import{S as Bi,i as Ri,s as Wi,e as a,k as p,w as T,t as s,M as Ui,c as r,d as t,m as u,a as i,x as k,h as n,b as h,G as e,g,y as $,q as C,o as H,B as E,v as Vi,L as ts}from"../../chunks/vendor-hf-doc-builder.js";import{T as Po}from"../../chunks/Tip-hf-doc-builder.js";import{D as ot}from"../../chunks/Docstring-hf-doc-builder.js";import{C as os}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as wt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as es}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Ki(M){let d,y,f,m,v;return m=new os({props:{code:`from transformers import HubertModel, HubertConfig

# Initializing a Hubert facebook/hubert-base-ls960 style configuration
configuration = HubertConfig()

# Initializing a model from the facebook/hubert-base-ls960 style configuration
model = HubertModel(configuration)

# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> HubertModel, HubertConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Hubert facebook/hubert-base-ls960 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = HubertConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the facebook/hubert-base-ls960 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = HubertModel(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`}}),{c(){d=a("p"),y=s("Example:"),f=p(),T(m.$$.fragment)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Example:"),c.forEach(t),f=u(l),k(m.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,f,c),$(m,l,c),v=!0},p:ts,i(l){v||(C(m.$$.fragment,l),v=!0)},o(l){H(m.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(f),E(m,l)}}}function Yi(M){let d,y,f,m,v;return{c(){d=a("p"),y=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),m=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=r(c,"CODE",{});var F=i(f);m=n(F,"Module"),F.forEach(t),v=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,f),e(f,m),e(d,v)},d(l){l&&t(d)}}}function Gi(M){let d,y,f,m,v;return m=new os({props:{code:`from transformers import AutoProcessor, HubertModel
from datasets import load_dataset
import soundfile as sf

processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")


def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch


ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)

input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
hidden_states = model(input_values).last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, HubertModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> soundfile <span class="hljs-keyword">as</span> sf

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = HubertModel.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">map_to_array</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span>    speech, _ = sf.read(batch[<span class="hljs-string">&quot;file&quot;</span>])
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;speech&quot;</span>] = speech
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> batch


<span class="hljs-meta">&gt;&gt;&gt; </span>ds = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_dummy&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = ds.<span class="hljs-built_in">map</span>(map_to_array)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_values = processor(ds[<span class="hljs-string">&quot;speech&quot;</span>][<span class="hljs-number">0</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_values  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_states = model(input_values).last_hidden_state`}}),{c(){d=a("p"),y=s("Example:"),f=p(),T(m.$$.fragment)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Example:"),c.forEach(t),f=u(l),k(m.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,f,c),$(m,l,c),v=!0},p:ts,i(l){v||(C(m.$$.fragment,l),v=!0)},o(l){H(m.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(f),E(m,l)}}}function Xi(M){let d,y,f,m,v;return{c(){d=a("p"),y=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),m=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=r(c,"CODE",{});var F=i(f);m=n(F,"Module"),F.forEach(t),v=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,f),e(f,m),e(d,v)},d(l){l&&t(d)}}}function Ji(M){let d,y,f,m,v;return m=new os({props:{code:`from transformers import AutoProcessor, HubertForCTC
from datasets import load_dataset
import torch

dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
dataset = dataset.sort("id")
sampling_rate = dataset.features["audio"].sampling_rate

processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")

# audio file is decoded on the fly
inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)

# transcribe speech
transcription = processor.batch_decode(predicted_ids)
transcription[0]

inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="pt").input_ids

# compute loss
loss = model(**inputs).loss
round(loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, HubertForCTC
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = HubertForCTC.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># audio file is decoded on the fly</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_ids = torch.argmax(logits, dim=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># transcribe speech</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>transcription = processor.batch_decode(predicted_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>transcription[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs[<span class="hljs-string">&quot;labels&quot;</span>] = processor(text=dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;text&quot;</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># compute loss</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs).loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">22.68</span>`}}),{c(){d=a("p"),y=s("Example:"),f=p(),T(m.$$.fragment)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Example:"),c.forEach(t),f=u(l),k(m.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,f,c),$(m,l,c),v=!0},p:ts,i(l){v||(C(m.$$.fragment,l),v=!0)},o(l){H(m.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(f),E(m,l)}}}function Qi(M){let d,y,f,m,v;return{c(){d=a("p"),y=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),m=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=r(c,"CODE",{});var F=i(f);m=n(F,"Module"),F.forEach(t),v=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,f),e(f,m),e(d,v)},d(l){l&&t(d)}}}function Zi(M){let d,y,f,m,v;return m=new os({props:{code:`from transformers import AutoFeatureExtractor, HubertForSequenceClassification
from datasets import load_dataset
import torch

dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
dataset = dataset.sort("id")
sampling_rate = dataset.features["audio"].sampling_rate

feature_extractor = AutoFeatureExtractor.from_pretrained("superb/hubert-base-superb-ks")
model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ks")

# audio file is decoded on the fly
inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_ids = torch.argmax(logits, dim=-1).item()
predicted_label = model.config.id2label[predicted_class_ids]
predicted_label

# compute loss - target_label is e.g. "down"
target_label = model.config.id2label[0]
inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
loss = model(**inputs).loss
round(loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, HubertForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;superb/hubert-base-superb-ks&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = HubertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;superb/hubert-base-superb-ks&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># audio file is decoded on the fly</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = feature_extractor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_ids = torch.argmax(logits, dim=-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = model.config.id2label[predicted_class_ids]
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label
<span class="hljs-string">&#x27;_unknown_&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># compute loss - target_label is e.g. &quot;down&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_label = model.config.id2label[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs[<span class="hljs-string">&quot;labels&quot;</span>] = torch.tensor([model.config.label2id[target_label]])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs).loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">8.53</span>`}}),{c(){d=a("p"),y=s("Example:"),f=p(),T(m.$$.fragment)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Example:"),c.forEach(t),f=u(l),k(m.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,f,c),$(m,l,c),v=!0},p:ts,i(l){v||(C(m.$$.fragment,l),v=!0)},o(l){H(m.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(f),E(m,l)}}}function el(M){let d,y,f,m,v,l,c,F,Ie,we,S,X,ee,w,Le,U,Ne,Te,L,Be,te,oe,Re,ke,R,We,$e,W,ge,Ue,me,q,O,Ce,N,_e,Ve,se,ne,Ke,A,Ye,ae,He,J,re,ie,Ge,V,Ee,D,Q,le,P,Xe,z,Je,je;return{c(){d=a("p"),y=s("TensorFlow models and layers in "),f=a("code"),m=s("transformers"),v=s(" accept two formats as input:"),l=p(),c=a("ul"),F=a("li"),Ie=s("having all inputs as keyword arguments (like PyTorch models), or"),we=p(),S=a("li"),X=s("having all inputs as a list, tuple or dict in the first positional argument."),ee=p(),w=a("p"),Le=s(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),U=a("code"),Ne=s("model.fit()"),Te=s(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=a("code"),Be=s("model.fit()"),te=s(` supports! If, however, you want to use the second
format outside of Keras methods like `),oe=a("code"),Re=s("fit()"),ke=s(" and "),R=a("code"),We=s("predict()"),$e=s(`, such as when creating your own layers or models with
the Keras `),W=a("code"),ge=s("Functional"),Ue=s(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),me=p(),q=a("ul"),O=a("li"),Ce=s("a single Tensor with "),N=a("code"),_e=s("input_values"),Ve=s(" only and nothing else: "),se=a("code"),ne=s("model(input_values)"),Ke=p(),A=a("li"),Ye=s(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),ae=a("code"),He=s("model([input_values, attention_mask])"),J=s(" or "),re=a("code"),ie=s("model([input_values, attention_mask, token_type_ids])"),Ge=p(),V=a("li"),Ee=s(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
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`),z=a("a"),Je=s("subclassing"),je=s(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(b){d=r(b,"P",{});var j=i(d);y=n(j,"TensorFlow models and layers in "),f=r(j,"CODE",{});var Fe=i(f);m=n(Fe,"transformers"),Fe.forEach(t),v=n(j," accept two formats as input:"),j.forEach(t),l=u(b),c=r(b,"UL",{});var be=i(c);F=r(be,"LI",{});var rt=i(F);Ie=n(rt,"having all inputs as keyword arguments (like PyTorch models), or"),rt.forEach(t),we=u(be),S=r(be,"LI",{});var Qe=i(S);X=n(Qe,"having all inputs as a list, tuple or dict in the first positional argument."),Qe.forEach(t),be.forEach(t),ee=u(b),w=r(b,"P",{});var x=i(w);Le=n(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),U=r(x,"CODE",{});var it=i(U);Ne=n(it,"model.fit()"),it.forEach(t),Te=n(x,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=r(x,"CODE",{});var st=i(L);Be=n(st,"model.fit()"),st.forEach(t),te=n(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),oe=r(x,"CODE",{});var de=i(oe);Re=n(de,"fit()"),de.forEach(t),ke=n(x," and "),R=r(x,"CODE",{});var fe=i(R);We=n(fe,"predict()"),fe.forEach(t),$e=n(x,`, such as when creating your own layers or models with
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positional argument:`),x.forEach(t),me=u(b),q=r(b,"UL",{});var I=i(q);O=r(I,"LI",{});var ce=i(O);Ce=n(ce,"a single Tensor with "),N=r(ce,"CODE",{});var et=i(N);_e=n(et,"input_values"),et.forEach(t),Ve=n(ce," only and nothing else: "),se=r(ce,"CODE",{});var lt=i(se);ne=n(lt,"model(input_values)"),lt.forEach(t),ce.forEach(t),Ke=u(I),A=r(I,"LI",{});var Z=i(A);Ye=n(Z,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),ae=r(Z,"CODE",{});var B=i(ae);He=n(B,"model([input_values, attention_mask])"),B.forEach(t),J=n(Z," or "),re=r(Z,"CODE",{});var ve=i(re);ie=n(ve,"model([input_values, attention_mask, token_type_ids])"),ve.forEach(t),Z.forEach(t),Ge=u(I),V=r(I,"LI",{});var tt=i(V);Ee=n(tt,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),D=r(tt,"CODE",{});var pe=i(D);Q=n(pe,'model({"input_values": input_values, "token_type_ids": token_type_ids})'),pe.forEach(t),tt.forEach(t),I.forEach(t),le=u(b),P=r(b,"P",{});var ye=i(P);Xe=n(ye,`Note that when creating models and layers with
`),z=r(ye,"A",{href:!0,rel:!0});var xe=i(z);Je=n(xe,"subclassing"),xe.forEach(t),je=n(ye,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ye.forEach(t),this.h()},h(){h(z,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),h(z,"rel","nofollow")},m(b,j){g(b,d,j),e(d,y),e(d,f),e(f,m),e(d,v),g(b,l,j),g(b,c,j),e(c,F),e(F,Ie),e(c,we),e(c,S),e(S,X),g(b,ee,j),g(b,w,j),e(w,Le),e(w,U),e(U,Ne),e(w,Te),e(w,L),e(L,Be),e(w,te),e(w,oe),e(oe,Re),e(w,ke),e(w,R),e(R,We),e(w,$e),e(w,W),e(W,ge),e(w,Ue),g(b,me,j),g(b,q,j),e(q,O),e(O,Ce),e(O,N),e(N,_e),e(O,Ve),e(O,se),e(se,ne),e(q,Ke),e(q,A),e(A,Ye),e(A,ae),e(ae,He),e(A,J),e(A,re),e(re,ie),e(q,Ge),e(q,V),e(V,Ee),e(V,D),e(D,Q),g(b,le,j),g(b,P,j),e(P,Xe),e(P,z),e(z,Je),e(P,je)},d(b){b&&t(d),b&&t(l),b&&t(c),b&&t(ee),b&&t(w),b&&t(me),b&&t(q),b&&t(le),b&&t(P)}}}function tl(M){let d,y,f,m,v;return{c(){d=a("p"),y=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),m=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=r(c,"CODE",{});var F=i(f);m=n(F,"Module"),F.forEach(t),v=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,f),e(f,m),e(d,v)},d(l){l&&t(d)}}}function ol(M){let d,y,f,m,v;return m=new os({props:{code:`from transformers import AutoProcessor, TFHubertModel
from datasets import load_dataset
import soundfile as sf

processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
model = TFHubertModel.from_pretrained("facebook/hubert-large-ls960-ft")


def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch


ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)

input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
hidden_states = model(input_values).last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, TFHubertModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> soundfile <span class="hljs-keyword">as</span> sf

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFHubertModel.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">map_to_array</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span>    speech, _ = sf.read(batch[<span class="hljs-string">&quot;file&quot;</span>])
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;speech&quot;</span>] = speech
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> batch


<span class="hljs-meta">&gt;&gt;&gt; </span>ds = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_dummy&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = ds.<span class="hljs-built_in">map</span>(map_to_array)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_values = processor(ds[<span class="hljs-string">&quot;speech&quot;</span>][<span class="hljs-number">0</span>], return_tensors=<span class="hljs-string">&quot;tf&quot;</span>).input_values  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_states = model(input_values).last_hidden_state`}}),{c(){d=a("p"),y=s("Example:"),f=p(),T(m.$$.fragment)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Example:"),c.forEach(t),f=u(l),k(m.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,f,c),$(m,l,c),v=!0},p:ts,i(l){v||(C(m.$$.fragment,l),v=!0)},o(l){H(m.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(f),E(m,l)}}}function sl(M){let d,y,f,m,v,l,c,F,Ie,we,S,X,ee,w,Le,U,Ne,Te,L,Be,te,oe,Re,ke,R,We,$e,W,ge,Ue,me,q,O,Ce,N,_e,Ve,se,ne,Ke,A,Ye,ae,He,J,re,ie,Ge,V,Ee,D,Q,le,P,Xe,z,Je,je;return{c(){d=a("p"),y=s("TensorFlow models and layers in "),f=a("code"),m=s("transformers"),v=s(" accept two formats as input:"),l=p(),c=a("ul"),F=a("li"),Ie=s("having all inputs as keyword arguments (like PyTorch models), or"),we=p(),S=a("li"),X=s("having all inputs as a list, tuple or dict in the first positional argument."),ee=p(),w=a("p"),Le=s(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),U=a("code"),Ne=s("model.fit()"),Te=s(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=a("code"),Be=s("model.fit()"),te=s(` supports! If, however, you want to use the second
format outside of Keras methods like `),oe=a("code"),Re=s("fit()"),ke=s(" and "),R=a("code"),We=s("predict()"),$e=s(`, such as when creating your own layers or models with
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positional argument:`),me=p(),q=a("ul"),O=a("li"),Ce=s("a single Tensor with "),N=a("code"),_e=s("input_values"),Ve=s(" only and nothing else: "),se=a("code"),ne=s("model(input_values)"),Ke=p(),A=a("li"),Ye=s(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),z=a("a"),Je=s("subclassing"),je=s(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(b){d=r(b,"P",{});var j=i(d);y=n(j,"TensorFlow models and layers in "),f=r(j,"CODE",{});var Fe=i(f);m=n(Fe,"transformers"),Fe.forEach(t),v=n(j," accept two formats as input:"),j.forEach(t),l=u(b),c=r(b,"UL",{});var be=i(c);F=r(be,"LI",{});var rt=i(F);Ie=n(rt,"having all inputs as keyword arguments (like PyTorch models), or"),rt.forEach(t),we=u(be),S=r(be,"LI",{});var Qe=i(S);X=n(Qe,"having all inputs as a list, tuple or dict in the first positional argument."),Qe.forEach(t),be.forEach(t),ee=u(b),w=r(b,"P",{});var x=i(w);Le=n(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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pass your inputs and labels in any format that `),L=r(x,"CODE",{});var st=i(L);Be=n(st,"model.fit()"),st.forEach(t),te=n(x,` supports! If, however, you want to use the second
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`),ae=r(Z,"CODE",{});var B=i(ae);He=n(B,"model([input_values, attention_mask])"),B.forEach(t),J=n(Z," or "),re=r(Z,"CODE",{});var ve=i(re);ie=n(ve,"model([input_values, attention_mask, token_type_ids])"),ve.forEach(t),Z.forEach(t),Ge=u(I),V=r(I,"LI",{});var tt=i(V);Ee=n(tt,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),D=r(tt,"CODE",{});var pe=i(D);Q=n(pe,'model({"input_values": input_values, "token_type_ids": token_type_ids})'),pe.forEach(t),tt.forEach(t),I.forEach(t),le=u(b),P=r(b,"P",{});var ye=i(P);Xe=n(ye,`Note that when creating models and layers with
`),z=r(ye,"A",{href:!0,rel:!0});var xe=i(z);Je=n(xe,"subclassing"),xe.forEach(t),je=n(ye,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ye.forEach(t),this.h()},h(){h(z,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),h(z,"rel","nofollow")},m(b,j){g(b,d,j),e(d,y),e(d,f),e(f,m),e(d,v),g(b,l,j),g(b,c,j),e(c,F),e(F,Ie),e(c,we),e(c,S),e(S,X),g(b,ee,j),g(b,w,j),e(w,Le),e(w,U),e(U,Ne),e(w,Te),e(w,L),e(L,Be),e(w,te),e(w,oe),e(oe,Re),e(w,ke),e(w,R),e(R,We),e(w,$e),e(w,W),e(W,ge),e(w,Ue),g(b,me,j),g(b,q,j),e(q,O),e(O,Ce),e(O,N),e(N,_e),e(O,Ve),e(O,se),e(se,ne),e(q,Ke),e(q,A),e(A,Ye),e(A,ae),e(ae,He),e(A,J),e(A,re),e(re,ie),e(q,Ge),e(q,V),e(V,Ee),e(V,D),e(D,Q),g(b,le,j),g(b,P,j),e(P,Xe),e(P,z),e(z,Je),e(P,je)},d(b){b&&t(d),b&&t(l),b&&t(c),b&&t(ee),b&&t(w),b&&t(me),b&&t(q),b&&t(le),b&&t(P)}}}function nl(M){let d,y,f,m,v;return{c(){d=a("p"),y=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),m=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=r(c,"CODE",{});var F=i(f);m=n(F,"Module"),F.forEach(t),v=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,f),e(f,m),e(d,v)},d(l){l&&t(d)}}}function al(M){let d,y,f,m,v;return m=new os({props:{code:`import tensorflow as tf
from transformers import AutoProcessor, TFHubertForCTC
from datasets import load_dataset
import soundfile as sf

processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
model = TFHubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")


def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch


ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)

input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
logits = model(input_values).logits
predicted_ids = tf.argmax(logits, axis=-1)

transcription = processor.decode(predicted_ids[0])

# compute loss
target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"

# Pass the transcription as text to encode labels
labels = processor(text=transcription, return_tensors="tf").input_values

loss = model(input_values, labels=labels).loss`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, TFHubertForCTC
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> soundfile <span class="hljs-keyword">as</span> sf

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFHubertForCTC.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">map_to_array</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span>    speech, _ = sf.read(batch[<span class="hljs-string">&quot;file&quot;</span>])
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;speech&quot;</span>] = speech
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> batch


<span class="hljs-meta">&gt;&gt;&gt; </span>ds = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_dummy&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = ds.<span class="hljs-built_in">map</span>(map_to_array)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_values = processor(ds[<span class="hljs-string">&quot;speech&quot;</span>][<span class="hljs-number">0</span>], return_tensors=<span class="hljs-string">&quot;tf&quot;</span>).input_values  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(input_values).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_ids = tf.argmax(logits, axis=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>transcription = processor.decode(predicted_ids[<span class="hljs-number">0</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># compute loss</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_transcription = <span class="hljs-string">&quot;A MAN SAID TO THE UNIVERSE SIR I EXIST&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Pass the transcription as text to encode labels</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = processor(text=transcription, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>).input_values

<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(input_values, labels=labels).loss`}}),{c(){d=a("p"),y=s("Example:"),f=p(),T(m.$$.fragment)},l(l){d=r(l,"P",{});var c=i(d);y=n(c,"Example:"),c.forEach(t),f=u(l),k(m.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,f,c),$(m,l,c),v=!0},p:ts,i(l){v||(C(m.$$.fragment,l),v=!0)},o(l){H(m.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(f),E(m,l)}}}function rl(M){let d,y,f,m,v,l,c,F,Ie,we,S,X,ee,w,Le,U,Ne,Te,L,Be,te,oe,Re,ke,R,We,$e,W,ge,Ue,me,q,O,Ce,N,_e,Ve,se,ne,Ke,A,Ye,ae,He,J,re,ie,Ge,V,Ee,D,Q,le,P,Xe,z,Je,je,b,j,Fe,be,rt,Qe,x,it,st,de,fe,Ze,I,ce,et,lt,Z,B,ve,tt,pe,ye,xe,mn,fn,Vt,gn,_n,bn,dt,vn,Ao,yn,wn,So,Tn,kn,$n,Tt,qs,ct,kt,ss,Kt,Cn,ns,Hn,Ps,ue,Yt,En,Gt,jn,Xt,Fn,xn,Mn,Jt,qn,Oo,Pn,An,Sn,Qt,On,Zt,Dn,zn,In,Me,eo,Ln,pt,Nn,Do,Bn,Rn,as,Wn,Un,Vn,$t,Kn,Ct,As,ut,Ht,rs,to,Yn,is,Gn,Ss,he,oo,Xn,ht,Jn,ls,Qn,Zn,so,ea,ta,oa,no,sa,zo,na,aa,ra,ao,ia,ro,la,da,ca,qe,io,pa,mt,ua,Io,ha,ma,ds,fa,ga,_a,Et,ba,jt,Os,ft,Ft,cs,lo,va,ps,ya,Ds,K,co,wa,us,Ta,ka,po,$a,uo,Ca,Ha,Ea,ho,ja,Lo,Fa,xa,Ma,mo,qa,fo,Pa,Aa,Sa,Pe,go,Oa,gt,Da,No,za,Ia,hs,La,Na,Ba,xt,Ra,Mt,zs,_t,qt,ms,_o,Wa,fs,Ua,Is,Y,bo,Va,gs,Ka,Ya,vo,Ga,Bo,Xa,Ja,Qa,yo,Za,wo,er,tr,or,Pt,sr,Ae,To,nr,bt,ar,Ro,rr,ir,_s,lr,dr,cr,At,pr,St,Ls,vt,Ot,bs,ko,ur,vs,hr,Ns,G,$o,mr,Co,fr,ys,gr,_r,br,Ho,vr,Wo,yr,wr,Tr,Eo,kr,jo,$r,Cr,Hr,Dt,Er,Se,Fo,jr,yt,Fr,Uo,xr,Mr,ws,qr,Pr,Ar,zt,Sr,It,Bs;return l=new wt({}),w=new wt({}),P=new wt({}),I=new wt({}),ve=new ot({props:{name:"class transformers.HubertConfig",anchor:"transformers.HubertConfig",parameters:[{name:"vocab_size",val:" = 32"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout",val:" = 0.1"},{name:"activation_dropout",val:" = 0.1"},{name:"attention_dropout",val:" = 0.1"},{name:"feat_proj_layer_norm",val:" = True"},{name:"feat_proj_dropout",val:" = 0.0"},{name:"final_dropout",val:" = 0.1"},{name:"layerdrop",val:" = 0.1"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"feat_extract_norm",val:" = 'group'"},{name:"feat_extract_activation",val:" = 'gelu'"},{name:"conv_dim",val:" = (512, 512, 512, 512, 512, 512, 512)"},{name:"conv_stride",val:" = (5, 2, 2, 2, 2, 2, 2)"},{name:"conv_kernel",val:" = (10, 3, 3, 3, 3, 2, 2)"},{name:"conv_bias",val:" = False"},{name:"num_conv_pos_embeddings",val:" = 128"},{name:"num_conv_pos_embedding_groups",val:" = 16"},{name:"do_stable_layer_norm",val:" = False"},{name:"apply_spec_augment",val:" = True"},{name:"mask_time_prob",val:" = 0.05"},{name:"mask_time_length",val:" = 10"},{name:"mask_time_min_masks",val:" = 2"},{name:"mask_feature_prob",val:" = 0.0"},{name:"mask_feature_length",val:" = 10"},{name:"mask_feature_min_masks",val:" = 0"},{name:"ctc_loss_reduction",val:" = 'sum'"},{name:"ctc_zero_infinity",val:" = False"},{name:"use_weighted_layer_sum",val:" = False"},{name:"classifier_proj_size",val:" = 256"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 1"},{name:"eos_token_id",val:" = 2"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.HubertConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Vocabulary size of the Hubert model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertModel">HubertModel</a>. Vocabulary size of the model. Defines the different
tokens that can be represented by the <em>inputs_ids</em> passed to the forward method of <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertModel">HubertModel</a>.`,name:"vocab_size"},{anchor:"transformers.HubertConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.HubertConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.HubertConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.HubertConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.HubertConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.HubertConfig.hidden_dropout(float,",description:`<strong>hidden_dropout(<code>float</code>,</strong> <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout(float,"},{anchor:"transformers.HubertConfig.attention_dropout(float,",description:`<strong>attention_dropout(<code>float</code>,</strong> <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout(float,"},{anchor:"transformers.HubertConfig.final_dropout",description:`<strong>final_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilitiy for the final projection layer of <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a>.`,name:"final_dropout"},{anchor:"transformers.HubertConfig.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.HubertConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.HubertConfig.feat_extract_norm",description:`<strong>feat_extract_norm</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;group&quot;</code>) &#x2014;
The norm to be applied to 1D convolutional layers in feature encoder. One of <code>&quot;group&quot;</code> for group
normalization of only the first 1D convolutional layer or <code>&quot;layer&quot;</code> for layer normalization of all 1D
convolutional layers.`,name:"feat_extract_norm"},{anchor:"transformers.HubertConfig.feat_proj_dropout",description:`<strong>feat_proj_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for output of the feature encoder.`,name:"feat_proj_dropout"},{anchor:"transformers.HubertConfig.feat_proj_layer_norm",description:`<strong>feat_proj_layer_norm</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to apply LayerNorm to the output of the feature encoder.`,name:"feat_proj_layer_norm"},{anchor:"transformers.HubertConfig.feat_extract_activation",description:"<strong>feat_extract_activation</strong> (<code>str, </code>optional<code>, defaults to </code>&#x201C;gelu&#x201D;<code>) -- The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, </code>&#x201C;gelu&#x201D;<code>, </code>&#x201C;relu&#x201D;<code>, </code>&#x201C;selu&#x201D;<code>and</code>&#x201C;gelu_new&#x201D;` are supported.",name:"feat_extract_activation"},{anchor:"transformers.HubertConfig.conv_dim",description:`<strong>conv_dim</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to <code>(512, 512, 512, 512, 512, 512, 512)</code>) &#x2014;
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of <em>conv_dim</em> defines the number of 1D convolutional layers.`,name:"conv_dim"},{anchor:"transformers.HubertConfig.conv_stride",description:`<strong>conv_stride</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to <code>(5, 2, 2, 2, 2, 2, 2)</code>) &#x2014;
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of <em>conv_stride</em> defines the number of convolutional layers and has to match the length of <em>conv_dim</em>.`,name:"conv_stride"},{anchor:"transformers.HubertConfig.conv_kernel",description:`<strong>conv_kernel</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to <code>(10, 3, 3, 3, 3, 3, 3)</code>) &#x2014;
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of <em>conv_kernel</em> defines the number of convolutional layers and has to match the length of
<em>conv_dim</em>.`,name:"conv_kernel"},{anchor:"transformers.HubertConfig.conv_bias",description:`<strong>conv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether the 1D convolutional layers have a bias.`,name:"conv_bias"},{anchor:"transformers.HubertConfig.num_conv_pos_embeddings",description:`<strong>num_conv_pos_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.`,name:"num_conv_pos_embeddings"},{anchor:"transformers.HubertConfig.num_conv_pos_embedding_groups",description:`<strong>num_conv_pos_embedding_groups</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of groups of 1D convolutional positional embeddings layer.`,name:"num_conv_pos_embedding_groups"},{anchor:"transformers.HubertConfig.do_stable_layer_norm",description:`<strong>do_stable_layer_norm</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether do apply <em>stable</em> layer norm architecture of the Transformer encoder. <code>do_stable_layer_norm is True</code> corresponds to applying layer norm before the attention layer, whereas <code>do_stable_layer_norm is False</code> corresponds to applying layer norm after the attention layer.`,name:"do_stable_layer_norm"},{anchor:"transformers.HubertConfig.apply_spec_augment",description:`<strong>apply_spec_augment</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to apply <em>SpecAugment</em> data augmentation to the outputs of the feature encoder. For reference see
<a href="https://arxiv.org/abs/1904.08779" rel="nofollow">SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition</a>.`,name:"apply_spec_augment"},{anchor:"transformers.HubertConfig.mask_time_prob",description:`<strong>mask_time_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.05) &#x2014;
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates &#x201D;mask_time_prob<em>len(time_axis)/mask_time_length&#x201D; independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, </em>mask_time_prob<em> should be \`prob_vector_start</em>mask_time_length<code>. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if </code>apply_spec_augment is True\`.`,name:"mask_time_prob"},{anchor:"transformers.HubertConfig.mask_time_length",description:`<strong>mask_time_length</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
Length of vector span along the time axis.`,name:"mask_time_length"},{anchor:"transformers.HubertConfig.mask_time_min_masks",description:`<strong>mask_time_min_masks</strong> (<code>int</code>, <em>optional</em>, defaults to 2), &#x2014;
The minimum number of masks of length <code>mask_feature_length</code> generated along the time axis, each time step,
irrespectively of <code>mask_feature_prob</code>. Only relevant if &#x201D;mask_time_prob*len(time_axis)/mask_time_length &lt;
mask_time_min_masks&#x201D;`,name:"mask_time_min_masks"},{anchor:"transformers.HubertConfig.mask_feature_prob",description:`<strong>mask_feature_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates &#x201D;mask_feature_prob<em>len(feature_axis)/mask_time_length&#x201D; independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, </em>mask_feature_prob<em> should be \`prob_vector_start</em>mask_feature_length<code>. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if </code>apply_spec_augment is
True\`.`,name:"mask_feature_prob"},{anchor:"transformers.HubertConfig.mask_feature_length",description:`<strong>mask_feature_length</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
Length of vector span along the feature axis.`,name:"mask_feature_length"},{anchor:"transformers.HubertConfig.mask_feature_min_masks",description:`<strong>mask_feature_min_masks</strong> (<code>int</code>, <em>optional</em>, defaults to 0), &#x2014;
The minimum number of masks of length <code>mask_feature_length</code> generated along the feature axis, each time
step, irrespectively of <code>mask_feature_prob</code>. Only relevant if
&#x201D;mask_feature_prob*len(feature_axis)/mask_feature_length &lt; mask_feature_min_masks&#x201D;`,name:"mask_feature_min_masks"},{anchor:"transformers.HubertConfig.ctc_loss_reduction",description:`<strong>ctc_loss_reduction</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;sum&quot;</code>) &#x2014;
Specifies the reduction to apply to the output of <code>torch.nn.CTCLoss</code>. Only relevant when training an
instance of <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a>.`,name:"ctc_loss_reduction"},{anchor:"transformers.HubertConfig.ctc_zero_infinity",description:`<strong>ctc_zero_infinity</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to zero infinite losses and the associated gradients of <code>torch.nn.CTCLoss</code>. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a>.`,name:"ctc_zero_infinity"},{anchor:"transformers.HubertConfig.use_weighted_layer_sum",description:`<strong>use_weighted_layer_sum</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a>.`,name:"use_weighted_layer_sum"},{anchor:"transformers.HubertConfig.classifier_proj_size",description:`<strong>classifier_proj_size</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimensionality of the projection before token mean-pooling for classification.`,name:"classifier_proj_size"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/hubert/configuration_hubert.py#L32"}}),Tt=new es({props:{anchor:"transformers.HubertConfig.example",$$slots:{default:[Ki]},$$scope:{ctx:M}}}),Kt=new wt({}),Yt=new ot({props:{name:"class transformers.HubertModel",anchor:"transformers.HubertModel",parameters:[{name:"config",val:": HubertConfig"}],parametersDescription:[{anchor:"transformers.HubertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</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/v4.27.1/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/v4.27.1/src/transformers/models/hubert/modeling_hubert.py#L955"}}),eo=new ot({props:{name:"forward",anchor:"transformers.HubertModel.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"mask_time_indices",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.HubertModel.forward.input_values",description:`<strong>input_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform. Values can be obtained by loading a <code>.flac</code> or <code>.wav</code> audio file
into an array of type <code>List[float]</code> or a <code>numpy.ndarray</code>, <em>e.g.</em> via the soundfile library (<code>pip install soundfile</code>). To prepare the array into <code>input_values</code>, the <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a> should be used for padding and
conversion into a tensor of type <code>torch.FloatTensor</code>. See <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.HubertModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing convolution and 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></p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
						
<p><code>attention_mask</code> should only be passed if the corresponding processor has <code>config.return_attention_mask == True</code>. For all models whose processor has <code>config.return_attention_mask == False</code>, such as
<a href="https://huggingface.co/facebook/hubert-base-ls960" rel="nofollow">hubert-base</a>, <code>attention_mask</code> should <strong>not</strong> be passed
to avoid degraded performance when doing batched inference. For such models <code>input_values</code> should simply be
padded with 0 and passed without <code>attention_mask</code>. Be aware that these models also yield slightly different
results depending on whether <code>input_values</code> is padded or not.</p>

					</div>`,name:"attention_mask"},{anchor:"transformers.HubertModel.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.HubertModel.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.HubertModel.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/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/hubert/modeling_hubert.py#L1020",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig"
>HubertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),$t=new Po({props:{$$slots:{default:[Yi]},$$scope:{ctx:M}}}),Ct=new es({props:{anchor:"transformers.HubertModel.forward.example",$$slots:{default:[Gi]},$$scope:{ctx:M}}}),to=new wt({}),oo=new ot({props:{name:"class transformers.HubertForCTC",anchor:"transformers.HubertForCTC",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.HubertForCTC.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</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/v4.27.1/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/v4.27.1/src/transformers/models/hubert/modeling_hubert.py#L1099"}}),io=new ot({props:{name:"forward",anchor:"transformers.HubertForCTC.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"transformers.HubertForCTC.forward.input_values",description:`<strong>input_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform. Values can be obtained by loading a <code>.flac</code> or <code>.wav</code> audio file
into an array of type <code>List[float]</code> or a <code>numpy.ndarray</code>, <em>e.g.</em> via the soundfile library (<code>pip install soundfile</code>). To prepare the array into <code>input_values</code>, the <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a> should be used for padding and
conversion into a tensor of type <code>torch.FloatTensor</code>. See <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.HubertForCTC.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing convolution and 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></p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
						
<p><code>attention_mask</code> should only be passed if the corresponding processor has <code>config.return_attention_mask == True</code>. For all models whose processor has <code>config.return_attention_mask == False</code>, such as
<a href="https://huggingface.co/facebook/hubert-base-ls960" rel="nofollow">hubert-base</a>, <code>attention_mask</code> should <strong>not</strong> be passed
to avoid degraded performance when doing batched inference. For such models <code>input_values</code> should simply be
padded with 0 and passed without <code>attention_mask</code>. Be aware that these models also yield slightly different
results depending on whether <code>input_values</code> is padded or not.</p>

					</div>`,name:"attention_mask"},{anchor:"transformers.HubertForCTC.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.HubertForCTC.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.HubertForCTC.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/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.HubertForCTC.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_length)</code>, <em>optional</em>) &#x2014;
Labels for connectionist temporal classification. Note that <code>target_length</code> has to be smaller or equal to
the sequence length of the output logits. Indices are selected in <code>[-100, 0, ..., config.vocab_size - 1]</code>.
All labels set to <code>-100</code> are ignored (masked), the loss is only computed for labels in <code>[0, ..., config.vocab_size - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/hubert/modeling_hubert.py#L1140",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.CausalLMOutput"
>transformers.modeling_outputs.CausalLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig"
>HubertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.CausalLMOutput"
>transformers.modeling_outputs.CausalLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Et=new Po({props:{$$slots:{default:[Xi]},$$scope:{ctx:M}}}),jt=new es({props:{anchor:"transformers.HubertForCTC.forward.example",$$slots:{default:[Ji]},$$scope:{ctx:M}}}),lo=new wt({}),co=new ot({props:{name:"class transformers.HubertForSequenceClassification",anchor:"transformers.HubertForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.HubertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</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/v4.27.1/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/v4.27.1/src/transformers/models/hubert/modeling_hubert.py#L1228"}}),go=new ot({props:{name:"forward",anchor:"transformers.HubertForSequenceClassification.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"transformers.HubertForSequenceClassification.forward.input_values",description:`<strong>input_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform. Values can be obtained by loading a <code>.flac</code> or <code>.wav</code> audio file
into an array of type <code>List[float]</code> or a <code>numpy.ndarray</code>, <em>e.g.</em> via the soundfile library (<code>pip install soundfile</code>). To prepare the array into <code>input_values</code>, the <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a> should be used for padding and
conversion into a tensor of type <code>torch.FloatTensor</code>. See <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.HubertForSequenceClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing convolution and 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></p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
						
<p><code>attention_mask</code> should only be passed if the corresponding processor has <code>config.return_attention_mask == True</code>. For all models whose processor has <code>config.return_attention_mask == False</code>, such as
<a href="https://huggingface.co/facebook/hubert-base-ls960" rel="nofollow">hubert-base</a>, <code>attention_mask</code> should <strong>not</strong> be passed
to avoid degraded performance when doing batched inference. For such models <code>input_values</code> should simply be
padded with 0 and passed without <code>attention_mask</code>. Be aware that these models also yield slightly different
results depending on whether <code>input_values</code> is padded or not.</p>

					</div>`,name:"attention_mask"},{anchor:"transformers.HubertForSequenceClassification.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.HubertForSequenceClassification.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.HubertForSequenceClassification.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/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.HubertForSequenceClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/hubert/modeling_hubert.py#L1273",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig"
>HubertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),xt=new Po({props:{$$slots:{default:[Qi]},$$scope:{ctx:M}}}),Mt=new es({props:{anchor:"transformers.HubertForSequenceClassification.forward.example",$$slots:{default:[Zi]},$$scope:{ctx:M}}}),_o=new wt({}),bo=new ot({props:{name:"class transformers.TFHubertModel",anchor:"transformers.TFHubertModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFHubertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</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/v4.27.1/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/v4.27.1/src/transformers/models/hubert/modeling_tf_hubert.py#L1290"}}),Pt=new Po({props:{$$slots:{default:[el]},$$scope:{ctx:M}}}),To=new ot({props:{name:"call",anchor:"transformers.TFHubertModel.call",parameters:[{name:"input_values",val:": Tensor"},{name:"attention_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"position_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"head_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFHubertModel.call.input_values",description:`<strong>input_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> <code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_values"},{anchor:"transformers.TFHubertModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>({0})</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.TFHubertModel.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFHubertModel.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>({0})</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.TFHubertModel.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. 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.TFHubertModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_values</code> you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert <code>input_values</code> indices into associated vectors
than the model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFHubertModel.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFHubertModel.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFHubertModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFHubertModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/hubert/modeling_tf_hubert.py#L1296",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig"
>HubertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),At=new Po({props:{$$slots:{default:[tl]},$$scope:{ctx:M}}}),St=new es({props:{anchor:"transformers.TFHubertModel.call.example",$$slots:{default:[ol]},$$scope:{ctx:M}}}),ko=new wt({}),$o=new ot({props:{name:"class transformers.TFHubertForCTC",anchor:"transformers.TFHubertForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFHubertForCTC.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</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/v4.27.1/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/v4.27.1/src/transformers/models/hubert/modeling_tf_hubert.py#L1371"}}),Dt=new Po({props:{$$slots:{default:[sl]},$$scope:{ctx:M}}}),Fo=new ot({props:{name:"call",anchor:"transformers.TFHubertForCTC.call",parameters:[{name:"input_values",val:": Tensor"},{name:"attention_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"position_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"head_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFHubertForCTC.call.input_values",description:`<strong>input_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> <code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_values"},{anchor:"transformers.TFHubertForCTC.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>({0})</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.TFHubertForCTC.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFHubertForCTC.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>({0})</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.TFHubertForCTC.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. 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.TFHubertForCTC.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_values</code> you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert <code>input_values</code> indices into associated vectors
than the model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFHubertForCTC.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFHubertForCTC.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFHubertForCTC.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFHubertForCTC.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFHubertForCTC.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_values</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked),
the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/hubert/modeling_tf_hubert.py#L1398",returnDescription:`
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</li>
<li>
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<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
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<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
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</li>
</ul>
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