd>A batch of waveforms used for continuation.
|
||
Prompt should be [B, C, T], or [C, T] if only one sample is generated.</dd>
|
||
<dt><strong><code>prompt_sample_rate</code></strong> : <code>int</code></dt>
|
||
<dd>Sampling rate of the given audio waveforms.</dd>
|
||
<dt><strong><code>descriptions</code></strong> : <code>list</code> of <code>str</code>, optional</dt>
|
||
<dd>A list of strings used as text conditioning. Defaults to None.</dd>
|
||
<dt><strong><code>progress</code></strong> : <code>bool</code>, optional</dt>
|
||
<dd>Flag to display progress of the generation process. Defaults to False.</dd>
|
||
</dl></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.generate_unconditional"><code class="name flex">
|
||
<span>def <span class="ident">generate_unconditional</span></span>(<span>self, num_samples: int, progress: bool = False, return_tokens: bool = False) ‑> torch.Tensor | Tuple[torch.Tensor, torch.Tensor]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def generate_unconditional(self, num_samples: int, progress: bool = False,
|
||
return_tokens: bool = False) -> tp.Union[torch.Tensor,
|
||
tp.Tuple[torch.Tensor, torch.Tensor]]:
|
||
"""Generate samples in an unconditional manner.
|
||
|
||
Args:
|
||
num_samples (int): Number of samples to be generated.
|
||
progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
|
||
"""
|
||
descriptions: tp.List[tp.Optional[str]] = [None] * num_samples
|
||
attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
|
||
tokens = self._generate_tokens(attributes, prompt_tokens, progress)
|
||
if return_tokens:
|
||
return self.generate_audio(tokens), tokens
|
||
return self.generate_audio(tokens)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Generate samples in an unconditional manner.</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>num_samples</code></strong> : <code>int</code></dt>
|
||
<dd>Number of samples to be generated.</dd>
|
||
<dt><strong><code>progress</code></strong> : <code>bool</code>, optional</dt>
|
||
<dd>Flag to display progress of the generation process. Defaults to False.</dd>
|
||
</dl></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.set_custom_progress_callback"><code class="name flex">
|
||
<span>def <span class="ident">set_custom_progress_callback</span></span>(<span>self, progress_callback: Callable[[int, int], None] | None = None)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def set_custom_progress_callback(self, progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None):
|
||
"""Override the default progress callback."""
|
||
self._progress_callback = progress_callback</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Override the default progress callback.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.models.genmodel.BaseGenModel.set_generation_params"><code class="name flex">
|
||
<span>def <span class="ident">set_generation_params</span></span>(<span>self, *args, **kwargs)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@abstractmethod
|
||
def set_generation_params(self, *args, **kwargs):
|
||
"""Set the generation parameters."""
|
||
raise NotImplementedError("No base implementation for setting generation params.")</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Set the generation parameters.</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.genmodel.BaseGenModel" href="#audiocraft.models.genmodel.BaseGenModel">BaseGenModel</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.audio_channels" href="#audiocraft.models.genmodel.BaseGenModel.audio_channels">audio_channels</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.frame_rate" href="#audiocraft.models.genmodel.BaseGenModel.frame_rate">frame_rate</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.generate" href="#audiocraft.models.genmodel.BaseGenModel.generate">generate</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.generate_audio" href="#audiocraft.models.genmodel.BaseGenModel.generate_audio">generate_audio</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.generate_continuation" href="#audiocraft.models.genmodel.BaseGenModel.generate_continuation">generate_continuation</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.generate_unconditional" href="#audiocraft.models.genmodel.BaseGenModel.generate_unconditional">generate_unconditional</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.get_pretrained" href="#audiocraft.models.genmodel.BaseGenModel.get_pretrained">get_pretrained</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.sample_rate" href="#audiocraft.models.genmodel.BaseGenModel.sample_rate">sample_rate</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.set_custom_progress_callback" href="#audiocraft.models.genmodel.BaseGenModel.set_custom_progress_callback">set_custom_progress_callback</a></code></li>
|
||
<li><code><a title="audiocraft.models.genmodel.BaseGenModel.set_generation_params" href="#audiocraft.models.genmodel.BaseGenModel.set_generation_params">set_generation_params</a></code></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</nav>
|
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||
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|
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