import{S as Na,i as Za,s as Ca,e as s,k as c,w as b,t as a,M as Ba,c as n,d as o,m as p,a as i,x as y,h as r,b as l,G as e,g as u,y as w,q as M,o as D,B as T,v as Va,L as Ra}from"../../../chunks/vendor-hf-doc-builder.js";import{D as gt}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Zs}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ve}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Wa}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Fa(vt){let g,R,E,v,x;return v=new Zs({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwQXVkaW9MRE1QaXBlbGluZSUwQSUwQXBpcGUlMjAlM0QlMjBBdWRpb0xETVBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJjdnNzcCUyRmF1ZGlvbGRtJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2KSUwQXBpcGUlMjAlM0QlMjBwaXBlLnRvKCUyMmN1ZGElMjIpJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIyQSUyMGhhbW1lciUyMGhpdHRpbmclMjBhJTIwd29vZGVuJTIwc3VyZmFjZSUyMiUwQWF1ZGlvJTIwJTNEJTIwcGlwZShwcm9tcHQpLmF1ZGlvJTVCMCU1RA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AudioLDMPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = AudioLDMPipeline.from_pretrained(<span class="hljs-string">&quot;cvssp/audioldm&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A hammer hitting a wooden surface&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>audio = pipe(prompt).audio[<span class="hljs-number">0</span>]`}}),{c(){g=s("p"),R=a("Examples:"),E=c(),b(v.$$.fragment)},l(f){g=n(f,"P",{});var L=i(g);R=r(L,"Examples:"),L.forEach(o),E=p(f),y(v.$$.fragment,f)},m(f,L){u(f,g,L),e(g,R),u(f,E,L),w(v,f,L),x=!0},p:Ra,i(f){x||(M(v.$$.fragment,f),x=!0)},o(f){D(v.$$.fragment,f),x=!1},d(f){f&&o(g),f&&o(E),T(v,f)}}}function qa(vt){let g,R,E,v,x,f,L,Re,Qt,_t,j,W,We,oe,Yt,Fe,Ot,bt,F,Kt,se,eo,to,yt,P,oo,ae,so,ao,qe,no,ro,ne,io,lo,wt,k,co,re,po,uo,ie,fo,ho,Mt,I,q,He,le,mo,Xe,go,Dt,S,vo,Te,_o,bo,de,yo,wo,Tt,ce,Et,N,H,ze,pe,Mo,Qe,Do,At,Ee,To,$t,X,Ye,Eo,Ao,Oe,$o,Pt,Ae,Po,xt,z,Z,xo,Ke,Lo,ko,et,So,Uo,Go,C,Jo,tt,jo,Io,ot,No,Zo,Lt,B,Q,st,ue,Co,at,Bo,kt,h,Vo,$e,Ro,Wo,nt,Fo,qo,Pe,Ho,Xo,xe,zo,Qo,Le,Yo,Oo,ke,Ko,es,Se,ts,os,St,A,ss,Ue,as,ns,rt,rs,is,it,ls,ds,Ge,cs,ps,Ut,fe,Gt,V,Y,lt,he,us,dt,fs,Jt,m,me,hs,ct,ms,gs,ge,vs,Je,_s,bs,ys,U,ve,ws,pt,Ms,Ds,O,Ts,K,_e,Es,be,As,ut,$s,Ps,xs,ee,ye,Ls,we,ks,ft,Ss,Us,Gs,G,Me,Js,ht,js,Is,mt,Ns,jt;return f=new Ve({}),oe=new Ve({}),le=new Ve({}),ce=new Zs({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AudioLDMPipeline
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> scipy

repo_id = <span class="hljs-string">&quot;cvssp/audioldm-s-full-v2&quot;</span>
pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;Techno music with a strong, upbeat tempo and high melodic riffs&quot;</span>
audio = pipe(prompt, num_inference_steps=<span class="hljs-number">10</span>, audio_length_in_s=<span class="hljs-number">5.0</span>).audios[<span class="hljs-number">0</span>]

<span class="hljs-comment"># save the audio sample as a .wav file</span>
scipy.io.wavfile.write(<span class="hljs-string">&quot;techno.wav&quot;</span>, rate=<span class="hljs-number">16000</span>, data=audio)`}}),pe=new Ve({}),ue=new Ve({}),fe=new Zs({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AudioLDMPipeline, DPMSolverMultistepScheduler
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = AudioLDMPipeline.from_pretrained(<span class="hljs-string">&quot;cvssp/audioldm-s-full-v2&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># or</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>dpm_scheduler = DPMSolverMultistepScheduler.from_pretrained(<span class="hljs-string">&quot;cvssp/audioldm-s-full-v2&quot;</span>, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = AudioLDMPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;cvssp/audioldm-s-full-v2&quot;</span>, scheduler=dpm_scheduler, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)`}}),he=new Ve({}),me=new gt({props:{name:"class diffusers.AudioLDMPipeline",anchor:"diffusers.AudioLDMPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": ClapTextModelWithProjection"},{name:"tokenizer",val:": typing.Union[transformers.models.roberta.tokenization_roberta.RobertaTokenizer, transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast]"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"vocoder",val:": SpeechT5HifiGan"}],parametersDescription:[{anchor:"diffusers.AudioLDMPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.17.0/en/api/models#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode audios to and from latent representations.`,name:"vae"},{anchor:"diffusers.AudioLDMPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>ClapTextModelWithProjection</code>) &#x2014;
Frozen text-encoder. AudioLDM uses the text portion of
<a href="https://huggingface.co/docs/transformers/main/model_doc/clap#transformers.ClapTextModelWithProjection" rel="nofollow">CLAP</a>,
specifically the <a href="https://huggingface.co/laion/clap-htsat-unfused" rel="nofollow">RoBERTa HSTAT-unfused</a> variant.`,name:"text_encoder"},{anchor:"diffusers.AudioLDMPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>PreTrainedTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaTokenizer" rel="nofollow">RobertaTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.AudioLDMPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.17.0/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; U-Net architecture to denoise the encoded audio latents.',name:"unet"},{anchor:"diffusers.AudioLDMPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.17.0/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded audio latents. Can be one of
<a href="/docs/diffusers/v0.17.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.17.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.17.0/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.AudioLDMPipeline.vocoder",description:`<strong>vocoder</strong> (<code>SpeechT5HifiGan</code>) &#x2014;
Vocoder of class
<a href="https://huggingface.co/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5HifiGan" rel="nofollow">SpeechT5HifiGan</a>.`,name:"vocoder"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L46"}}),ve=new gt({props:{name:"__call__",anchor:"diffusers.AudioLDMPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"audio_length_in_s",val:": typing.Optional[float] = None"},{name:"num_inference_steps",val:": int = 10"},{name:"guidance_scale",val:": float = 2.5"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_waveforms_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": typing.Optional[int] = 1"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"output_type",val:": typing.Optional[str] = 'np'"}],parametersDescription:[{anchor:"diffusers.AudioLDMPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the audio generation. If not defined, one has to pass <code>prompt_embeds</code>.
instead.`,name:"prompt"},{anchor:"diffusers.AudioLDMPipeline.__call__.audio_length_in_s",description:`<strong>audio_length_in_s</strong> (<code>int</code>, <em>optional</em>, defaults to 5.12) &#x2014;
The length of the generated audio sample in seconds.`,name:"audio_length_in_s"},{anchor:"diffusers.AudioLDMPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.AudioLDMPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 2.5) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate audios that are closely linked to the text <code>prompt</code>,
usually at the expense of lower sound quality.`,name:"guidance_scale"},{anchor:"diffusers.AudioLDMPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the audio generation. If not defined, one has to pass
<code>negative_prompt_embeds</code> instead. Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code> is
less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.AudioLDMPipeline.__call__.num_waveforms_per_prompt",description:`<strong>num_waveforms_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of waveforms to generate per prompt.`,name:"num_waveforms_per_prompt"},{anchor:"diffusers.AudioLDMPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) in the DDIM paper: <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">https://arxiv.org/abs/2010.02502</a>. Only applies to
<a href="/docs/diffusers/v0.17.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.AudioLDMPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.AudioLDMPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for audio
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.AudioLDMPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.AudioLDMPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AudioLDMPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.17.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.AudioLDMPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that will be called every <code>callback_steps</code> steps during inference. The function will be
called with the following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.AudioLDMPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function will be called. If not specified, the callback will be
called at every step.`,name:"callback_steps"},{anchor:"diffusers.AudioLDMPipeline.__call__.cross_attention_kwargs",description:`<strong>cross_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttnProcessor</code> as defined under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py" rel="nofollow">diffusers.cross_attention</a>.`,name:"cross_attention_kwargs"},{anchor:"diffusers.AudioLDMPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;np&quot;</code>) &#x2014;
The output format of the generate image. Choose between:<ul>
<li><code>&quot;np&quot;</code>: Return Numpy <code>np.ndarray</code> objects.</li>
<li><code>&quot;pt&quot;</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
</ul>`,name:"output_type"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L398",returnDescription:`
<p><a
  href="/docs/diffusers/v0.17.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> if <code>return_dict</code> is True, otherwise a \`tuple.
When returning a tuple, the first element is a list with the generated audios.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.17.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),O=new Wa({props:{anchor:"diffusers.AudioLDMPipeline.__call__.example",$$slots:{default:[Fa]},$$scope:{ctx:vt}}}),_e=new gt({props:{name:"disable_vae_slicing",anchor:"diffusers.AudioLDMPipeline.disable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L104"}}),ye=new gt({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.AudioLDMPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L111"}}),Me=new gt({props:{name:"enable_vae_slicing",anchor:"diffusers.AudioLDMPipeline.enable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L94"}}),{c(){g=s("meta"),R=c(),E=s("h1"),v=s("a"),x=s("span"),b(f.$$.fragment),L=c(),Re=s("span"),Qt=a("AudioLDM"),_t=c(),j=s("h2"),W=s("a"),We=s("span"),b(oe.$$.fragment),Yt=c(),Fe=s("span"),Ot=a("Overview"),bt=c(),F=s("p"),Kt=a("AudioLDM was proposed in "),se=s("a"),eo=a("AudioLDM: Text-to-Audio Generation with Latent Diffusion Models"),to=a(" by Haohe Liu et al."),yt=c(),P=s("p"),oo=a("Inspired by "),ae=s("a"),so=a("Stable Diffusion"),ao=a(`, AudioLDM
is a text-to-audio `),qe=s("em"),no=a("latent diffusion model (LDM)"),ro=a(" that learns continuous audio representations from "),ne=s("a"),io=a("CLAP"),lo=a(`
latents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional
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