import{S as ks,i as Es,s as Ps,e as r,k as m,w as v,t as s,M as zs,c as n,d as o,m as p,a,x as b,h as i,b as c,G as e,g as u,y as w,q as M,o as $,B as y,v as Fs,L as va}from"../../chunks/vendor-hf-doc-builder.js";import{T as tr}from"../../chunks/Tip-hf-doc-builder.js";import{D as S}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ba}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Fe}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ca}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function js(z){let d,C,f,g,T;return g=new ba({props:{code:`from transformers import MCTCTConfig, MCTCTModel

# Initializing a M-CTC-T mctct-large style configuration
configuration = MCTCTConfig()

# Initializing a model (with random weights) from the mctct-large style configuration
model = MCTCTModel(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> MCTCTConfig, MCTCTModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a M-CTC-T mctct-large style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = MCTCTConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the mctct-large style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MCTCTModel(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=r("p"),C=s("Example:"),f=m(),v(g.$$.fragment)},l(l){d=n(l,"P",{});var _=a(d);C=i(_,"Example:"),_.forEach(o),f=p(l),b(g.$$.fragment,l)},m(l,_){u(l,d,_),e(d,C),u(l,f,_),w(g,l,_),T=!0},p:va,i(l){T||(M(g.$$.fragment,l),T=!0)},o(l){$(g.$$.fragment,l),T=!1},d(l){l&&o(d),l&&o(f),y(g,l)}}}function qs(z){let d,C,f,g,T,l,_,k,A,E,F;return{c(){d=r("p"),C=s(`This class method is simply calling the feature extractor
`),f=r("a"),g=s("from_pretrained()"),T=s(`, image processor
`),l=r("a"),_=s("ImageProcessingMixin"),k=s(` and the tokenizer
`),A=r("code"),E=s("~tokenization_utils_base.PreTrainedTokenizer.from_pretrained"),F=s(` methods. Please refer to the docstrings of the
methods above for more information.`),this.h()},l(P){d=n(P,"P",{});var j=a(d);C=i(j,`This class method is simply calling the feature extractor
`),f=n(j,"A",{href:!0});var R=a(f);g=i(R,"from_pretrained()"),R.forEach(o),T=i(j,`, image processor
`),l=n(j,"A",{href:!0});var pt=a(l);_=i(pt,"ImageProcessingMixin"),pt.forEach(o),k=i(j,` and the tokenizer
`),A=n(j,"CODE",{});var ie=a(A);E=i(ie,"~tokenization_utils_base.PreTrainedTokenizer.from_pretrained"),ie.forEach(o),F=i(j,` methods. Please refer to the docstrings of the
methods above for more information.`),j.forEach(o),this.h()},h(){c(f,"href","/docs/transformers/v4.27.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained"),c(l,"href","/docs/transformers/v4.27.1/en/internal/image_processing_utils#transformers.ImageProcessingMixin")},m(P,j){u(P,d,j),e(d,C),e(d,f),e(f,g),e(d,T),e(d,l),e(l,_),e(d,k),e(d,A),e(A,E),e(d,F)},d(P){P&&o(d)}}}function As(z){let d,C,f,g,T,l,_,k;return{c(){d=r("p"),C=s("This class method is simply calling "),f=r("a"),g=s("save_pretrained()"),T=s(` and
`),l=r("a"),_=s("save_pretrained()"),k=s(`. Please refer to the docstrings of the
methods above for more information.`),this.h()},l(A){d=n(A,"P",{});var E=a(d);C=i(E,"This class method is simply calling "),f=n(E,"A",{href:!0});var F=a(f);g=i(F,"save_pretrained()"),F.forEach(o),T=i(E,` and
`),l=n(E,"A",{href:!0});var P=a(l);_=i(P,"save_pretrained()"),P.forEach(o),k=i(E,`. Please refer to the docstrings of the
methods above for more information.`),E.forEach(o),this.h()},h(){c(f,"href","/docs/transformers/v4.27.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained"),c(l,"href","/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained")},m(A,E){u(A,d,E),e(d,C),e(d,f),e(f,g),e(d,T),e(d,l),e(l,_),e(d,k)},d(A){A&&o(d)}}}function Is(z){let d,C,f,g,T;return{c(){d=r("p"),C=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=r("code"),g=s("Module"),T=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=n(l,"P",{});var _=a(d);C=i(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=n(_,"CODE",{});var k=a(f);g=i(k,"Module"),k.forEach(o),T=i(_,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),_.forEach(o)},m(l,_){u(l,d,_),e(d,C),e(d,f),e(f,g),e(d,T)},d(l){l&&o(d)}}}function Ds(z){let d,C,f,g,T;return g=new ba({props:{code:`from transformers import AutoProcessor, MCTCTModel
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("speechbrain/m-ctc-t-large")
model = MCTCTModel.from_pretrained("speechbrain/m-ctc-t-large")

# 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`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, MCTCTModel
<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;speechbrain/m-ctc-t-large&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MCTCTModel.from_pretrained(<span class="hljs-string">&quot;speechbrain/m-ctc-t-large&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-number">1</span>, <span class="hljs-number">195</span>, <span class="hljs-number">1536</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`}}),{c(){d=r("p"),C=s("Example:"),f=m(),v(g.$$.fragment)},l(l){d=n(l,"P",{});var _=a(d);C=i(_,"Example:"),_.forEach(o),f=p(l),b(g.$$.fragment,l)},m(l,_){u(l,d,_),e(d,C),u(l,f,_),w(g,l,_),T=!0},p:va,i(l){T||(M(g.$$.fragment,l),T=!0)},o(l){$(g.$$.fragment,l),T=!1},d(l){l&&o(d),l&&o(f),y(g,l)}}}function Ls(z){let d,C,f,g,T;return{c(){d=r("p"),C=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=r("code"),g=s("Module"),T=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=n(l,"P",{});var _=a(d);C=i(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=n(_,"CODE",{});var k=a(f);g=i(k,"Module"),k.forEach(o),T=i(_,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),_.forEach(o)},m(l,_){u(l,d,_),e(d,C),e(d,f),e(f,g),e(d,T)},d(l){l&&o(d)}}}function Ss(z){let d,C,f,g,T;return g=new ba({props:{code:`from transformers import AutoProcessor, MCTCTForCTC
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("speechbrain/m-ctc-t-large")
model = MCTCTForCTC.from_pretrained("speechbrain/m-ctc-t-large")

# 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, MCTCTForCTC
<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;speechbrain/m-ctc-t-large&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MCTCTForCTC.from_pretrained(<span class="hljs-string">&quot;speechbrain/m-ctc-t-large&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">&quot;Mr. Quilter is the apostle of the middle classes, and we&#x27;re glad to welcome his gospel.&quot;</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">1885.65</span>`}}),{c(){d=r("p"),C=s("Example:"),f=m(),v(g.$$.fragment)},l(l){d=n(l,"P",{});var _=a(d);C=i(_,"Example:"),_.forEach(o),f=p(l),b(g.$$.fragment,l)},m(l,_){u(l,d,_),e(d,C),u(l,f,_),w(g,l,_),T=!0},p:va,i(l){T||(M(g.$$.fragment,l),T=!0)},o(l){$(g.$$.fragment,l),T=!1},d(l){l&&o(d),l&&o(f),y(g,l)}}}function Ns(z){let d,C,f,g,T,l,_,k,A,E,F,P,j,R,pt,ie,or,bo,le,rr,je,nr,ar,wo,ht,sr,Mo,ft,Bt,ir,$o,H,lr,qe,cr,dr,Ae,mr,pr,yo,K,ce,Ut,Ie,hr,Rt,fr,xo,ut,Ht,gt,ur,ko,_t,gr,Eo,Tt,Gt,_r,Po,Q,de,Jt,De,Tr,Kt,Cr,zo,D,Le,vr,Y,br,Ct,wr,Mr,Se,$r,yr,xr,X,kr,vt,Er,Pr,bt,zr,Fr,jr,me,Fo,Z,pe,Qt,Ne,qr,Yt,Ar,jo,L,Oe,Ir,Xt,Dr,Lr,ee,Sr,wt,Nr,Or,Ve,Vr,Wr,Br,he,We,Ur,Zt,Rr,qo,te,fe,eo,Be,Hr,to,Gr,Ao,x,Ue,Jr,oo,Kr,Qr,I,Mt,Yr,Xr,$t,Zr,en,yt,tn,on,Re,ro,rn,nn,an,xt,sn,ln,cn,ue,He,dn,V,mn,Ge,no,pn,hn,fn,ao,un,gn,so,_n,Tn,Cn,G,Je,vn,io,bn,wn,ge,Mn,J,Ke,$n,Qe,yn,kt,xn,kn,En,_e,Pn,Te,Ye,zn,Xe,Fn,Et,jn,qn,An,Ce,Ze,In,et,Dn,Pt,Ln,Sn,Io,oe,ve,lo,tt,Nn,co,On,Do,W,ot,Vn,rt,Wn,nt,Bn,Un,Rn,N,at,Hn,re,Gn,zt,Jn,Kn,mo,Qn,Yn,Xn,be,Zn,we,Lo,ne,Me,po,st,ea,ho,ta,So,B,it,oa,ae,ra,fo,na,aa,lt,sa,ia,la,O,ct,ca,se,da,Ft,ma,pa,uo,ha,fa,ua,$e,ga,ye,No;return l=new Fe({}),R=new Fe({}),Ie=new Fe({}),De=new Fe({}),Le=new S({props:{name:"class transformers.MCTCTConfig",anchor:"transformers.MCTCTConfig",parameters:[{name:"vocab_size",val:" = 8065"},{name:"hidden_size",val:" = 1536"},{name:"num_hidden_layers",val:" = 36"},{name:"intermediate_size",val:" = 6144"},{name:"num_attention_heads",val:" = 4"},{name:"attention_head_dim",val:" = 384"},{name:"max_position_embeddings",val:" = 920"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"layerdrop",val:" = 0.3"},{name:"hidden_act",val:" = 'relu'"},{name:"initializer_range",val:" = 0.02"},{name:"hidden_dropout_prob",val:" = 0.3"},{name:"attention_probs_dropout_prob",val:" = 0.3"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 0"},{name:"eos_token_id",val:" = 2"},{name:"conv_glu_dim",val:" = 1"},{name:"conv_dropout",val:" = 0.3"},{name:"num_conv_layers",val:" = 1"},{name:"conv_kernel",val:" = (7,)"},{name:"conv_stride",val:" = (3,)"},{name:"input_feat_per_channel",val:" = 80"},{name:"input_channels",val:" = 1"},{name:"conv_channels",val:" = None"},{name:"ctc_loss_reduction",val:" = 'sum'"},{name:"ctc_zero_infinity",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MCTCTConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8065) &#x2014;
Vocabulary size of the M-CTC-T 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/mctct#transformers.MCTCTModel">MCTCTModel</a>.`,name:"vocab_size"},{anchor:"transformers.MCTCTConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1536) &#x2014;
Dimension of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.MCTCTConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 36) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.MCTCTConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 6144) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.MCTCTConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.MCTCTConfig.attention_head_dim",description:`<strong>attention_head_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 384) &#x2014;
Dimensions of each attention head for each attention layer in the Transformer encoder.`,name:"attention_head_dim"},{anchor:"transformers.MCTCTConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 920) &#x2014;
The maximum sequence length that this model might ever be used with (after log-mel spectrogram extraction).`,name:"max_position_embeddings"},{anchor:"transformers.MCTCTConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.MCTCTConfig.layerdrop",description:`<strong>layerdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.3) &#x2014;
The probability of dropping an encoder layer during training. The default 0.3 value is used in the original
implementation.`,name:"layerdrop"},{anchor:"transformers.MCTCTConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;relu&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.MCTCTConfig.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.MCTCTConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.MCTCTConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.MCTCTConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The tokenizer index of the pad token.`,name:"pad_token_id"},{anchor:"transformers.MCTCTConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The tokenizer index of the bos token.`,name:"bos_token_id"},{anchor:"transformers.MCTCTConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The tokenizer index of the eos token.`,name:"eos_token_id"},{anchor:"transformers.MCTCTConfig.conv_glu_dim",description:`<strong>conv_glu_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The dimension of the output of the <code>Conv1dSubsampler</code> layer in which GLU is applied on. Though the original
Flashlight code uses the value of 2, here it&#x2019;s adapted to 1 due to transposition differences.`,name:"conv_glu_dim"},{anchor:"transformers.MCTCTConfig.conv_dropout",description:`<strong>conv_dropout</strong> (<code>int</code>, <em>optional</em>, defaults to 0.3) &#x2014;
The probability of randomly dropping the <code>Conv1dSubsampler</code> layer during training.`,name:"conv_dropout"},{anchor:"transformers.MCTCTConfig.num_conv_layers",description:`<strong>num_conv_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of convolution layers before applying transformer encoder layers.`,name:"num_conv_layers"},{anchor:"transformers.MCTCTConfig.conv_kernel",description:`<strong>conv_kernel</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[7]</code>) &#x2014;
The kernel size of the 1D convolution applied before transformer layers. <code>len(conv_kernel)</code> must be equal
to <code>num_conv_layers</code>.`,name:"conv_kernel"},{anchor:"transformers.MCTCTConfig.conv_stride",description:`<strong>conv_stride</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[3]</code>) &#x2014;
The stride length of the 1D convolution applied before transformer layers. <code>len(conv_stride)</code> must be equal
to <code>num_conv_layers</code>.`,name:"conv_stride"},{anchor:"transformers.MCTCTConfig.input_feat_per_channel",description:`<strong>input_feat_per_channel</strong> (<code>int</code>, <em>optional</em>, defaults to 80) &#x2014;
Feature dimensions of the channels of the input to the Conv1D layer.`,name:"input_feat_per_channel"},{anchor:"transformers.MCTCTConfig.input_channels",description:`<strong>input_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of input channels of the input to the Conv1D layer.`,name:"input_channels"},{anchor:"transformers.MCTCTConfig.conv_channels",description:`<strong>conv_channels</strong> (<code>List[int]</code>, <em>optional</em>, defaults to None) &#x2014;
Channel sizes of intermediate Conv1D layers.`,name:"conv_channels"},{anchor:"transformers.MCTCTConfig.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/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a>.`,name:"ctc_loss_reduction"},{anchor:"transformers.MCTCTConfig.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/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a>.`,name:"ctc_zero_infinity"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mctct/configuration_mctct.py#L29"}}),me=new Ca({props:{anchor:"transformers.MCTCTConfig.example",$$slots:{default:[js]},$$scope:{ctx:z}}}),Ne=new Fe({}),Oe=new S({props:{name:"class transformers.MCTCTFeatureExtractor",anchor:"transformers.MCTCTFeatureExtractor",parameters:[{name:"feature_size",val:" = 80"},{name:"sampling_rate",val:" = 16000"},{name:"padding_value",val:" = 0.0"},{name:"hop_length",val:" = 10"},{name:"win_length",val:" = 25"},{name:"win_function",val:" = 'hamming_window'"},{name:"frame_signal_scale",val:" = 32768.0"},{name:"preemphasis_coeff",val:" = 0.97"},{name:"mel_floor",val:" = 1.0"},{name:"normalize_means",val:" = True"},{name:"normalize_vars",val:" = True"},{name:"return_attention_mask",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MCTCTFeatureExtractor.feature_size",description:`<strong>feature_size</strong> (<code>int</code>, defaults to 80) &#x2014;
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The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).`,name:"sampling_rate"},{anchor:"transformers.MCTCTFeatureExtractor.padding_value",description:`<strong>padding_value</strong> (<code>float</code>, defaults to 0.0) &#x2014;
The value that is used to fill the padding values.`,name:"padding_value"},{anchor:"transformers.MCTCTFeatureExtractor.hop_length",description:`<strong>hop_length</strong> (<code>int</code>, defaults to 10) &#x2014;
Number of audio samples between windows. Otherwise referred to as &#x201C;shift&#x201D; in many papers.`,name:"hop_length"},{anchor:"transformers.MCTCTFeatureExtractor.win_length",description:`<strong>win_length</strong> (<code>int</code>, defaults to 25) &#x2014;
Number of ms per window`,name:"win_length"},{anchor:"transformers.MCTCTFeatureExtractor.win_function",description:`<strong>win_function</strong> (<code>str</code>, defaults to <code>&quot;hamming_window&quot;</code>) &#x2014;
Name for the window function used for windowing, must be accessible via <code>torch.{win_function}</code>`,name:"win_function"},{anchor:"transformers.MCTCTFeatureExtractor.frame_signal_scale",description:`<strong>frame_signal_scale</strong> (<code>float</code>, defaults to 32768.0) &#x2014;
Constant multiplied in creating the frames before applying DFT.`,name:"frame_signal_scale"},{anchor:"transformers.MCTCTFeatureExtractor.preemphasis_coeff",description:`<strong>preemphasis_coeff</strong> (<code>float</code>, defaults to 0.97) &#x2014;
Constant multiplied in applying Pre-emphasis before DFT.`,name:"preemphasis_coeff"},{anchor:"transformers.MCTCTFeatureExtractor.mel_floor",description:`<strong>mel_floor</strong> (<code>float</code> defaults to 1.0) &#x2014;
Minimum value of mel frequency banks.`,name:"mel_floor"},{anchor:"transformers.MCTCTFeatureExtractor.normalize_means",description:`<strong>normalize_means</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to zero-mean normalize the extracted features.`,name:"normalize_means"},{anchor:"transformers.MCTCTFeatureExtractor.normalize_vars",description:`<strong>normalize_vars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to unit-variance normalize the extracted features.`,name:"normalize_vars"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mctct/feature_extraction_mctct.py#L42"}}),We=new S({props:{name:"__call__",anchor:"transformers.MCTCTFeatureExtractor.__call__",parameters:[{name:"raw_speech",val:": typing.Union[numpy.ndarray, typing.List[float], typing.List[numpy.ndarray], typing.List[typing.List[float]]]"},{name:"padding",val:": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"truncation",val:": bool = False"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_attention_mask",val:": typing.Optional[bool] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"sampling_rate",val:": typing.Optional[int] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MCTCTFeatureExtractor.__call__.raw_speech",description:`<strong>raw_speech</strong> (<code>torch.Tensor</code>, <code>np.ndarray</code>, <code>List[float]</code>, <code>List[torch.Tensor]</code>, <code>List[np.ndarray]</code>, <code>List[List[float]]</code>) &#x2014;
The sequence or batch of sequences to be padded. Each sequence can be a tensor, a numpy array, a list
of float values, a list of tensors, a list of numpy arrays or a list of list of float values.`,name:"raw_speech"},{anchor:"transformers.MCTCTFeatureExtractor.__call__.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.27.1/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Select a strategy to pad the returned sequences (according to the model&#x2019;s padding side and padding
index) among:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.MCTCTFeatureExtractor.__call__.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Maximum length of the returned list and optionally padding length (see above).`,name:"max_length"},{anchor:"transformers.MCTCTFeatureExtractor.__call__.truncation",description:`<strong>truncation</strong> (<code>bool</code>) &#x2014;
Activates truncation to cut input sequences longer than <em>max_length</em> to <em>max_length</em>.`,name:"truncation"},{anchor:"transformers.MCTCTFeatureExtractor.__call__.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) &#x2014;
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
<code>&gt;= 7.5</code> (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.`,name:"pad_to_multiple_of"},{anchor:"transformers.MCTCTFeatureExtractor.__call__.return_attention_mask",description:`<strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific feature_extractor&#x2019;s default.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"},{anchor:"transformers.MCTCTFeatureExtractor.__call__.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/v4.27.1/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>&apos;tf&apos;</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<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"},{anchor:"transformers.MCTCTFeatureExtractor.__call__.sampling_rate",description:`<strong>sampling_rate</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The sampling rate at which the <code>raw_speech</code> input was sampled. It is strongly recommended to pass
<code>sampling_rate</code> at the forward call to prevent silent errors.`,name:"sampling_rate"},{anchor:"transformers.MCTCTFeatureExtractor.__call__.padding_value",description:"<strong>padding_value</strong> (<code>float</code>, defaults to 0.0) &#x2014;",name:"padding_value"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mctct/feature_extraction_mctct.py#L240"}}),Be=new Fe({}),Ue=new S({props:{name:"class transformers.MCTCTProcessor",anchor:"transformers.MCTCTProcessor",parameters:[{name:"feature_extractor",val:""},{name:"tokenizer",val:""}],parametersDescription:[{anchor:"transformers.MCTCTProcessor.feature_extractor",description:`<strong>feature_extractor</strong> (<code>MCTCTFeatureExtractor</code>) &#x2014;
An instance of <a href="/docs/transformers/v4.27.1/en/model_doc/mctct#transformers.MCTCTFeatureExtractor">MCTCTFeatureExtractor</a>. The feature extractor is a required input.`,name:"feature_extractor"},{anchor:"transformers.MCTCTProcessor.tokenizer",description:`<strong>tokenizer</strong> (<code>AutoTokenizer</code>) &#x2014;
An instance of <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. The tokenizer is a required input.`,name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mctct/processing_mctct.py#L24"}}),He=new S({props:{name:"__call__",anchor:"transformers.MCTCTProcessor.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mctct/processing_mctct.py#L45"}}),Je=new S({props:{name:"from_pretrained",anchor:"transformers.MCTCTProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MCTCTProcessor.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 feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/v4.27.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.
**kwargs &#x2014;
Additional keyword arguments passed along to both
<a href="/docs/transformers/v4.27.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a> and
<code>~tokenization_utils_base.PreTrainedTokenizer.from_pretrained</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/processing_utils.py#L153"}}),ge=new tr({props:{$$slots:{default:[qs]},$$scope:{ctx:z}}}),Ke=new S({props:{name:"save_pretrained",anchor:"transformers.MCTCTProcessor.save_pretrained",parameters:[{name:"save_directory",val:""},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MCTCTProcessor.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist).`,name:"save_directory"},{anchor:"transformers.MCTCTProcessor.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/v4.27.1/en/internal/image_processing_utils#transformers.ImageProcessingMixin.push_to_hub">push_to_hub()</a> method.`,name:"push_to_hub"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/processing_utils.py#L91"}}),_e=new tr({props:{$$slots:{default:[As]},$$scope:{ctx:z}}}),Ye=new S({props:{name:"batch_decode",anchor:"transformers.MCTCTProcessor.batch_decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mctct/processing_mctct.py#L83"}}),Ze=new S({props:{name:"decode",anchor:"transformers.MCTCTProcessor.decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mctct/processing_mctct.py#L120"}}),tt=new Fe({}),ot=new S({props:{name:"class transformers.MCTCTModel",anchor:"transformers.MCTCTModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MCTCTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</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/mctct/modeling_mctct.py#L668"}}),at=new S({props:{name:"forward",anchor:"transformers.MCTCTModel.forward",parameters:[{name:"input_features",val:": Tensor"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_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"}],parametersDescription:[{anchor:"transformers.MCTCTModel.forward.input_features",description:`<strong>input_features</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</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/wav2vec2#transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_features"},{anchor:"transformers.MCTCTModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</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.MCTCTModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</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.MCTCTModel.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.MCTCTModel.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.MCTCTModel.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/mctct/modeling_mctct.py#L678",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/mctct#transformers.MCTCTConfig"
>MCTCTConfig</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>
`}}),be=new tr({props:{$$slots:{default:[Is]},$$scope:{ctx:z}}}),we=new Ca({props:{anchor:"transformers.MCTCTModel.forward.example",$$slots:{default:[Ds]},$$scope:{ctx:z}}}),st=new Fe({}),it=new S({props:{name:"class transformers.MCTCTForCTC",anchor:"transformers.MCTCTForCTC",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MCTCTForCTC.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</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/mctct/modeling_mctct.py#L728"}}),ct=new S({props:{name:"forward",anchor:"transformers.MCTCTForCTC.forward",parameters:[{name:"input_features",val:": Tensor"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_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.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.MCTCTForCTC.forward.input_features",description:`<strong>input_features</strong> (<code>torch.LongTensor</code> of 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/wav2vec2#transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_features"},{anchor:"transformers.MCTCTForCTC.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</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.MCTCTForCTC.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</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>
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more detail.`,name:"output_hidden_states"},{anchor:"transformers.MCTCTForCTC.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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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>.
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<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/mctct#transformers.MCTCTConfig"
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<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
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