import{s as en,o as tn,n as st}from"../chunks/scheduler.182ea377.js";import{S as nn,i as sn,g as d,s as i,r as f,A as on,h as l,f as s,c as o,j as D,u as c,x as v,k as P,y as t,a as u,v as m,d as g,t as h,w as _}from"../chunks/index.abf12888.js";import{T as rn}from"../chunks/Tip.230e2334.js";import{D as k}from"../chunks/Docstring.93f6f462.js";import{C as Ut}from"../chunks/CodeBlock.57fe6e13.js";import{E as Lt}from"../chunks/ExampleCodeBlock.658f5cd6.js";import{H as Le}from"../chunks/Heading.16916d63.js";function an(j){let r,T='Make sure to check out the Schedulers <a href="../../using-diffusers/schedulers">guide</a> to learn how to explore the tradeoff between scheduler speed and quality, and see the <a href="../../using-diffusers/loading#reuse-components-across-pipelines">reuse components across pipelines</a> section to learn how to efficiently load the same components into multiple pipelines.';return{c(){r=d("p"),r.innerHTML=T},l($){r=l($,"P",{"data-svelte-h":!0}),v(r)!=="svelte-1wmc0l4"&&(r.innerHTML=T)},m($,p){u($,r,p)},p:st,d($){$&&s(r)}}}function dn(j){let r,T="For audio diffusion:",$,p,b;return p=new Ut({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> IPython.display <span class="hljs-keyword">import</span> Audio
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>
pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;teticio/audio-diffusion-256&quot;</span>).to(device)

output = pipe()
display(output.images[<span class="hljs-number">0</span>])
display(Audio(output.audios[<span class="hljs-number">0</span>], rate=mel.get_sample_rate()))`,wrap:!1}}),{c(){r=d("p"),r.textContent=T,$=i(),f(p.$$.fragment)},l(n){r=l(n,"P",{"data-svelte-h":!0}),v(r)!=="svelte-1smf6bg"&&(r.textContent=T),$=o(n),c(p.$$.fragment,n)},m(n,y){u(n,r,y),u(n,$,y),m(p,n,y),b=!0},p:st,i(n){b||(g(p.$$.fragment,n),b=!0)},o(n){h(p.$$.fragment,n),b=!1},d(n){n&&(s(r),s($)),_(p,n)}}}function ln(j){let r,T="For latent audio diffusion:",$,p,b;return p=new Ut({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> IPython.display <span class="hljs-keyword">import</span> Audio
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>
pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;teticio/latent-audio-diffusion-256&quot;</span>).to(device)

output = pipe()
display(output.images[<span class="hljs-number">0</span>])
display(Audio(output.audios[<span class="hljs-number">0</span>], rate=pipe.mel.get_sample_rate()))`,wrap:!1}}),{c(){r=d("p"),r.textContent=T,$=i(),f(p.$$.fragment)},l(n){r=l(n,"P",{"data-svelte-h":!0}),v(r)!=="svelte-1tdalp8"&&(r.textContent=T),$=o(n),c(p.$$.fragment,n)},m(n,y){u(n,r,y),u(n,$,y),m(p,n,y),b=!0},p:st,i(n){b||(g(p.$$.fragment,n),b=!0)},o(n){h(p.$$.fragment,n),b=!1},d(n){n&&(s(r),s($)),_(p,n)}}}function pn(j){let r,T="For other tasks like variation, inpainting, outpainting, etc:",$,p,b;return p=new Ut({props:{code:"b3V0cHV0JTIwJTNEJTIwcGlwZSglMEElMjAlMjAlMjAlMjByYXdfYXVkaW8lM0RvdXRwdXQuYXVkaW9zJTVCMCUyQyUyMDAlNUQlMkMlMEElMjAlMjAlMjAlMjBzdGFydF9zdGVwJTNEaW50KHBpcGUuZ2V0X2RlZmF1bHRfc3RlcHMoKSUyMCUyRiUyMDIpJTJDJTBBJTIwJTIwJTIwJTIwbWFza19zdGFydF9zZWNzJTNEMSUyQyUwQSUyMCUyMCUyMCUyMG1hc2tfZW5kX3NlY3MlM0QxJTJDJTBBKSUwQWRpc3BsYXkob3V0cHV0LmltYWdlcyU1QjAlNUQpJTBBZGlzcGxheShBdWRpbyhvdXRwdXQuYXVkaW9zJTVCMCU1RCUyQyUyMHJhdGUlM0RwaXBlLm1lbC5nZXRfc2FtcGxlX3JhdGUoKSkp",highlighted:`output = pipe(
    raw_audio=output.audios[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
    start_step=<span class="hljs-built_in">int</span>(pipe.get_default_steps() / <span class="hljs-number">2</span>),
    mask_start_secs=<span class="hljs-number">1</span>,
    mask_end_secs=<span class="hljs-number">1</span>,
)
display(output.images[<span class="hljs-number">0</span>])
display(Audio(output.audios[<span class="hljs-number">0</span>], rate=pipe.mel.get_sample_rate()))`,wrap:!1}}),{c(){r=d("p"),r.textContent=T,$=i(),f(p.$$.fragment)},l(n){r=l(n,"P",{"data-svelte-h":!0}),v(r)!=="svelte-1p5i2x8"&&(r.textContent=T),$=o(n),c(p.$$.fragment,n)},m(n,y){u(n,r,y),u(n,$,y),m(p,n,y),b=!0},p:st,i(n){b||(g(p.$$.fragment,n),b=!0)},o(n){h(p.$$.fragment,n),b=!1},d(n){n&&(s(r),s($)),_(p,n)}}}function un(j){let r,T,$,p,b,n,y,Gt='<a href="https://github.com/teticio/audio-diffusion" rel="nofollow">Audio Diffusion</a> is by Robert Dargavel Smith, and it leverages the recent advances in image generation from diffusion models by converting audio samples to and from Mel spectrogram images.',Ue,q,Nt='The original codebase, training scripts and example notebooks can be found at <a href="https://github.com/teticio/audio-diffusion" rel="nofollow">teticio/audio-diffusion</a>.',Ge,U,Ne,Y,We,w,H,it,_e,Wt="Pipeline for audio diffusion.",ot,$e,Et=`This model inherits from <a href="/docs/diffusers/v0.22.0/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`,rt,C,O,at,be,St="The call function to the pipeline for generation.",dt,ve,Vt="Examples:",lt,G,pt,N,ut,W,ft,E,K,ct,xe,Zt="Reverse the denoising step process to recover a noisy image from the generated image.",mt,S,ee,gt,ye,Xt="Returns default number of steps recommended for inference.",ht,V,te,_t,we,Bt="Spherical Linear intERPolation.",Ee,ne,Se,J,se,$t,Me,Rt="Output class for audio pipelines.",Ve,ie,Ze,L,oe,bt,De,Ft="Output class for image pipelines.",Xe,re,Be,x,ae,vt,Z,de,xt,Pe,zt="Convert slice of audio to spectrogram.",yt,X,le,wt,Te,Qt="Get slice of audio.",Mt,B,pe,Dt,Ce,qt="Get number of slices in audio.",Pt,R,ue,Tt,Ie,Yt="Get sample rate.",Ct,F,fe,It,Ae,Ht="Converts spectrogram to audio.",At,z,ce,kt,ke,Ot="Load audio.",jt,Q,me,Jt,je,Kt="Set resolution.",Re,Je,Fe;return b=new Le({props:{title:"Audio Diffusion",local:"audio-diffusion",headingTag:"h1"}}),U=new rn({props:{$$slots:{default:[an]},$$scope:{ctx:j}}}),Y=new Le({props:{title:"AudioDiffusionPipeline",local:"diffusers.AudioDiffusionPipeline",headingTag:"h2"}}),H=new k({props:{name:"class diffusers.AudioDiffusionPipeline",anchor:"diffusers.AudioDiffusionPipeline",parameters:[{name:"vqvae",val:": AutoencoderKL"},{name:"unet",val:": UNet2DConditionModel"},{name:"mel",val:": Mel"},{name:"scheduler",val:": typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_ddpm.DDPMScheduler]"}],parametersDescription:[{anchor:"diffusers.AudioDiffusionPipeline.vqae",description:`<strong>vqae</strong> (<a href="/docs/diffusers/v0.22.0/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.`,name:"vqae"},{anchor:"diffusers.AudioDiffusionPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/v0.22.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <code>UNet2DConditionModel</code> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.AudioDiffusionPipeline.mel",description:`<strong>mel</strong> (<a href="/docs/diffusers/v0.22.0/en/api/pipelines/audio_diffusion#diffusers.Mel">Mel</a>) &#x2014;
Transform audio into a spectrogram.`,name:"mel"},{anchor:"diffusers.AudioDiffusionPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.22.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a> or <a href="/docs/diffusers/v0.22.0/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents. Can be one of
<a href="/docs/diffusers/v0.22.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a> or <a href="/docs/diffusers/v0.22.0/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L30"}}),O=new k({props:{name:"__call__",anchor:"diffusers.AudioDiffusionPipeline.__call__",parameters:[{name:"batch_size",val:": int = 1"},{name:"audio_file",val:": str = None"},{name:"raw_audio",val:": ndarray = None"},{name:"slice",val:": int = 0"},{name:"start_step",val:": int = 0"},{name:"steps",val:": int = None"},{name:"generator",val:": Generator = None"},{name:"mask_start_secs",val:": float = 0"},{name:"mask_end_secs",val:": float = 0"},{name:"step_generator",val:": Generator = None"},{name:"eta",val:": float = 0"},{name:"noise",val:": Tensor = None"},{name:"encoding",val:": Tensor = None"},{name:"return_dict",val:" = True"}],parametersDescription:[{anchor:"diffusers.AudioDiffusionPipeline.__call__.batch_size",description:`<strong>batch_size</strong> (<code>int</code>) &#x2014;
Number of samples to generate.`,name:"batch_size"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.audio_file",description:`<strong>audio_file</strong> (<code>str</code>) &#x2014;
An audio file that must be on disk due to <a href="https://librosa.org/" rel="nofollow">Librosa</a> limitation.`,name:"audio_file"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.raw_audio",description:`<strong>raw_audio</strong> (<code>np.ndarray</code>) &#x2014;
The raw audio file as a NumPy array.`,name:"raw_audio"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.slice",description:`<strong>slice</strong> (<code>int</code>) &#x2014;
Slice number of audio to convert.`,name:"slice"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.start_step",description:`<strong>start_step</strong> (int) &#x2014;
Step to start diffusion from.`,name:"start_step"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.steps",description:`<strong>steps</strong> (<code>int</code>) &#x2014;
Number of denoising steps (defaults to <code>50</code> for DDIM and <code>1000</code> for DDPM).`,name:"steps"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.mask_start_secs",description:`<strong>mask_start_secs</strong> (<code>float</code>) &#x2014;
Number of seconds of audio to mask (not generate) at start.`,name:"mask_start_secs"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.mask_end_secs",description:`<strong>mask_end_secs</strong> (<code>float</code>) &#x2014;
Number of seconds of audio to mask (not generate) at end.`,name:"mask_end_secs"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.step_generator",description:`<strong>step_generator</strong> (<code>torch.Generator</code>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> used to denoise.
None`,name:"step_generator"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>) &#x2014;
Corresponds to parameter eta (&#x3B7;) from the <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">DDIM</a> paper. Only applies
to the <a href="/docs/diffusers/v0.22.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, and is ignored in other schedulers.`,name:"eta"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.noise",description:`<strong>noise</strong> (<code>torch.Tensor</code>) &#x2014;
A noise tensor of shape <code>(batch_size, 1, height, width)</code> or <code>None</code>.`,name:"noise"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.encoding",description:`<strong>encoding</strong> (<code>torch.Tensor</code>) &#x2014;
A tensor for <a href="/docs/diffusers/v0.22.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> of shape <code>(batch_size, seq_length, cross_attention_dim)</code>.`,name:"encoding"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.22.0/en/api/pipelines/audio_diffusion#diffusers.AudioPipelineOutput">AudioPipelineOutput</a>, <a href="/docs/diffusers/v0.22.0/en/api/pipelines/stable_unclip#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> or a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L70",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of Mel spectrograms (<code>float</code>, <code>List[np.ndarray]</code>) with the sample rate and raw audio.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[PIL Image]</code></p>
`}}),G=new Lt({props:{anchor:"diffusers.AudioDiffusionPipeline.__call__.example",$$slots:{default:[dn]},$$scope:{ctx:j}}}),N=new Lt({props:{anchor:"diffusers.AudioDiffusionPipeline.__call__.example-2",$$slots:{default:[ln]},$$scope:{ctx:j}}}),W=new Lt({props:{anchor:"diffusers.AudioDiffusionPipeline.__call__.example-3",$$slots:{default:[pn]},$$scope:{ctx:j}}}),K=new k({props:{name:"encode",anchor:"diffusers.AudioDiffusionPipeline.encode",parameters:[{name:"images",val:": typing.List[PIL.Image.Image]"},{name:"steps",val:": int = 50"}],parametersDescription:[{anchor:"diffusers.AudioDiffusionPipeline.encode.images",description:`<strong>images</strong> (<code>List[PIL Image]</code>) &#x2014;
List of images to encode.`,name:"images"},{anchor:"diffusers.AudioDiffusionPipeline.encode.steps",description:`<strong>steps</strong> (<code>int</code>) &#x2014;
Number of encoding steps to perform (defaults to <code>50</code>).`,name:"steps"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L270",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A noise tensor of shape <code>(batch_size, 1, height, width)</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>np.ndarray</code></p>
`}}),ee=new k({props:{name:"get_default_steps",anchor:"diffusers.AudioDiffusionPipeline.get_default_steps",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L61",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The number of steps.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>int</code></p>
`}}),te=new k({props:{name:"slerp",anchor:"diffusers.AudioDiffusionPipeline.slerp",parameters:[{name:"x0",val:": Tensor"},{name:"x1",val:": Tensor"},{name:"alpha",val:": float"}],parametersDescription:[{anchor:"diffusers.AudioDiffusionPipeline.slerp.x0",description:`<strong>x0</strong> (<code>torch.Tensor</code>) &#x2014;
The first tensor to interpolate between.`,name:"x0"},{anchor:"diffusers.AudioDiffusionPipeline.slerp.x1",description:`<strong>x1</strong> (<code>torch.Tensor</code>) &#x2014;
Second tensor to interpolate between.`,name:"x1"},{anchor:"diffusers.AudioDiffusionPipeline.slerp.alpha",description:`<strong>alpha</strong> (<code>float</code>) &#x2014;
Interpolation between 0 and 1`,name:"alpha"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L311",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The interpolated tensor.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),ne=new Le({props:{title:"AudioPipelineOutput",local:"diffusers.AudioPipelineOutput",headingTag:"h2"}}),se=new k({props:{name:"class diffusers.AudioPipelineOutput",anchor:"diffusers.AudioPipelineOutput",parameters:[{name:"audios",val:": ndarray"}],parametersDescription:[{anchor:"diffusers.AudioPipelineOutput.audios",description:`<strong>audios</strong> (<code>np.ndarray</code>) &#x2014;
List of denoised audio samples of a NumPy array of shape <code>(batch_size, num_channels, sample_rate)</code>.`,name:"audios"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/pipeline_utils.py#L124"}}),ie=new Le({props:{title:"ImagePipelineOutput",local:"diffusers.ImagePipelineOutput",headingTag:"h2"}}),oe=new k({props:{name:"class diffusers.ImagePipelineOutput",anchor:"diffusers.ImagePipelineOutput",parameters:[{name:"images",val:": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"}],parametersDescription:[{anchor:"diffusers.ImagePipelineOutput.images",description:`<strong>images</strong> (<code>List[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
List of denoised PIL images of length <code>batch_size</code> or NumPy array of shape <code>(batch_size, height, width, num_channels)</code>.`,name:"images"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/pipeline_utils.py#L110"}}),re=new Le({props:{title:"Mel",local:"diffusers.Mel",headingTag:"h2"}}),ae=new k({props:{name:"class diffusers.Mel",anchor:"diffusers.Mel",parameters:[{name:"x_res",val:": int = 256"},{name:"y_res",val:": int = 256"},{name:"sample_rate",val:": int = 22050"},{name:"n_fft",val:": int = 2048"},{name:"hop_length",val:": int = 512"},{name:"top_db",val:": int = 80"},{name:"n_iter",val:": int = 32"}],parametersDescription:[{anchor:"diffusers.Mel.x_res",description:`<strong>x_res</strong> (<code>int</code>) &#x2014;
x resolution of spectrogram (time).`,name:"x_res"},{anchor:"diffusers.Mel.y_res",description:`<strong>y_res</strong> (<code>int</code>) &#x2014;
y resolution of spectrogram (frequency bins).`,name:"y_res"},{anchor:"diffusers.Mel.sample_rate",description:`<strong>sample_rate</strong> (<code>int</code>) &#x2014;
Sample rate of audio.`,name:"sample_rate"},{anchor:"diffusers.Mel.n_fft",description:`<strong>n_fft</strong> (<code>int</code>) &#x2014;
Number of Fast Fourier Transforms.`,name:"n_fft"},{anchor:"diffusers.Mel.hop_length",description:`<strong>hop_length</strong> (<code>int</code>) &#x2014;
Hop length (a higher number is recommended if <code>y_res</code> &lt; 256).`,name:"hop_length"},{anchor:"diffusers.Mel.top_db",description:`<strong>top_db</strong> (<code>int</code>) &#x2014;
Loudest decibel value.`,name:"top_db"},{anchor:"diffusers.Mel.n_iter",description:`<strong>n_iter</strong> (<code>int</code>) &#x2014;
Number of iterations for Griffin-Lim Mel inversion.`,name:"n_iter"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/mel.py#L37"}}),de=new k({props:{name:"audio_slice_to_image",anchor:"diffusers.Mel.audio_slice_to_image",parameters:[{name:"slice",val:": int"}],parametersDescription:[{anchor:"diffusers.Mel.audio_slice_to_image.slice",description:`<strong>slice</strong> (<code>int</code>) &#x2014;
Slice number of audio to convert (out of <code>get_number_of_slices()</code>).`,name:"slice"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/mel.py#L143",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A grayscale image of <code>x_res x y_res</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>PIL Image</code></p>
`}}),le=new k({props:{name:"get_audio_slice",anchor:"diffusers.Mel.get_audio_slice",parameters:[{name:"slice",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.Mel.get_audio_slice.slice",description:`<strong>slice</strong> (<code>int</code>) &#x2014;
Slice number of audio (out of <code>get_number_of_slices()</code>).`,name:"slice"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/mel.py#L121",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The audio slice as a NumPy array.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>np.ndarray</code></p>
`}}),pe=new k({props:{name:"get_number_of_slices",anchor:"diffusers.Mel.get_number_of_slices",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/mel.py#L112",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Number of spectograms audio can be sliced into.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>int</code></p>
`}}),ue=new k({props:{name:"get_sample_rate",anchor:"diffusers.Mel.get_sample_rate",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/mel.py#L134",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Sample rate of audio.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>int</code></p>
`}}),fe=new k({props:{name:"image_to_audio",anchor:"diffusers.Mel.image_to_audio",parameters:[{name:"image",val:": Image"}],parametersDescription:[{anchor:"diffusers.Mel.image_to_audio.image",description:`<strong>image</strong> (<code>PIL Image</code>) &#x2014;
An grayscale image of <code>x_res x y_res</code>.`,name:"image"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/mel.py#L162",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The audio as a NumPy array.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>audio (<code>np.ndarray</code>)</p>
`}}),ce=new k({props:{name:"load_audio",anchor:"diffusers.Mel.load_audio",parameters:[{name:"audio_file",val:": str = None"},{name:"raw_audio",val:": ndarray = None"}],parametersDescription:[{anchor:"diffusers.Mel.load_audio.audio_file",description:`<strong>audio_file</strong> (<code>str</code>) &#x2014;
An audio file that must be on disk due to <a href="https://librosa.org/" rel="nofollow">Librosa</a> limitation.`,name:"audio_file"},{anchor:"diffusers.Mel.load_audio.raw_audio",description:`<strong>raw_audio</strong> (<code>np.ndarray</code>) &#x2014;
The raw audio file as a NumPy array.`,name:"raw_audio"}],source:"https://github.com/huggingface/diffusers/blob/v0.22.0/src/diffusers/pipelines/audio_diffusion/mel.py#L94"}}),me=new k({props:{name:"set_resolution",anchor:"diffusers.Mel.set_resolution",parameters:[{name:"x_res",val:": int"},{name:"y_res",val:": int"}],parametersDescription:[{anchor:"diffusers.Mel.set_resolution.x_res",description:`<strong>x_res</strong> (<code>int</code>) &#x2014;
x resolution of spectrogram (time).`,name:"x_res"},{anchor:"diffusers.Mel.set_resolution.y_res",description:`<strong>y_res</strong> (<code>int</code>) &#x2014;
y resolution of spectrogram (frequency 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