import{s as Lr,o as Sr,n as he}from"../chunks/scheduler.25b97de1.js";import{S as Yr,i as Xr,g as i,s as n,r as c,A as qr,h as l,f as o,c as a,j as $,u as m,x as g,k as w,l as Er,y as s,a as r,v as p,d as f,t as h,w as u}from"../chunks/index.d9030fc9.js";import{T as Ln}from"../chunks/Tip.baa67368.js";import{D as C}from"../chunks/Docstring.ffac8efa.js";import{C as U}from"../chunks/CodeBlock.e6cd0d95.js";import{E as Sn}from"../chunks/ExampleCodeBlock.22dfe688.js";import{H as T,E as Rr}from"../chunks/EditOnGithub.91d95064.js";function Dr(J){let d,y="Example:",b,k,v;return k=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, BarkModel

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;suno/bark-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BarkModel.from_pretrained(<span class="hljs-string">&quot;suno/bark-small&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To add a voice preset, you can pass \`voice_preset\` to \`BarkProcessor.__call__(...)\`</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>voice_preset = <span class="hljs-string">&quot;v2/en_speaker_6&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(<span class="hljs-string">&quot;Hello, my dog is cute, I need him in my life&quot;</span>, voice_preset=voice_preset)

<span class="hljs-meta">&gt;&gt;&gt; </span>audio_array = model.generate(**inputs, semantic_max_new_tokens=<span class="hljs-number">100</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_array = audio_array.cpu().numpy().squeeze()`,wrap:!1}}),{c(){d=i("p"),d.textContent=y,b=n(),c(k.$$.fragment)},l(_){d=l(_,"P",{"data-svelte-h":!0}),g(d)!=="svelte-11lpom8"&&(d.textContent=y),b=a(_),m(k.$$.fragment,_)},m(_,M){r(_,d,M),r(_,b,M),p(k,_,M),v=!0},p:he,i(_){v||(f(k.$$.fragment,_),v=!0)},o(_){h(k.$$.fragment,_),v=!1},d(_){_&&(o(d),o(b)),u(k,_)}}}function Qr(J){let d,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){d=i("p"),d.innerHTML=y},l(b){d=l(b,"P",{"data-svelte-h":!0}),g(d)!=="svelte-fincs2"&&(d.innerHTML=y)},m(b,k){r(b,d,k)},p:he,d(b){b&&o(d)}}}function Or(J){let d,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){d=i("p"),d.innerHTML=y},l(b){d=l(b,"P",{"data-svelte-h":!0}),g(d)!=="svelte-fincs2"&&(d.innerHTML=y)},m(b,k){r(b,d,k)},p:he,d(b){b&&o(d)}}}function Ar(J){let d,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){d=i("p"),d.innerHTML=y},l(b){d=l(b,"P",{"data-svelte-h":!0}),g(d)!=="svelte-fincs2"&&(d.innerHTML=y)},m(b,k){r(b,d,k)},p:he,d(b){b&&o(d)}}}function Kr(J){let d,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){d=i("p"),d.innerHTML=y},l(b){d=l(b,"P",{"data-svelte-h":!0}),g(d)!=="svelte-fincs2"&&(d.innerHTML=y)},m(b,k){r(b,d,k)},p:he,d(b){b&&o(d)}}}function es(J){let d,y="Example:",b,k,v;return k=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BarkCoarseConfig, BarkCoarseModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Bark sub-module style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BarkCoarseConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the suno/bark style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BarkCoarseModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){d=i("p"),d.textContent=y,b=n(),c(k.$$.fragment)},l(_){d=l(_,"P",{"data-svelte-h":!0}),g(d)!=="svelte-11lpom8"&&(d.textContent=y),b=a(_),m(k.$$.fragment,_)},m(_,M){r(_,d,M),r(_,b,M),p(k,_,M),v=!0},p:he,i(_){v||(f(k.$$.fragment,_),v=!0)},o(_){h(k.$$.fragment,_),v=!1},d(_){_&&(o(d),o(b)),u(k,_)}}}function ts(J){let d,y="Example:",b,k,v;return k=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BarkFineConfig, BarkFineModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Bark sub-module style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BarkFineConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the suno/bark style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BarkFineModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){d=i("p"),d.textContent=y,b=n(),c(k.$$.fragment)},l(_){d=l(_,"P",{"data-svelte-h":!0}),g(d)!=="svelte-11lpom8"&&(d.textContent=y),b=a(_),m(k.$$.fragment,_)},m(_,M){r(_,d,M),r(_,b,M),p(k,_,M),v=!0},p:he,i(_){v||(f(k.$$.fragment,_),v=!0)},o(_){h(k.$$.fragment,_),v=!1},d(_){_&&(o(d),o(b)),u(k,_)}}}function os(J){let d,y="Example:",b,k,v;return k=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BarkSemanticConfig, BarkSemanticModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Bark sub-module style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BarkSemanticConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the suno/bark style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BarkSemanticModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){d=i("p"),d.textContent=y,b=n(),c(k.$$.fragment)},l(_){d=l(_,"P",{"data-svelte-h":!0}),g(d)!=="svelte-11lpom8"&&(d.textContent=y),b=a(_),m(k.$$.fragment,_)},m(_,M){r(_,d,M),r(_,b,M),p(k,_,M),v=!0},p:he,i(_){v||(f(k.$$.fragment,_),v=!0)},o(_){h(k.$$.fragment,_),v=!1},d(_){_&&(o(d),o(b)),u(k,_)}}}function ns(J){let d,y,b,k,v,_,M,wo,ue,Ga='Bark is a transformer-based text-to-speech model proposed by Suno AI in <a href="https://github.com/suno-ai/bark" rel="nofollow">suno-ai/bark</a>.',To,ge,Ha="Bark is made of 4 main models:",Bo,_e,Va='<li><a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkSemanticModel">BarkSemanticModel</a> (also referred to as the ‘text’ model): a causal auto-regressive transformer model that takes as input tokenized text, and predicts semantic text tokens that capture the meaning of the text.</li> <li><a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCoarseModel">BarkCoarseModel</a> (also referred to as the ‘coarse acoustics’ model): a causal autoregressive transformer, that takes as input the results of the <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkSemanticModel">BarkSemanticModel</a> model. It aims at predicting the first two audio codebooks necessary for EnCodec.</li> <li><a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkFineModel">BarkFineModel</a> (the ‘fine acoustics’ model), this time a non-causal autoencoder transformer, which iteratively predicts the last codebooks based on the sum of the previous codebooks embeddings.</li> <li>having predicted all the codebook channels from the <a href="/docs/transformers/pr_31098/en/model_doc/encodec#transformers.EncodecModel">EncodecModel</a>, Bark uses it to decode the output audio array.</li>',Co,be,La="It should be noted that each of the first three modules can support conditional speaker embeddings to condition the output sound according to specific predefined voice.",xo,ke,Sa=`This model was contributed by <a href="https://huggingface.co/ylacombe" rel="nofollow">Yoach Lacombe (ylacombe)</a> and <a href="https://github.com/sanchit-gandhi" rel="nofollow">Sanchit Gandhi (sanchit-gandhi)</a>.
The original code can be found <a href="https://github.com/suno-ai/bark" rel="nofollow">here</a>.`,Jo,ye,zo,ve,Ya="Bark can be optimized with just a few extra lines of code, which <strong>significantly reduces its memory footprint</strong> and <strong>accelerates inference</strong>.",jo,Me,Uo,$e,Xa="You can speed up inference and reduce memory footprint by 50% simply by loading the model in half-precision.",Io,we,Wo,Te,Zo,Be,qa="As mentioned above, Bark is made up of 4 sub-models, which are called up sequentially during audio generation. In other words, while one sub-model is in use, the other sub-models are idle.",No,Ce,Ea="If you’re using a CUDA device, a simple solution to benefit from an 80% reduction in memory footprint is to offload the submodels from GPU to CPU when they’re idle. This operation is called <em>CPU offloading</em>. You can use it with one line of code as follows:",Po,xe,Fo,Je,Ra='Note that 🤗 Accelerate must be installed before using this feature. <a href="https://huggingface.co/docs/accelerate/basic_tutorials/install" rel="nofollow">Here’s how to install it.</a>',Go,ze,Ho,je,Da="Better Transformer is an 🤗 Optimum feature that performs kernel fusion under the hood. You can gain 20% to 30% in speed with zero performance degradation. It only requires one line of code to export the model to 🤗 Better Transformer:",Vo,Ue,Lo,Ie,Qa='Note that 🤗 Optimum must be installed before using this feature. <a href="https://huggingface.co/docs/optimum/installation" rel="nofollow">Here’s how to install it.</a>',So,We,Yo,Ze,Oa="Flash Attention 2 is an even faster, optimized version of the previous optimization.",Xo,Ne,qo,Pe,Aa='First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the <a href="https://github.com/Dao-AILab/flash-attention#installation-and-features" rel="nofollow">official documentation</a>. If your hardware is not compatible with Flash Attention 2, you can still benefit from attention kernel optimisations through Better Transformer support covered <a href="https://huggingface.co/docs/transformers/main/en/model_doc/bark#using-better-transformer" rel="nofollow">above</a>.',Eo,Fe,Ka='Next, <a href="https://github.com/Dao-AILab/flash-attention#installation-and-features" rel="nofollow">install</a> the latest version of Flash Attention 2:',Ro,Ge,Do,He,Qo,Ve,er='To load a model using Flash Attention 2, we can pass the <code>attn_implementation=&quot;flash_attention_2&quot;</code> flag to <a href="https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained" rel="nofollow"><code>.from_pretrained</code></a>. We’ll also load the model in half-precision (e.g. <code>torch.float16</code>), since it results in almost no degradation to audio quality but significantly lower memory usage and faster inference:',Oo,Le,Ao,Se,Ko,Ye,tr="The following diagram shows the latency for the native attention implementation (no optimisation) against Better Transformer and Flash Attention 2. In all cases, we generate 400 semantic tokens on a 40GB A100 GPU with PyTorch 2.1. Flash Attention 2 is also consistently faster than Better Transformer, and its performance improves even more as batch sizes increase:",en,R,or='<img src="https://huggingface.co/datasets/ylacombe/benchmark-comparison/resolve/main/Bark%20Optimization%20Benchmark.png"/>',tn,Xe,nr='To put this into perspective, on an NVIDIA A100 and when generating 400 semantic tokens with a batch size of 16, you can get 17 times the <a href="https://huggingface.co/blog/optimizing-bark#throughput" rel="nofollow">throughput</a> and still be 2 seconds faster than generating sentences one by one with the native model implementation. In other words, all the samples will be generated 17 times faster.',on,qe,ar="At batch size 8, on an NVIDIA A100, Flash Attention 2 is also 10% faster than Better Transformer, and at batch size 16, 25%.",nn,Ee,an,Re,rr="You can combine optimization techniques, and use CPU offload, half-precision and Flash Attention 2 (or 🤗 Better Transformer) all at once.",rn,De,sn,Qe,sr='Find out more on inference optimization techniques <a href="https://huggingface.co/docs/transformers/perf_infer_gpu_one" rel="nofollow">here</a>.',ln,Oe,dn,Ae,ir=`Suno offers a library of voice presets in a number of languages <a href="https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c" rel="nofollow">here</a>.
These presets are also uploaded in the hub <a href="https://huggingface.co/suno/bark-small/tree/main/speaker_embeddings" rel="nofollow">here</a> or <a href="https://huggingface.co/suno/bark/tree/main/speaker_embeddings" rel="nofollow">here</a>.`,cn,Ke,mn,et,lr="Bark can generate highly realistic, <strong>multilingual</strong> speech as well as other audio - including music, background noise and simple sound effects.",pn,tt,fn,ot,dr="The model can also produce <strong>nonverbal communications</strong> like laughing, sighing and crying.",hn,nt,un,at,cr="To save the audio, simply take the sample rate from the model config and some scipy utility:",gn,rt,_n,st,bn,z,it,Yn,Vt,mr=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkModel">BarkModel</a>. It is used to instantiate a Bark
model according to the specified sub-models configurations, defining the model architecture.`,Xn,Lt,pr=`Instantiating a configuration with the defaults will yield a similar configuration to that of the Bark
<a href="https://huggingface.co/suno/bark" rel="nofollow">suno/bark</a> architecture.`,qn,St,fr=`Configuration objects inherit from <a href="/docs/transformers/pr_31098/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/pr_31098/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,En,D,lt,Rn,Yt,hr='Instantiate a <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkConfig">BarkConfig</a> (or a derived class) from bark sub-models configuration.',kn,dt,yn,j,ct,Dn,Xt,ur="Constructs a Bark processor which wraps a text tokenizer and optional Bark voice presets into a single processor.",Qn,Q,mt,On,qt,gr=`Main method to prepare for the model one or several sequences(s). This method forwards the <code>text</code> and <code>kwargs</code>
arguments to the AutoTokenizer’s <code>__call__()</code> to encode the text. The method also proposes a
voice preset which is a dictionary of arrays that conditions <code>Bark</code>’s output. <code>kwargs</code> arguments are forwarded
to the tokenizer and to <code>cached_file</code> method if <code>voice_preset</code> is a valid filename.`,An,O,pt,Kn,Et,_r="Instantiate a Bark processor associated with a pretrained model.",ea,A,ft,ta,Rt,br=`Saves the attributes of this processor (tokenizer…) in the specified directory so that it can be reloaded
using the <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkProcessor.from_pretrained">from_pretrained()</a> method.`,vn,ht,Mn,B,ut,oa,Dt,kr="The full Bark model, a text-to-speech model composed of 4 sub-models:",na,Qt,yr=`<li><a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkSemanticModel">BarkSemanticModel</a> (also referred to as the ‘text’ model): a causal auto-regressive transformer model that
takes
as input tokenized text, and predicts semantic text tokens that capture the meaning of the text.</li> <li><a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCoarseModel">BarkCoarseModel</a> (also refered to as the ‘coarse acoustics’ model), also a causal autoregressive transformer,
that takes into input the results of the last model. It aims at regressing the first two audio codebooks necessary
to <code>encodec</code>.</li> <li><a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkFineModel">BarkFineModel</a> (the ‘fine acoustics’ model), this time a non-causal autoencoder transformer, which iteratively
predicts the last codebooks based on the sum of the previous codebooks embeddings.</li> <li>having predicted all the codebook channels from the <a href="/docs/transformers/pr_31098/en/model_doc/encodec#transformers.EncodecModel">EncodecModel</a>, Bark uses it to decode the output audio
array.</li>`,aa,Ot,vr=`It should be noted that each of the first three modules can support conditional speaker embeddings to condition the
output sound according to specific predefined voice.`,ra,At,Mr=`This model inherits from <a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,sa,Kt,$r=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,ia,V,gt,la,eo,wr="Generates audio from an input prompt and an additional optional <code>Bark</code> speaker prompt.",da,K,ca,ee,_t,ma,to,Tr=`Offloads all sub-models to CPU using accelerate, reducing memory usage with a low impact on performance. This
method moves one whole sub-model at a time to the GPU when it is used, and the sub-model remains in GPU until
the next sub-model runs.`,$n,bt,wn,I,kt,pa,oo,Br=`Bark semantic (or text) model. It shares the same architecture as the coarse model.
It is a GPT-2 like autoregressive model with a language modeling head on top.
This model inherits from <a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,fa,no,Cr=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,ha,L,yt,ua,ao,xr='The <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCausalModel">BarkCausalModel</a> forward method, overrides the <code>__call__</code> special method.',ga,te,Tn,vt,Bn,W,Mt,_a,ro,Jr=`Bark coarse acoustics model.
It shares the same architecture as the semantic (or text) model. It is a GPT-2 like autoregressive model with a
language modeling head on top.
This model inherits from <a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ba,so,zr=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,ka,S,$t,ya,io,jr='The <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCausalModel">BarkCausalModel</a> forward method, overrides the <code>__call__</code> special method.',va,oe,Cn,wt,xn,Z,Tt,Ma,lo,Ur=`Bark fine acoustics model. It is a non-causal GPT-like model with <code>config.n_codes_total</code> embedding layers and
language modeling heads, one for each codebook.
This model inherits from <a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,$a,co,Ir=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,wa,Y,Bt,Ta,mo,Wr='The <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkFineModel">BarkFineModel</a> forward method, overrides the <code>__call__</code> special method.',Ba,ne,Jn,Ct,zn,q,xt,Ca,X,Jt,xa,po,Zr='The <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCausalModel">BarkCausalModel</a> forward method, overrides the <code>__call__</code> special method.',Ja,ae,jn,zt,Un,N,jt,za,fo,Nr=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCoarseModel">BarkCoarseModel</a>. It is used to instantiate the model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Bark <a href="https://huggingface.co/suno/bark" rel="nofollow">suno/bark</a>
architecture.`,ja,ho,Pr=`Configuration objects inherit from <a href="/docs/transformers/pr_31098/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/pr_31098/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Ua,re,In,Ut,Wn,P,It,Ia,uo,Fr=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkFineModel">BarkFineModel</a>. It is used to instantiate the model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Bark <a href="https://huggingface.co/suno/bark" rel="nofollow">suno/bark</a>
architecture.`,Wa,go,Gr=`Configuration objects inherit from <a href="/docs/transformers/pr_31098/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/pr_31098/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Za,se,Zn,Wt,Nn,F,Zt,Na,_o,Hr=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkSemanticModel">BarkSemanticModel</a>. It is used to instantiate the model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Bark <a href="https://huggingface.co/suno/bark" rel="nofollow">suno/bark</a>
architecture.`,Pa,bo,Vr=`Configuration objects inherit from <a href="/docs/transformers/pr_31098/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/pr_31098/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Fa,ie,Pn,Nt,Fn,$o,Gn;return v=new T({props:{title:"Bark",local:"bark",headingTag:"h1"}}),M=new T({props:{title:"Overview",local:"overview",headingTag:"h2"}}),ye=new T({props:{title:"Optimizing Bark",local:"optimizing-bark",headingTag:"h3"}}),Me=new T({props:{title:"Using half-precision",local:"using-half-precision",headingTag:"h4"}}),we=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEJhcmtNb2RlbCUwQWltcG9ydCUyMHRvcmNoJTBBJTBBZGV2aWNlJTIwJTNEJTIwJTIyY3VkYSUyMiUyMGlmJTIwdG9yY2guY3VkYS5pc19hdmFpbGFibGUoKSUyMGVsc2UlMjAlMjJjcHUlMjIlMEFtb2RlbCUyMCUzRCUyMEJhcmtNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyc3VubyUyRmJhcmstc21hbGwlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpLnRvKGRldmljZSk=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BarkModel
<span class="hljs-keyword">import</span> torch

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>
model = BarkModel.from_pretrained(<span class="hljs-string">&quot;suno/bark-small&quot;</span>, torch_dtype=torch.float16).to(device)`,wrap:!1}}),Te=new T({props:{title:"Using CPU offload",local:"using-cpu-offload",headingTag:"h4"}}),xe=new U({props:{code:"bW9kZWwuZW5hYmxlX2NwdV9vZmZsb2FkKCk=",highlighted:"model.enable_cpu_offload()",wrap:!1}}),ze=new T({props:{title:"Using Better Transformer",local:"using-better-transformer",headingTag:"h4"}}),Ue=new U({props:{code:"bW9kZWwlMjAlM0QlMjAlMjBtb2RlbC50b19iZXR0ZXJ0cmFuc2Zvcm1lcigp",highlighted:"model =  model.to_bettertransformer()",wrap:!1}}),We=new T({props:{title:"Using Flash Attention 2",local:"using-flash-attention-2",headingTag:"h4"}}),Ne=new T({props:{title:"Installation",local:"installation",headingTag:"h5"}}),Ge=new U({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1VJTIwZmxhc2gtYXR0biUyMC0tbm8tYnVpbGQtaXNvbGF0aW9u",highlighted:"pip install -U flash-attn --no-build-isolation",wrap:!1}}),He=new T({props:{title:"Usage",local:"usage",headingTag:"h5"}}),Le=new U({props:{code:"bW9kZWwlMjAlM0QlMjBCYXJrTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMnN1bm8lMkZiYXJrLXNtYWxsJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTJDJTIwYXR0bl9pbXBsZW1lbnRhdGlvbiUzRCUyMmZsYXNoX2F0dGVudGlvbl8yJTIyKS50byhkZXZpY2Up",highlighted:'model = BarkModel.from_pretrained(<span class="hljs-string">&quot;suno/bark-small&quot;</span>, torch_dtype=torch.float16, attn_implementation=<span class="hljs-string">&quot;flash_attention_2&quot;</span>).to(device)',wrap:!1}}),Se=new T({props:{title:"Performance comparison",local:"performance-comparison",headingTag:"h5"}}),Ee=new T({props:{title:"Combining optimization techniques",local:"combining-optimization-techniques",headingTag:"h4"}}),De=new U({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BarkModel
<span class="hljs-keyword">import</span> torch

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>

<span class="hljs-comment"># load in fp16 and use Flash Attention 2</span>
model = BarkModel.from_pretrained(<span class="hljs-string">&quot;suno/bark-small&quot;</span>, torch_dtype=torch.float16, attn_implementation=<span class="hljs-string">&quot;flash_attention_2&quot;</span>).to(device)

<span class="hljs-comment"># enable CPU offload</span>
model.enable_cpu_offload()`,wrap:!1}}),Oe=new T({props:{title:"Usage tips",local:"usage-tips",headingTag:"h3"}}),Ke=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, BarkModel

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;suno/bark&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BarkModel.from_pretrained(<span class="hljs-string">&quot;suno/bark&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>voice_preset = <span class="hljs-string">&quot;v2/en_speaker_6&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, voice_preset=voice_preset)

<span class="hljs-meta">&gt;&gt;&gt; </span>audio_array = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_array = audio_array.cpu().numpy().squeeze()`,wrap:!1}}),tt=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Multilingual speech - simplified Chinese</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(<span class="hljs-string">&quot;惊人的！我会说中文&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Multilingual speech - French - let&#x27;s use a voice_preset as well</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(<span class="hljs-string">&quot;Incroyable! Je peux générer du son.&quot;</span>, voice_preset=<span class="hljs-string">&quot;fr_speaker_5&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Bark can also generate music. You can help it out by adding music notes around your lyrics.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(<span class="hljs-string">&quot;♪ Hello, my dog is cute ♪&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>audio_array = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_array = audio_array.cpu().numpy().squeeze()`,wrap:!1}}),nt=new U({props:{code:"JTIzJTIwQWRkaW5nJTIwbm9uLXNwZWVjaCUyMGN1ZXMlMjB0byUyMHRoZSUyMGlucHV0JTIwdGV4dCUwQWlucHV0cyUyMCUzRCUyMHByb2Nlc3NvciglMjJIZWxsbyUyMHVoJTIwLi4uJTIwJTVCY2xlYXJzJTIwdGhyb2F0JTVEJTJDJTIwbXklMjBkb2clMjBpcyUyMGN1dGUlMjAlNUJsYXVnaHRlciU1RCUyMiklMEElMEFhdWRpb19hcnJheSUyMCUzRCUyMG1vZGVsLmdlbmVyYXRlKCoqaW5wdXRzKSUwQWF1ZGlvX2FycmF5JTIwJTNEJTIwYXVkaW9fYXJyYXkuY3B1KCkubnVtcHkoKS5zcXVlZXplKCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Adding non-speech cues to the input text</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(<span class="hljs-string">&quot;Hello uh ... [clears throat], my dog is cute [laughter]&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>audio_array = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_array = audio_array.cpu().numpy().squeeze()`,wrap:!1}}),rt=new U({props:{code:"ZnJvbSUyMHNjaXB5LmlvLndhdmZpbGUlMjBpbXBvcnQlMjB3cml0ZSUyMGFzJTIwd3JpdGVfd2F2JTBBJTBBJTIzJTIwc2F2ZSUyMGF1ZGlvJTIwdG8lMjBkaXNrJTJDJTIwYnV0JTIwZmlyc3QlMjB0YWtlJTIwdGhlJTIwc2FtcGxlJTIwcmF0ZSUyMGZyb20lMjB0aGUlMjBtb2RlbCUyMGNvbmZpZyUwQXNhbXBsZV9yYXRlJTIwJTNEJTIwbW9kZWwuZ2VuZXJhdGlvbl9jb25maWcuc2FtcGxlX3JhdGUlMEF3cml0ZV93YXYoJTIyYmFya19nZW5lcmF0aW9uLndhdiUyMiUyQyUyMHNhbXBsZV9yYXRlJTJDJTIwYXVkaW9fYXJyYXkp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> scipy.io.wavfile <span class="hljs-keyword">import</span> write <span class="hljs-keyword">as</span> write_wav

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># save audio to disk, but first take the sample rate from the model config</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>sample_rate = model.generation_config.sample_rate
<span class="hljs-meta">&gt;&gt;&gt; </span>write_wav(<span class="hljs-string">&quot;bark_generation.wav&quot;</span>, sample_rate, audio_array)`,wrap:!1}}),st=new T({props:{title:"BarkConfig",local:"transformers.BarkConfig",headingTag:"h2"}}),it=new C({props:{name:"class transformers.BarkConfig",anchor:"transformers.BarkConfig",parameters:[{name:"semantic_config",val:": Dict = None"},{name:"coarse_acoustics_config",val:": Dict = None"},{name:"fine_acoustics_config",val:": Dict = None"},{name:"codec_config",val:": Dict = None"},{name:"initializer_range",val:" = 0.02"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarkConfig.semantic_config",description:`<strong>semantic_config</strong> (<a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkSemanticConfig">BarkSemanticConfig</a>, <em>optional</em>) &#x2014;
Configuration of the underlying semantic sub-model.`,name:"semantic_config"},{anchor:"transformers.BarkConfig.coarse_acoustics_config",description:`<strong>coarse_acoustics_config</strong> (<a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCoarseConfig">BarkCoarseConfig</a>, <em>optional</em>) &#x2014;
Configuration of the underlying coarse acoustics sub-model.`,name:"coarse_acoustics_config"},{anchor:"transformers.BarkConfig.fine_acoustics_config",description:`<strong>fine_acoustics_config</strong> (<a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkFineConfig">BarkFineConfig</a>, <em>optional</em>) &#x2014;
Configuration of the underlying fine acoustics sub-model.`,name:"fine_acoustics_config"},{anchor:"transformers.BarkConfig.codec_config",description:`<strong>codec_config</strong> (<a href="/docs/transformers/pr_31098/en/model_doc/auto#transformers.AutoConfig">AutoConfig</a>, <em>optional</em>) &#x2014;
Configuration of the underlying codec sub-model.</p>
<p>Example &#x2014;`,name:"codec_config"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/configuration_bark.py#L214"}}),lt=new C({props:{name:"from_sub_model_configs",anchor:"transformers.BarkConfig.from_sub_model_configs",parameters:[{name:"semantic_config",val:": BarkSemanticConfig"},{name:"coarse_acoustics_config",val:": BarkCoarseConfig"},{name:"fine_acoustics_config",val:": BarkFineConfig"},{name:"codec_config",val:": PretrainedConfig"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/configuration_bark.py#L304",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>An instance of a configuration object</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkConfig"
>BarkConfig</a></p>
`}}),dt=new T({props:{title:"BarkProcessor",local:"transformers.BarkProcessor",headingTag:"h2"}}),ct=new C({props:{name:"class transformers.BarkProcessor",anchor:"transformers.BarkProcessor",parameters:[{name:"tokenizer",val:""},{name:"speaker_embeddings",val:" = None"}],parametersDescription:[{anchor:"transformers.BarkProcessor.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_31098/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) &#x2014;
An instance of <a href="/docs/transformers/pr_31098/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.BarkProcessor.speaker_embeddings",description:`<strong>speaker_embeddings</strong> (<code>Dict[Dict[str]]</code>, <em>optional</em>) &#x2014;
Optional nested speaker embeddings dictionary. The first level contains voice preset names (e.g
<code>&quot;en_speaker_4&quot;</code>). The second level contains <code>&quot;semantic_prompt&quot;</code>, <code>&quot;coarse_prompt&quot;</code> and <code>&quot;fine_prompt&quot;</code>
embeddings. The values correspond to the path of the corresponding <code>np.ndarray</code>. See
<a href="https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c" rel="nofollow">here</a> for
a list of <code>voice_preset_names</code>.`,name:"speaker_embeddings"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/processing_bark.py#L35"}}),mt=new C({props:{name:"__call__",anchor:"transformers.BarkProcessor.__call__",parameters:[{name:"text",val:" = None"},{name:"voice_preset",val:" = None"},{name:"return_tensors",val:" = 'pt'"},{name:"max_length",val:" = 256"},{name:"add_special_tokens",val:" = False"},{name:"return_attention_mask",val:" = True"},{name:"return_token_type_ids",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarkProcessor.__call__.text",description:`<strong>text</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) &#x2014;
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).`,name:"text"},{anchor:"transformers.BarkProcessor.__call__.voice_preset",description:`<strong>voice_preset</strong> (<code>str</code>, <code>Dict[np.ndarray]</code>) &#x2014;
The voice preset, i.e the speaker embeddings. It can either be a valid voice_preset name, e.g
<code>&quot;en_speaker_1&quot;</code>, or directly a dictionnary of <code>np.ndarray</code> embeddings for each submodel of <code>Bark</code>. Or
it can be a valid file name of a local <code>.npz</code> single voice preset.`,name:"voice_preset"},{anchor:"transformers.BarkProcessor.__call__.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_31098/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
If set, will return tensors of a particular framework. Acceptable values are:</p>
<ul>
<li><code>&apos;pt&apos;</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>&apos;np&apos;</code>: Return NumPy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/processing_bark.py#L219",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A tuple composed of a <a
  href="/docs/transformers/pr_31098/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a>, i.e the output of the
<code>tokenizer</code> and a <a
  href="/docs/transformers/pr_31098/en/main_classes/image_processor#transformers.BatchFeature"
>BatchFeature</a>, i.e the voice preset with the right tensors type.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Tuple(<a
  href="/docs/transformers/pr_31098/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a>, <a
  href="/docs/transformers/pr_31098/en/main_classes/image_processor#transformers.BatchFeature"
>BatchFeature</a>)</p>
`}}),pt=new C({props:{name:"from_pretrained",anchor:"transformers.BarkProcessor.from_pretrained",parameters:[{name:"pretrained_processor_name_or_path",val:""},{name:"speaker_embeddings_dict_path",val:" = 'speaker_embeddings_path.json'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarkProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkProcessor">BarkProcessor</a> hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a processor saved using the <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkProcessor.save_pretrained">save_pretrained()</a>
method, e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.BarkProcessor.from_pretrained.speaker_embeddings_dict_path",description:`<strong>speaker_embeddings_dict_path</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;speaker_embeddings_path.json&quot;</code>) &#x2014;
The name of the <code>.json</code> file containing the speaker_embeddings dictionnary located in
<code>pretrained_model_name_or_path</code>. If <code>None</code>, no speaker_embeddings is loaded.
**kwargs &#x2014;
Additional keyword arguments passed along to both
<code>~tokenization_utils_base.PreTrainedTokenizer.from_pretrained</code>.`,name:"speaker_embeddings_dict_path"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/processing_bark.py#L65"}}),ft=new C({props:{name:"save_pretrained",anchor:"transformers.BarkProcessor.save_pretrained",parameters:[{name:"save_directory",val:""},{name:"speaker_embeddings_dict_path",val:" = 'speaker_embeddings_path.json'"},{name:"speaker_embeddings_directory",val:" = 'speaker_embeddings'"},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarkProcessor.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory where the tokenizer files and the speaker embeddings will be saved (directory will be created
if it does not exist).`,name:"save_directory"},{anchor:"transformers.BarkProcessor.save_pretrained.speaker_embeddings_dict_path",description:`<strong>speaker_embeddings_dict_path</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;speaker_embeddings_path.json&quot;</code>) &#x2014;
The name of the <code>.json</code> file that will contains the speaker_embeddings nested path dictionnary, if it
exists, and that will be located in <code>pretrained_model_name_or_path/speaker_embeddings_directory</code>.`,name:"speaker_embeddings_dict_path"},{anchor:"transformers.BarkProcessor.save_pretrained.speaker_embeddings_directory",description:`<strong>speaker_embeddings_directory</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;speaker_embeddings/&quot;</code>) &#x2014;
The name of the folder in which the speaker_embeddings arrays will be saved.`,name:"speaker_embeddings_directory"},{anchor:"transformers.BarkProcessor.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with <code>repo_id</code> (will default to the name of <code>save_directory</code> in your
namespace).
kwargs &#x2014;
Additional key word arguments passed along to the <a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"push_to_hub"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/processing_bark.py#L118"}}),ht=new T({props:{title:"BarkModel",local:"transformers.BarkModel",headingTag:"h2"}}),ut=new C({props:{name:"class transformers.BarkModel",anchor:"transformers.BarkModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BarkModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkConfig">BarkConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L1532"}}),gt=new C({props:{name:"generate",anchor:"transformers.BarkModel.generate",parameters:[{name:"input_ids",val:": Optional = None"},{name:"history_prompt",val:": Optional = None"},{name:"return_output_lengths",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarkModel.generate.input_ids",description:`<strong>input_ids</strong> (<code>Optional[torch.Tensor]</code> of shape (batch_size, seq_len), <em>optional</em>) &#x2014;
Input ids. Will be truncated up to 256 tokens. Note that the output audios will be as long as the
longest generation among the batch.`,name:"input_ids"},{anchor:"transformers.BarkModel.generate.history_prompt",description:`<strong>history_prompt</strong> (<code>Optional[Dict[str,torch.Tensor]]</code>, <em>optional</em>) &#x2014;
Optional <code>Bark</code> speaker prompt. Note that for now, this model takes only one speaker prompt per batch.`,name:"history_prompt"},{anchor:"transformers.BarkModel.generate.kwargs",description:`<strong>kwargs</strong> (<em>optional</em>) &#x2014; Remaining dictionary of keyword arguments. Keyword arguments are of two types:</p>
<ul>
<li>Without a prefix, they will be entered as <code>**kwargs</code> for the <code>generate</code> method of each sub-model.</li>
<li>With a <em>semantic_</em>, <em>coarse_</em>, <em>fine_</em> prefix, they will be input for the <code>generate</code> method of the
semantic, coarse and fine respectively. It has the priority over the keywords without a prefix.</li>
</ul>
<p>This means you can, for example, specify a generation strategy for all sub-models except one.`,name:"kwargs"},{anchor:"transformers.BarkModel.generate.return_output_lengths",description:`<strong>return_output_lengths</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the waveform lengths. Useful when batching.`,name:"return_output_lengths"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L1640",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><strong>audio_waveform</strong> (<code>torch.Tensor</code> of shape (batch_size, seq_len)): Generated audio waveform.
When <code>return_output_lengths=True</code>:
Returns a tuple made of:</li>
<li><strong>audio_waveform</strong> (<code>torch.Tensor</code> of shape (batch_size, seq_len)): Generated audio waveform.</li>
<li><strong>output_lengths</strong> (<code>torch.Tensor</code> of shape (batch_size)): The length of each waveform in the batch</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>By default</p>
`}}),K=new Sn({props:{anchor:"transformers.BarkModel.generate.example",$$slots:{default:[Dr]},$$scope:{ctx:J}}}),_t=new C({props:{name:"enable_cpu_offload",anchor:"transformers.BarkModel.enable_cpu_offload",parameters:[{name:"gpu_id",val:": Optional = 0"}],parametersDescription:[{anchor:"transformers.BarkModel.enable_cpu_offload.gpu_id",description:`<strong>gpu_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
GPU id on which the sub-models will be loaded and offloaded.`,name:"gpu_id"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L1583"}}),bt=new T({props:{title:"BarkSemanticModel",local:"transformers.BarkSemanticModel",headingTag:"h2"}}),kt=new C({props:{name:"class transformers.BarkSemanticModel",anchor:"transformers.BarkSemanticModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BarkSemanticModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkSemanticConfig">BarkSemanticConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L805"}}),yt=new C({props:{name:"forward",anchor:"transformers.BarkSemanticModel.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"input_embeds",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BarkSemanticModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it. Indices can be obtained using <a href="/docs/transformers/pr_31098/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31098/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31098/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BarkSemanticModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.BarkSemanticModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.BarkSemanticModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BarkSemanticModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BarkSemanticModel.forward.input_embeds",description:`<strong>input_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, input_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation.
Here, due to <code>Bark</code> particularities, if <code>past_key_values</code> is used, <code>input_embeds</code> will be ignored and you
have to use <code>input_ids</code>. If <code>past_key_values</code> is not used and <code>use_cache</code> is set to <code>True</code>, <code>input_embeds</code>
is used in priority instead of <code>input_ids</code>.`,name:"input_embeds"},{anchor:"transformers.BarkSemanticModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.BarkSemanticModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.BarkSemanticModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.BarkSemanticModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31098/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L641"}}),te=new Ln({props:{$$slots:{default:[Qr]},$$scope:{ctx:J}}}),vt=new T({props:{title:"BarkCoarseModel",local:"transformers.BarkCoarseModel",headingTag:"h2"}}),Mt=new C({props:{name:"class transformers.BarkCoarseModel",anchor:"transformers.BarkCoarseModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BarkCoarseModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCoarseConfig">BarkCoarseConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L914"}}),$t=new C({props:{name:"forward",anchor:"transformers.BarkCoarseModel.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"input_embeds",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BarkCoarseModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it. Indices can be obtained using <a href="/docs/transformers/pr_31098/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31098/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31098/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BarkCoarseModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.BarkCoarseModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.BarkCoarseModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BarkCoarseModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BarkCoarseModel.forward.input_embeds",description:`<strong>input_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, input_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation.
Here, due to <code>Bark</code> particularities, if <code>past_key_values</code> is used, <code>input_embeds</code> will be ignored and you
have to use <code>input_ids</code>. If <code>past_key_values</code> is not used and <code>use_cache</code> is set to <code>True</code>, <code>input_embeds</code>
is used in priority instead of <code>input_ids</code>.`,name:"input_embeds"},{anchor:"transformers.BarkCoarseModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.BarkCoarseModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.BarkCoarseModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.BarkCoarseModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31098/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L641"}}),oe=new Ln({props:{$$slots:{default:[Or]},$$scope:{ctx:J}}}),wt=new T({props:{title:"BarkFineModel",local:"transformers.BarkFineModel",headingTag:"h2"}}),Tt=new C({props:{name:"class transformers.BarkFineModel",anchor:"transformers.BarkFineModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BarkFineModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkFineConfig">BarkFineConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/pr_31098/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L1135"}}),Bt=new C({props:{name:"forward",anchor:"transformers.BarkFineModel.forward",parameters:[{name:"codebook_idx",val:": int"},{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"input_embeds",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BarkFineModel.forward.codebook_idx",description:`<strong>codebook_idx</strong> (<code>int</code>) &#x2014;
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Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it. Initially, indices of the first two codebooks are obtained from the <code>coarse</code> sub-model. The rest is
predicted recursively by attending the previously predicted channels. The model predicts on windows of
length 1024.`,name:"input_ids"},{anchor:"transformers.BarkFineModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.BarkFineModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BarkFineModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BarkFineModel.forward.labels",description:"<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014; NOT IMPLEMENTED YET.",name:"labels"},{anchor:"transformers.BarkFineModel.forward.input_embeds",description:`<strong>input_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, input_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. If
<code>past_key_values</code> is used, optionally only the last <code>input_embeds</code> have to be input (see
<code>past_key_values</code>). This is useful if you want more control over how to convert <code>input_ids</code> indices into
associated vectors than the model&#x2019;s internal embedding lookup matrix.`,name:"input_embeds"},{anchor:"transformers.BarkFineModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.BarkFineModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.BarkFineModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31098/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L1284"}}),ne=new Ln({props:{$$slots:{default:[Ar]},$$scope:{ctx:J}}}),Ct=new T({props:{title:"BarkCausalModel",local:"transformers.BarkCausalModel",headingTag:"h2"}}),xt=new C({props:{name:"class transformers.BarkCausalModel",anchor:"transformers.BarkCausalModel",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L550"}}),Jt=new C({props:{name:"forward",anchor:"transformers.BarkCausalModel.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"input_embeds",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BarkCausalModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
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Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
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Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.BarkCausalModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BarkCausalModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BarkCausalModel.forward.input_embeds",description:`<strong>input_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, input_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation.
Here, due to <code>Bark</code> particularities, if <code>past_key_values</code> is used, <code>input_embeds</code> will be ignored and you
have to use <code>input_ids</code>. If <code>past_key_values</code> is not used and <code>use_cache</code> is set to <code>True</code>, <code>input_embeds</code>
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If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
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Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
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Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
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Whether or not to return a <a href="/docs/transformers/pr_31098/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/modeling_bark.py#L641"}}),ae=new Ln({props:{$$slots:{default:[Kr]},$$scope:{ctx:J}}}),zt=new T({props:{title:"BarkCoarseConfig",local:"transformers.BarkCoarseConfig",headingTag:"h2"}}),jt=new C({props:{name:"class transformers.BarkCoarseConfig",anchor:"transformers.BarkCoarseConfig",parameters:[{name:"block_size",val:" = 1024"},{name:"input_vocab_size",val:" = 10048"},{name:"output_vocab_size",val:" = 10048"},{name:"num_layers",val:" = 12"},{name:"num_heads",val:" = 12"},{name:"hidden_size",val:" = 768"},{name:"dropout",val:" = 0.0"},{name:"bias",val:" = True"},{name:"initializer_range",val:" = 0.02"},{name:"use_cache",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarkCoarseConfig.block_size",description:`<strong>block_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"block_size"},{anchor:"transformers.BarkCoarseConfig.input_vocab_size",description:`<strong>input_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 10_048) &#x2014;
Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCoarseModel">BarkCoarseModel</a>. Defaults to 10_048 but should be carefully thought with
regards to the chosen sub-model.`,name:"input_vocab_size"},{anchor:"transformers.BarkCoarseConfig.output_vocab_size",description:`<strong>output_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 10_048) &#x2014;
Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
by the: <code>output_ids</code> when passing forward a <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkCoarseModel">BarkCoarseModel</a>. Defaults to 10_048 but should be carefully thought
with regards to the chosen sub-model.`,name:"output_vocab_size"},{anchor:"transformers.BarkCoarseConfig.num_layers",description:`<strong>num_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the given sub-model.`,name:"num_layers"},{anchor:"transformers.BarkCoarseConfig.num_heads",description:`<strong>num_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer architecture.`,name:"num_heads"},{anchor:"transformers.BarkCoarseConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the architecture.`,name:"hidden_size"},{anchor:"transformers.BarkCoarseConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.BarkCoarseConfig.bias",description:`<strong>bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.BarkCoarseConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/configuration_bark.py#L159"}}),re=new Sn({props:{anchor:"transformers.BarkCoarseConfig.example",$$slots:{default:[es]},$$scope:{ctx:J}}}),Ut=new T({props:{title:"BarkFineConfig",local:"transformers.BarkFineConfig",headingTag:"h2"}}),It=new C({props:{name:"class transformers.BarkFineConfig",anchor:"transformers.BarkFineConfig",parameters:[{name:"tie_word_embeddings",val:" = True"},{name:"n_codes_total",val:" = 8"},{name:"n_codes_given",val:" = 1"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarkFineConfig.block_size",description:`<strong>block_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
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Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkFineModel">BarkFineModel</a>. Defaults to 10_048 but should be carefully thought with
regards to the chosen sub-model.`,name:"input_vocab_size"},{anchor:"transformers.BarkFineConfig.output_vocab_size",description:`<strong>output_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 10_048) &#x2014;
Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
by the: <code>output_ids</code> when passing forward a <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkFineModel">BarkFineModel</a>. Defaults to 10_048 but should be carefully thought
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Number of hidden layers in the given sub-model.`,name:"num_layers"},{anchor:"transformers.BarkFineConfig.num_heads",description:`<strong>num_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer architecture.`,name:"num_heads"},{anchor:"transformers.BarkFineConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the architecture.`,name:"hidden_size"},{anchor:"transformers.BarkFineConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.BarkFineConfig.bias",description:`<strong>bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to use bias in the linear layers and layer norm layers.`,name:"bias"},{anchor:"transformers.BarkFineConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.BarkFineConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models).`,name:"use_cache"},{anchor:"transformers.BarkFineConfig.n_codes_total",description:`<strong>n_codes_total</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
The total number of audio codebooks predicted. Used in the fine acoustics sub-model.`,name:"n_codes_total"},{anchor:"transformers.BarkFineConfig.n_codes_given",description:`<strong>n_codes_given</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
sub-models.`,name:"n_codes_given"}],source:"https://github.com/huggingface/transformers/blob/vr_31098/src/transformers/models/bark/configuration_bark.py#L181"}}),se=new Sn({props:{anchor:"transformers.BarkFineConfig.example",$$slots:{default:[ts]},$$scope:{ctx:J}}}),Wt=new T({props:{title:"BarkSemanticConfig",local:"transformers.BarkSemanticConfig",headingTag:"h2"}}),Zt=new C({props:{name:"class transformers.BarkSemanticConfig",anchor:"transformers.BarkSemanticConfig",parameters:[{name:"block_size",val:" = 1024"},{name:"input_vocab_size",val:" = 10048"},{name:"output_vocab_size",val:" = 10048"},{name:"num_layers",val:" = 12"},{name:"num_heads",val:" = 12"},{name:"hidden_size",val:" = 768"},{name:"dropout",val:" = 0.0"},{name:"bias",val:" = True"},{name:"initializer_range",val:" = 0.02"},{name:"use_cache",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarkSemanticConfig.block_size",description:`<strong>block_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"block_size"},{anchor:"transformers.BarkSemanticConfig.input_vocab_size",description:`<strong>input_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 10_048) &#x2014;
Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkSemanticModel">BarkSemanticModel</a>. Defaults to 10_048 but should be carefully thought with
regards to the chosen sub-model.`,name:"input_vocab_size"},{anchor:"transformers.BarkSemanticConfig.output_vocab_size",description:`<strong>output_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 10_048) &#x2014;
Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
by the: <code>output_ids</code> when passing forward a <a href="/docs/transformers/pr_31098/en/model_doc/bark#transformers.BarkSemanticModel">BarkSemanticModel</a>. Defaults to 10_048 but should be carefully thought
with regards to the chosen sub-model.`,name:"output_vocab_size"},{anchor:"transformers.BarkSemanticConfig.num_layers",description:`<strong>num_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the given sub-model.`,name:"num_layers"},{anchor:"transformers.BarkSemanticConfig.num_heads",description:`<strong>num_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer architecture.`,name:"num_heads"},{anchor:"transformers.BarkSemanticConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the architecture.`,name:"hidden_size"},{anchor:"transformers.BarkSemanticConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.BarkSemanticConfig.bias",description:`<strong>bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to use bias in the linear layers and layer norm layers.`,name:"bias"},{anchor:"transformers.BarkSemanticConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
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