import{s as Gn,o as zn,n as N}from"../chunks/scheduler.25b97de1.js";import{S as Rn,i as In,g as d,s as r,r as u,A as Nn,h as m,f as l,c as s,j as V,u as h,x as f,k,y as a,a as p,v as g,d as v,t as _,w as b}from"../chunks/index.d9030fc9.js";import{T as Vo}from"../chunks/Tip.baa67368.js";import{D as X}from"../chunks/Docstring.e257edda.js";import{C as Ae}from"../chunks/CodeBlock.e6cd0d95.js";import{E as Ee}from"../chunks/ExampleCodeBlock.20db4b6e.js";import{H as Q,E as Bn}from"../chunks/EditOnGithub.91d95064.js";function qn(T){let o,y="Example:",i,c,w;return c=new Ae({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2ConformerConfig, Wav2Vec2ConformerModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Wav2Vec2Conformer facebook/wav2vec2-conformer-rel-pos-large style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = Wav2Vec2ConformerConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the facebook/wav2vec2-conformer-rel-pos-large style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Wav2Vec2ConformerModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){o=d("p"),o.textContent=y,i=r(),u(c.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),f(o)!=="svelte-11lpom8"&&(o.textContent=y),i=s(t),h(c.$$.fragment,t)},m(t,M){p(t,o,M),p(t,i,M),g(c,t,M),w=!0},p:N,i(t){w||(v(c.$$.fragment,t),w=!0)},o(t){_(c.$$.fragment,t),w=!1},d(t){t&&(l(o),l(i)),b(c,t)}}}function Yn(T){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=y},l(i){o=m(i,"P",{"data-svelte-h":!0}),f(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(i,c){p(i,o,c)},p:N,d(i){i&&l(o)}}}function Hn(T){let o,y="Example:",i,c,w;return c=new Ae({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, Wav2Vec2ConformerModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<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>, trust_remote_code=<span class="hljs-literal">True</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/wav2vec2-conformer-rope-large-960h-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Wav2Vec2ConformerModel.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-conformer-rope-large-960h-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>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">292</span>, <span class="hljs-number">1024</span>]`,wrap:!1}}),{c(){o=d("p"),o.textContent=y,i=r(),u(c.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),f(o)!=="svelte-11lpom8"&&(o.textContent=y),i=s(t),h(c.$$.fragment,t)},m(t,M){p(t,o,M),p(t,i,M),g(c,t,M),w=!0},p:N,i(t){w||(v(c.$$.fragment,t),w=!0)},o(t){_(c.$$.fragment,t),w=!1},d(t){t&&(l(o),l(i)),b(c,t)}}}function Ln(T){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=y},l(i){o=m(i,"P",{"data-svelte-h":!0}),f(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(i,c){p(i,o,c)},p:N,d(i){i&&l(o)}}}function Sn(T){let o,y="Example:",i,c,w;return c=new Ae({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, Wav2Vec2ConformerForCTC
<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>, trust_remote_code=<span class="hljs-literal">True</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/wav2vec2-conformer-rope-large-960h-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Wav2Vec2ConformerForCTC.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-conformer-rope-large-960h-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">64.21</span>`,wrap:!1}}),{c(){o=d("p"),o.textContent=y,i=r(),u(c.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),f(o)!=="svelte-11lpom8"&&(o.textContent=y),i=s(t),h(c.$$.fragment,t)},m(t,M){p(t,o,M),p(t,i,M),g(c,t,M),w=!0},p:N,i(t){w||(v(c.$$.fragment,t),w=!0)},o(t){_(c.$$.fragment,t),w=!1},d(t){t&&(l(o),l(i)),b(c,t)}}}function Qn(T){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=y},l(i){o=m(i,"P",{"data-svelte-h":!0}),f(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(i,c){p(i,o,c)},p:N,d(i){i&&l(o)}}}function En(T){let o,y="Example:",i,c,w;return c=new Ae({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, Wav2Vec2ConformerForSequenceClassification
<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>, trust_remote_code=<span class="hljs-literal">True</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;facebook/wav2vec2-conformer-rope-large-960h-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Wav2Vec2ConformerForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-conformer-rope-large-960h-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 = 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><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`,wrap:!1}}),{c(){o=d("p"),o.textContent=y,i=r(),u(c.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),f(o)!=="svelte-11lpom8"&&(o.textContent=y),i=s(t),h(c.$$.fragment,t)},m(t,M){p(t,o,M),p(t,i,M),g(c,t,M),w=!0},p:N,i(t){w||(v(c.$$.fragment,t),w=!0)},o(t){_(c.$$.fragment,t),w=!1},d(t){t&&(l(o),l(i)),b(c,t)}}}function An(T){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=y},l(i){o=m(i,"P",{"data-svelte-h":!0}),f(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(i,c){p(i,o,c)},p:N,d(i){i&&l(o)}}}function Pn(T){let o,y="Example:",i,c,w;return c=new Ae({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, Wav2Vec2ConformerForAudioFrameClassification
<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>, trust_remote_code=<span class="hljs-literal">True</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;facebook/wav2vec2-conformer-rope-large-960h-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Wav2Vec2ConformerForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-conformer-rope-large-960h-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 = 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>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, sampling_rate=sampling_rate)
<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>probabilities = torch.sigmoid(logits[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># labels is a one-hot array of shape (num_frames, num_speakers)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = (probabilities &gt; <span class="hljs-number">0.5</span>).long()`,wrap:!1}}),{c(){o=d("p"),o.textContent=y,i=r(),u(c.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),f(o)!=="svelte-11lpom8"&&(o.textContent=y),i=s(t),h(c.$$.fragment,t)},m(t,M){p(t,o,M),p(t,i,M),g(c,t,M),w=!0},p:N,i(t){w||(v(c.$$.fragment,t),w=!0)},o(t){_(c.$$.fragment,t),w=!1},d(t){t&&(l(o),l(i)),b(c,t)}}}function Dn(T){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=y},l(i){o=m(i,"P",{"data-svelte-h":!0}),f(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(i,c){p(i,o,c)},p:N,d(i){i&&l(o)}}}function On(T){let o,y="Example:",i,c,w;return c=new Ae({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, Wav2Vec2ConformerForXVector
<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>, trust_remote_code=<span class="hljs-literal">True</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;facebook/wav2vec2-conformer-rope-large-960h-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Wav2Vec2ConformerForXVector.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-conformer-rope-large-960h-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 = feature_extractor(
<span class="hljs-meta">... </span>    [d[<span class="hljs-string">&quot;array&quot;</span>] <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> dataset[:<span class="hljs-number">2</span>][<span class="hljs-string">&quot;audio&quot;</span>]], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    embeddings = model(**inputs).embeddings

<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = torch.nn.functional.normalize(embeddings, dim=-<span class="hljs-number">1</span>).cpu()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the resulting embeddings can be used for cosine similarity-based retrieval</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>cosine_sim = torch.nn.CosineSimilarity(dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>similarity = cosine_sim(embeddings[<span class="hljs-number">0</span>], embeddings[<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>threshold = <span class="hljs-number">0.7</span>  <span class="hljs-comment"># the optimal threshold is dataset-dependent</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">if</span> similarity &lt; threshold:
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Speakers are not the same!&quot;</span>)`,wrap:!1}}),{c(){o=d("p"),o.textContent=y,i=r(),u(c.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),f(o)!=="svelte-11lpom8"&&(o.textContent=y),i=s(t),h(c.$$.fragment,t)},m(t,M){p(t,o,M),p(t,i,M),g(c,t,M),w=!0},p:N,i(t){w||(v(c.$$.fragment,t),w=!0)},o(t){_(c.$$.fragment,t),w=!1},d(t){t&&(l(o),l(i)),b(c,t)}}}function Kn(T){let o,y=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=y},l(i){o=m(i,"P",{"data-svelte-h":!0}),f(o)!=="svelte-fincs2"&&(o.innerHTML=y)},m(i,c){p(i,o,c)},p:N,d(i){i&&l(o)}}}function ea(T){let o,y="Example:",i,c,w;return c=new Ae({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, Wav2Vec2ConformerForPreTraining
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer <span class="hljs-keyword">import</span> _compute_mask_indices, _sample_negative_indices
<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>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-conformer-rel-pos-large&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Wav2Vec2ConformerForPreTraining.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-conformer-rel-pos-large&quot;</span>)

<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>input_values = feature_extractor(ds[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</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><span class="hljs-comment"># compute masked indices</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>batch_size, raw_sequence_length = input_values.shape
<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length).item()
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_time_indices = _compute_mask_indices(
<span class="hljs-meta">... </span>    shape=(batch_size, sequence_length), mask_prob=<span class="hljs-number">0.2</span>, mask_length=<span class="hljs-number">2</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampled_negative_indices = _sample_negative_indices(
<span class="hljs-meta">... </span>    features_shape=(batch_size, sequence_length),
<span class="hljs-meta">... </span>    num_negatives=model.config.num_negatives,
<span class="hljs-meta">... </span>    mask_time_indices=mask_time_indices,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_time_indices = torch.tensor(data=mask_time_indices, device=input_values.device, dtype=torch.long)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampled_negative_indices = torch.tensor(
<span class="hljs-meta">... </span>    data=sampled_negative_indices, device=input_values.device, dtype=torch.long
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(input_values, mask_time_indices=mask_time_indices)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># show that cosine similarity is much higher than random</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>cosine_sim[mask_time_indices.to(torch.<span class="hljs-built_in">bool</span>)].mean() &gt; <span class="hljs-number">0.5</span>
tensor(<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># for contrastive loss training model should be put into train mode</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = model.train()
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(
<span class="hljs-meta">... </span>    input_values, mask_time_indices=mask_time_indices, sampled_negative_indices=sampled_negative_indices
<span class="hljs-meta">... </span>).loss`,wrap:!1}}),{c(){o=d("p"),o.textContent=y,i=r(),u(c.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),f(o)!=="svelte-11lpom8"&&(o.textContent=y),i=s(t),h(c.$$.fragment,t)},m(t,M){p(t,o,M),p(t,i,M),g(c,t,M),w=!0},p:N,i(t){w||(v(c.$$.fragment,t),w=!0)},o(t){_(c.$$.fragment,t),w=!1},d(t){t&&(l(o),l(i)),b(c,t)}}}function oa(T){let o,y,i,c,w,t,M,Jo,he,Ot='The Wav2Vec2-Conformer was added to an updated version of <a href="https://arxiv.org/abs/2010.05171" rel="nofollow">fairseq S2T: Fast Speech-to-Text Modeling with fairseq</a> by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.',Fo,ge,Kt="The official results of the model can be found in Table 3 and Table 4 of the paper.",Zo,ve,en='The Wav2Vec2-Conformer weights were released by the Meta AI team within the <a href="https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md#pre-trained-models" rel="nofollow">Fairseq library</a>.',$o,_e,on=`This model was contributed by <a href="https://huggingface.co/patrickvonplaten" rel="nofollow">patrickvonplaten</a>.
The original code can be found <a href="https://github.com/pytorch/fairseq/tree/main/examples/wav2vec" rel="nofollow">here</a>.`,xo,be,tn='Note: Meta (FAIR) released a new version of <a href="https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert" rel="nofollow">Wav2Vec2-BERT 2.0</a> - it’s pretrained on 4.5M hours of audio. We especially recommend using it for fine-tuning tasks, e.g. as per <a href="https://huggingface.co/blog/fine-tune-w2v2-bert" rel="nofollow">this guide</a>.',Uo,ye,Xo,we,nn=`<li>Wav2Vec2-Conformer follows the same architecture as Wav2Vec2, but replaces the <em>Attention</em>-block with a <em>Conformer</em>-block
as introduced in <a href="https://arxiv.org/abs/2005.08100" rel="nofollow">Conformer: Convolution-augmented Transformer for Speech Recognition</a>.</li> <li>For the same number of layers, Wav2Vec2-Conformer requires more parameters than Wav2Vec2, but also yields
an improved word error rate.</li> <li>Wav2Vec2-Conformer uses the same tokenizer and feature extractor as Wav2Vec2.</li> <li>Wav2Vec2-Conformer can use either no relative position embeddings, Transformer-XL-like position embeddings, or
rotary position embeddings by setting the correct <code>config.position_embeddings_type</code>.</li>`,Go,Me,zo,Te,an='<li><a href="../tasks/audio_classification">Audio classification task guide</a></li> <li><a href="../tasks/asr">Automatic speech recognition task guide</a></li>',Ro,Ce,Io,G,We,nt,Pe,rn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a>. It is used to
instantiate an Wav2Vec2Conformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2Conformer
<a href="https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large" rel="nofollow">facebook/wav2vec2-conformer-rel-pos-large</a>
architecture.`,at,De,sn=`Configuration objects inherit from <a href="/docs/transformers/v4.43.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/v4.43.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,rt,oe,No,je,Bo,ee,Ve,st,Oe,ln='Output type of <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a>, with potential hidden states and attentions.',qo,ke,Yo,J,Je,lt,Ke,cn=`The bare Wav2Vec2Conformer Model transformer outputting raw hidden-states without any specific head on top.
Wav2Vec2Conformer was proposed in <a href="https://arxiv.org/abs/2006.11477" rel="nofollow">wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations</a> by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.`,it,eo,dn=`This model inherits from <a href="/docs/transformers/v4.43.0/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).`,ct,oo,mn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#nn.Module" rel="nofollow">nn.Module</a> sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.`,dt,B,Fe,mt,to,pn='The <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> forward method, overrides the <code>__call__</code> special method.',pt,te,ft,ne,Ho,Ze,Lo,F,$e,ut,no,fn=`Wav2Vec2Conformer Model with a <code>language modeling</code> head on top for Connectionist Temporal Classification (CTC).
Wav2Vec2Conformer was proposed in <a href="https://arxiv.org/abs/2006.11477" rel="nofollow">wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations</a> by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.`,ht,ao,un=`This model inherits from <a href="/docs/transformers/v4.43.0/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).`,gt,ro,hn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#nn.Module" rel="nofollow">nn.Module</a> sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.`,vt,q,xe,_t,so,gn='The <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> forward method, overrides the <code>__call__</code> special method.',bt,ae,yt,re,So,Ue,Qo,C,Xe,wt,lo,vn=`Wav2Vec2Conformer Model with a sequence classification head on top (a linear layer over the pooled output) for
tasks like SUPERB Keyword Spotting.`,Mt,io,_n=`Wav2Vec2Conformer was proposed in <a href="https://arxiv.org/abs/2006.11477" rel="nofollow">wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations</a> by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.`,Tt,co,bn=`This model inherits from <a href="/docs/transformers/v4.43.0/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).`,Ct,mo,yn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#nn.Module" rel="nofollow">nn.Module</a> sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.`,Wt,Y,Ge,jt,po,wn='The <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Vt,se,kt,le,Eo,ze,Ao,W,Re,Jt,fo,Mn="Wav2Vec2Conformer Model with a frame classification head on top for tasks like Speaker Diarization.",Ft,uo,Tn=`Wav2Vec2Conformer was proposed in <a href="https://arxiv.org/abs/2006.11477" rel="nofollow">wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations</a> by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.`,Zt,ho,Cn=`This model inherits from <a href="/docs/transformers/v4.43.0/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).`,$t,go,Wn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#nn.Module" rel="nofollow">nn.Module</a> sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.`,xt,H,Ie,Ut,vo,jn='The <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> forward method, overrides the <code>__call__</code> special method.',Xt,ie,Gt,ce,Po,Ne,Do,j,Be,zt,_o,Vn="Wav2Vec2Conformer Model with an XVector feature extraction head on top for tasks like Speaker Verification.",Rt,bo,kn=`Wav2Vec2Conformer was proposed in <a href="https://arxiv.org/abs/2006.11477" rel="nofollow">wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations</a> by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.`,It,yo,Jn=`This model inherits from <a href="/docs/transformers/v4.43.0/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).`,Nt,wo,Fn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#nn.Module" rel="nofollow">nn.Module</a> sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.`,Bt,L,qe,qt,Mo,Zn='The <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> forward method, overrides the <code>__call__</code> special method.',Yt,de,Ht,me,Oo,Ye,Ko,Z,He,Lt,To,$n=`Wav2Vec2Conformer Model with a quantizer and <code>VQ</code> head on top.
Wav2Vec2Conformer was proposed in <a href="https://arxiv.org/abs/2006.11477" rel="nofollow">wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations</a> by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.`,St,Co,xn=`This model inherits from <a href="/docs/transformers/v4.43.0/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).`,Qt,Wo,Un=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#nn.Module" rel="nofollow">nn.Module</a> sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.`,Et,S,Le,At,jo,Xn='The <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> forward method, overrides the <code>__call__</code> special method.',Pt,pe,Dt,fe,et,Se,ot,ko,tt;return w=new Q({props:{title:"Wav2Vec2-Conformer",local:"wav2vec2-conformer",headingTag:"h1"}}),M=new Q({props:{title:"Overview",local:"overview",headingTag:"h2"}}),ye=new Q({props:{title:"Usage tips",local:"usage-tips",headingTag:"h2"}}),Me=new Q({props:{title:"Resources",local:"resources",headingTag:"h2"}}),Ce=new Q({props:{title:"Wav2Vec2ConformerConfig",local:"transformers.Wav2Vec2ConformerConfig",headingTag:"h2"}}),We=new X({props:{name:"class transformers.Wav2Vec2ConformerConfig",anchor:"transformers.Wav2Vec2ConformerConfig",parameters:[{name:"vocab_size",val:" = None"},{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_dropout",val:" = 0.0"},{name:"feat_quantizer_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:"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:"num_codevectors_per_group",val:" = 320"},{name:"num_codevector_groups",val:" = 2"},{name:"contrastive_logits_temperature",val:" = 0.1"},{name:"num_negatives",val:" = 100"},{name:"codevector_dim",val:" = 256"},{name:"proj_codevector_dim",val:" = 256"},{name:"diversity_loss_weight",val:" = 0.1"},{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:"tdnn_dim",val:" = (512, 512, 512, 512, 1500)"},{name:"tdnn_kernel",val:" = (5, 3, 3, 1, 1)"},{name:"tdnn_dilation",val:" = (1, 2, 3, 1, 1)"},{name:"xvector_output_dim",val:" = 512"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 1"},{name:"eos_token_id",val:" = 2"},{name:"add_adapter",val:" = False"},{name:"adapter_kernel_size",val:" = 3"},{name:"adapter_stride",val:" = 2"},{name:"num_adapter_layers",val:" = 3"},{name:"output_hidden_size",val:" = None"},{name:"position_embeddings_type",val:" = 'relative'"},{name:"rotary_embedding_base",val:" = 10000"},{name:"max_source_positions",val:" = 5000"},{name:"conv_depthwise_kernel_size",val:" = 31"},{name:"conformer_conv_dropout",val:" = 0.1"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Vocabulary size of the Wav2Vec2Conformer model. Defines the number of different tokens that can be
represented by the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a>.`,name:"vocab_size"},{anchor:"transformers.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.hidden_dropout",description:`<strong>hidden_dropout</strong> (<code>float</code>, <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"},{anchor:"transformers.Wav2Vec2ConformerConfig.activation_dropout",description:`<strong>activation_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for activations inside the fully connected layer.`,name:"activation_dropout"},{anchor:"transformers.Wav2Vec2ConformerConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.Wav2Vec2ConformerConfig.final_dropout",description:`<strong>final_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for the final projection layer of <a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a>.`,name:"final_dropout"},{anchor:"transformers.Wav2Vec2ConformerConfig.layerdrop",description:`<strong>layerdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The LayerDrop probability. See the [LayerDrop paper](see <a href="https://arxiv.org/abs/1909.11556" rel="nofollow">https://arxiv.org/abs/1909.11556</a>) for more
details.`,name:"layerdrop"},{anchor:"transformers.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.feat_quantizer_dropout",description:`<strong>feat_quantizer_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for quantized feature encoder states.`,name:"feat_quantizer_dropout"},{anchor:"transformers.Wav2Vec2ConformerConfig.conv_dim",description:`<strong>conv_dim</strong> (<code>Tuple[int]</code> or <code>List[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.Wav2Vec2ConformerConfig.conv_stride",description:`<strong>conv_stride</strong> (<code>Tuple[int]</code> or <code>List[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.Wav2Vec2ConformerConfig.conv_kernel",description:`<strong>conv_kernel</strong> (<code>Tuple[int]</code> or <code>List[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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.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.Wav2Vec2ConformerConfig.num_codevectors_per_group",description:`<strong>num_codevectors_per_group</strong> (<code>int</code>, <em>optional</em>, defaults to 320) &#x2014;
Number of entries in each quantization codebook (group).`,name:"num_codevectors_per_group"},{anchor:"transformers.Wav2Vec2ConformerConfig.num_codevector_groups",description:`<strong>num_codevector_groups</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
Number of codevector groups for product codevector quantization.`,name:"num_codevector_groups"},{anchor:"transformers.Wav2Vec2ConformerConfig.contrastive_logits_temperature",description:`<strong>contrastive_logits_temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The temperature <em>kappa</em> in the contrastive loss.`,name:"contrastive_logits_temperature"},{anchor:"transformers.Wav2Vec2ConformerConfig.feat_quantizer_dropout",description:`<strong>feat_quantizer_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for the output of the feature encoder that&#x2019;s used by the quantizer.`,name:"feat_quantizer_dropout"},{anchor:"transformers.Wav2Vec2ConformerConfig.num_negatives",description:`<strong>num_negatives</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#x2014;
Number of negative samples for the contrastive loss.`,name:"num_negatives"},{anchor:"transformers.Wav2Vec2ConformerConfig.codevector_dim",description:`<strong>codevector_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimensionality of the quantized feature vectors.`,name:"codevector_dim"},{anchor:"transformers.Wav2Vec2ConformerConfig.proj_codevector_dim",description:`<strong>proj_codevector_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimensionality of the final projection of both the quantized and the transformer features.`,name:"proj_codevector_dim"},{anchor:"transformers.Wav2Vec2ConformerConfig.diversity_loss_weight",description:`<strong>diversity_loss_weight</strong> (<code>int</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The weight of the codebook diversity loss component.`,name:"diversity_loss_weight"},{anchor:"transformers.Wav2Vec2ConformerConfig.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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a>.`,name:"ctc_loss_reduction"},{anchor:"transformers.Wav2Vec2ConformerConfig.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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a>.`,name:"ctc_zero_infinity"},{anchor:"transformers.Wav2Vec2ConformerConfig.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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a>.`,name:"use_weighted_layer_sum"},{anchor:"transformers.Wav2Vec2ConformerConfig.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"},{anchor:"transformers.Wav2Vec2ConformerConfig.tdnn_dim",description:`<strong>tdnn_dim</strong> (<code>Tuple[int]</code> or <code>List[int]</code>, <em>optional</em>, defaults to <code>(512, 512, 512, 512, 1500)</code>) &#x2014;
A tuple of integers defining the number of output channels of each 1D convolutional layer in the <em>TDNN</em>
module of the <em>XVector</em> model. The length of <em>tdnn_dim</em> defines the number of <em>TDNN</em> layers.`,name:"tdnn_dim"},{anchor:"transformers.Wav2Vec2ConformerConfig.tdnn_kernel",description:`<strong>tdnn_kernel</strong> (<code>Tuple[int]</code> or <code>List[int]</code>, <em>optional</em>, defaults to <code>(5, 3, 3, 1, 1)</code>) &#x2014;
A tuple of integers defining the kernel size of each 1D convolutional layer in the <em>TDNN</em> module of the
<em>XVector</em> model. The length of <em>tdnn_kernel</em> has to match the length of <em>tdnn_dim</em>.`,name:"tdnn_kernel"},{anchor:"transformers.Wav2Vec2ConformerConfig.tdnn_dilation",description:`<strong>tdnn_dilation</strong> (<code>Tuple[int]</code> or <code>List[int]</code>, <em>optional</em>, defaults to <code>(1, 2, 3, 1, 1)</code>) &#x2014;
A tuple of integers defining the dilation factor of each 1D convolutional layer in <em>TDNN</em> module of the
<em>XVector</em> model. The length of <em>tdnn_dilation</em> has to match the length of <em>tdnn_dim</em>.`,name:"tdnn_dilation"},{anchor:"transformers.Wav2Vec2ConformerConfig.xvector_output_dim",description:`<strong>xvector_output_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the <em>XVector</em> embedding vectors.`,name:"xvector_output_dim"},{anchor:"transformers.Wav2Vec2ConformerConfig.add_adapter",description:`<strong>add_adapter</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether a convolutional network should be stacked on top of the Wav2Vec2Conformer Encoder. Can be very
useful for warm-starting Wav2Vec2Conformer for SpeechEncoderDecoder models.`,name:"add_adapter"},{anchor:"transformers.Wav2Vec2ConformerConfig.adapter_kernel_size",description:`<strong>adapter_kernel_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
Kernel size of the convolutional layers in the adapter network. Only relevant if <code>add_adapter is True</code>.`,name:"adapter_kernel_size"},{anchor:"transformers.Wav2Vec2ConformerConfig.adapter_stride",description:`<strong>adapter_stride</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
Stride of the convolutional layers in the adapter network. Only relevant if <code>add_adapter is True</code>.`,name:"adapter_stride"},{anchor:"transformers.Wav2Vec2ConformerConfig.num_adapter_layers",description:`<strong>num_adapter_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
Number of convolutional layers that should be used in the adapter network. Only relevant if <code>add_adapter is True</code>.`,name:"num_adapter_layers"},{anchor:"transformers.Wav2Vec2ConformerConfig.output_hidden_size",description:`<strong>output_hidden_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Dimensionality of the encoder output layer. If not defined, this defaults to <em>hidden-size</em>. Only relevant
if <code>add_adapter is True</code>.`,name:"output_hidden_size"},{anchor:"transformers.Wav2Vec2ConformerConfig.position_embeddings_type",description:`<strong>position_embeddings_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;relative&quot;</code>) &#x2014;
Can be specified to <code>relative</code> or <code>rotary</code> for relative or rotary position embeddings respectively. If left
<code>None</code> no relative position embedding is applied.`,name:"position_embeddings_type"},{anchor:"transformers.Wav2Vec2ConformerConfig.rotary_embedding_base",description:`<strong>rotary_embedding_base</strong> (<code>int</code>, <em>optional</em>, defaults to 10000) &#x2014;
If <code>&quot;rotary&quot;</code> position embeddings are used, defines the size of the embedding base.`,name:"rotary_embedding_base"},{anchor:"transformers.Wav2Vec2ConformerConfig.max_source_positions",description:`<strong>max_source_positions</strong> (<code>int</code>, <em>optional</em>, defaults to 5000) &#x2014;
if <code>&quot;relative&quot;</code> position embeddings are used, defines the maximum source input positions.`,name:"max_source_positions"},{anchor:"transformers.Wav2Vec2ConformerConfig.conv_depthwise_kernel_size",description:`<strong>conv_depthwise_kernel_size</strong> (<code>int</code>, defaults to 31) &#x2014;
Kernel size of convolutional depthwise 1D layer in Conformer blocks.`,name:"conv_depthwise_kernel_size"},{anchor:"transformers.Wav2Vec2ConformerConfig.conformer_conv_dropout",description:`<strong>conformer_conv_dropout</strong> (<code>float</code>, defaults to 0.1) &#x2014;
The dropout probability for all convolutional layers in Conformer blocks.`,name:"conformer_conv_dropout"}],source:"https://github.com/huggingface/transformers/blob/v4.43.0/src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py#L27"}}),oe=new Ee({props:{anchor:"transformers.Wav2Vec2ConformerConfig.example",$$slots:{default:[qn]},$$scope:{ctx:T}}}),je=new Q({props:{title:"Wav2Vec2Conformer specific outputs",local:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput",headingTag:"h2"}}),Ve=new X({props:{name:"class transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput",anchor:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput",parameters:[{name:"loss",val:": Optional = None"},{name:"projected_states",val:": FloatTensor = None"},{name:"projected_quantized_states",val:": FloatTensor = None"},{name:"codevector_perplexity",val:": FloatTensor = None"},{name:"hidden_states",val:": Optional = None"},{name:"attentions",val:": Optional = None"},{name:"contrastive_loss",val:": Optional = None"},{name:"diversity_loss",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput.loss",description:`<strong>loss</strong> (<em>optional</em>, returned when <code>sample_negative_indices</code> are passed, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) &#x2014;
Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the <a href="https://arxiv.org/pdf/2006.11477.pdf" rel="nofollow">official
paper</a> . (classification) loss.`,name:"loss"},{anchor:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput.projected_states",description:`<strong>projected_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.proj_codevector_dim)</code>) &#x2014;
Hidden-states of the model projected to <em>config.proj_codevector_dim</em> that can be used to predict the masked
projected quantized states.`,name:"projected_states"},{anchor:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput.projected_quantized_states",description:`<strong>projected_quantized_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.proj_codevector_dim)</code>) &#x2014;
Quantized extracted feature vectors projected to <em>config.proj_codevector_dim</em> representing the positive
target vectors for contrastive loss.`,name:"projected_quantized_states"},{anchor:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput.hidden_states",description:`<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>) &#x2014;
Tuple of <code>torch.FloatTensor</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.`,name:"hidden_states"},{anchor:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput.attentions",description:`<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>) &#x2014;
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.`,name:"attentions"},{anchor:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput.contrastive_loss",description:`<strong>contrastive_loss</strong> (<em>optional</em>, returned when <code>sample_negative_indices</code> are passed, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) &#x2014;
The contrastive loss (L_m) as stated in the <a href="https://arxiv.org/pdf/2006.11477.pdf" rel="nofollow">official paper</a> .`,name:"contrastive_loss"},{anchor:"transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput.diversity_loss",description:`<strong>diversity_loss</strong> (<em>optional</em>, returned when <code>sample_negative_indices</code> are passed, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) &#x2014;
The diversity loss (L_d) as stated in the <a href="https://arxiv.org/pdf/2006.11477.pdf" rel="nofollow">official paper</a> .`,name:"diversity_loss"}],source:"https://github.com/huggingface/transformers/blob/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L68"}}),ke=new Q({props:{title:"Wav2Vec2ConformerModel",local:"transformers.Wav2Vec2ConformerModel",headingTag:"h2"}}),Je=new X({props:{name:"class transformers.Wav2Vec2ConformerModel",anchor:"transformers.Wav2Vec2ConformerModel",parameters:[{name:"config",val:": Wav2Vec2ConformerConfig"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</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/v4.43.0/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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1228"}}),Fe=new X({props:{name:"forward",anchor:"transformers.Wav2Vec2ConformerModel.forward",parameters:[{name:"input_values",val:": Optional"},{name:"attention_mask",val:": Optional = None"},{name:"mask_time_indices",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerModel.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/v4.43.0/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/v4.43.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.Wav2Vec2ConformerModel.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/wav2vec2-conformer-rel-pos-large" rel="nofollow">wav2vec2-conformer-rel-pos-large</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.Wav2Vec2ConformerModel.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.Wav2Vec2ConformerModel.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.Wav2Vec2ConformerModel.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/v4.43.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1305",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2#transformers.modeling_outputs.Wav2Vec2BaseModelOutput"
>transformers.modeling_outputs.Wav2Vec2BaseModelOutput</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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig"
>Wav2Vec2ConformerConfig</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>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>extract_features</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, conv_dim[-1])</code>) — Sequence of extracted feature vectors of the last convolutional 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>) — Tuple of <code>torch.FloatTensor</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(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2#transformers.modeling_outputs.Wav2Vec2BaseModelOutput"
>transformers.modeling_outputs.Wav2Vec2BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),te=new Vo({props:{$$slots:{default:[Yn]},$$scope:{ctx:T}}}),ne=new Ee({props:{anchor:"transformers.Wav2Vec2ConformerModel.forward.example",$$slots:{default:[Hn]},$$scope:{ctx:T}}}),Ze=new Q({props:{title:"Wav2Vec2ConformerForCTC",local:"transformers.Wav2Vec2ConformerForCTC",headingTag:"h2"}}),$e=new X({props:{name:"class transformers.Wav2Vec2ConformerForCTC",anchor:"transformers.Wav2Vec2ConformerForCTC",parameters:[{name:"config",val:""},{name:"target_lang",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForCTC.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</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/v4.43.0/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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1586"}}),xe=new X({props:{name:"forward",anchor:"transformers.Wav2Vec2ConformerForCTC.forward",parameters:[{name:"input_values",val:": Optional"},{name:"attention_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"labels",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForCTC.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/v4.43.0/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/v4.43.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.Wav2Vec2ConformerForCTC.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/wav2vec2-conformer-rel-pos-large" rel="nofollow">wav2vec2-conformer-rel-pos-large</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.Wav2Vec2ConformerForCTC.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.Wav2Vec2ConformerForCTC.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.Wav2Vec2ConformerForCTC.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/v4.43.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.Wav2Vec2ConformerForCTC.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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1623",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/v4.43.0/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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig"
>Wav2Vec2ConformerConfig</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) — 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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/v4.43.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutput"
>transformers.modeling_outputs.CausalLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ae=new Vo({props:{$$slots:{default:[Ln]},$$scope:{ctx:T}}}),re=new Ee({props:{anchor:"transformers.Wav2Vec2ConformerForCTC.forward.example",$$slots:{default:[Sn]},$$scope:{ctx:T}}}),Ue=new Q({props:{title:"Wav2Vec2ConformerForSequenceClassification",local:"transformers.Wav2Vec2ConformerForSequenceClassification",headingTag:"h2"}}),Xe=new X({props:{name:"class transformers.Wav2Vec2ConformerForSequenceClassification",anchor:"transformers.Wav2Vec2ConformerForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</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/v4.43.0/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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1703"}}),Ge=new X({props:{name:"forward",anchor:"transformers.Wav2Vec2ConformerForSequenceClassification.forward",parameters:[{name:"input_values",val:": Optional"},{name:"attention_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"labels",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForSequenceClassification.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/v4.43.0/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/v4.43.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.Wav2Vec2ConformerForSequenceClassification.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/wav2vec2-conformer-rel-pos-large" rel="nofollow">wav2vec2-conformer-rel-pos-large</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.Wav2Vec2ConformerForSequenceClassification.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.Wav2Vec2ConformerForSequenceClassification.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.Wav2Vec2ConformerForSequenceClassification.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/v4.43.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.Wav2Vec2ConformerForSequenceClassification.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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1745",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/v4.43.0/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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig"
>Wav2Vec2ConformerConfig</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) — 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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/v4.43.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),se=new Vo({props:{$$slots:{default:[Qn]},$$scope:{ctx:T}}}),le=new Ee({props:{anchor:"transformers.Wav2Vec2ConformerForSequenceClassification.forward.example",$$slots:{default:[En]},$$scope:{ctx:T}}}),ze=new Q({props:{title:"Wav2Vec2ConformerForAudioFrameClassification",local:"transformers.Wav2Vec2ConformerForAudioFrameClassification",headingTag:"h2"}}),Re=new X({props:{name:"class transformers.Wav2Vec2ConformerForAudioFrameClassification",anchor:"transformers.Wav2Vec2ConformerForAudioFrameClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForAudioFrameClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</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/v4.43.0/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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1815"}}),Ie=new X({props:{name:"forward",anchor:"transformers.Wav2Vec2ConformerForAudioFrameClassification.forward",parameters:[{name:"input_values",val:": Optional"},{name:"attention_mask",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForAudioFrameClassification.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/v4.43.0/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/v4.43.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.Wav2Vec2ConformerForAudioFrameClassification.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/wav2vec2-conformer-rel-pos-large" rel="nofollow">wav2vec2-conformer-rel-pos-large</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.Wav2Vec2ConformerForAudioFrameClassification.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.Wav2Vec2ConformerForAudioFrameClassification.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.Wav2Vec2ConformerForAudioFrameClassification.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/v4.43.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.Wav2Vec2ConformerForAudioFrameClassification.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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1856",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/v4.43.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig"
>Wav2Vec2ConformerConfig</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)  — Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) — Classification 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/v4.43.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ie=new Vo({props:{$$slots:{default:[An]},$$scope:{ctx:T}}}),ce=new Ee({props:{anchor:"transformers.Wav2Vec2ConformerForAudioFrameClassification.forward.example",$$slots:{default:[Pn]},$$scope:{ctx:T}}}),Ne=new Q({props:{title:"Wav2Vec2ConformerForXVector",local:"transformers.Wav2Vec2ConformerForXVector",headingTag:"h2"}}),Be=new X({props:{name:"class transformers.Wav2Vec2ConformerForXVector",anchor:"transformers.Wav2Vec2ConformerForXVector",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForXVector.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</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/v4.43.0/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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1974"}}),qe=new X({props:{name:"forward",anchor:"transformers.Wav2Vec2ConformerForXVector.forward",parameters:[{name:"input_values",val:": Optional"},{name:"attention_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"labels",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForXVector.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/v4.43.0/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/v4.43.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.Wav2Vec2ConformerForXVector.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/wav2vec2-conformer-rel-pos-large" rel="nofollow">wav2vec2-conformer-rel-pos-large</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.Wav2Vec2ConformerForXVector.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.Wav2Vec2ConformerForXVector.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.Wav2Vec2ConformerForXVector.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/v4.43.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.Wav2Vec2ConformerForXVector.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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L2033",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/v4.43.0/en/main_classes/output#transformers.modeling_outputs.XVectorOutput"
>transformers.modeling_outputs.XVectorOutput</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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig"
>Wav2Vec2ConformerConfig</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) — Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.xvector_output_dim)</code>) — Classification hidden states before AMSoftmax.</p>
</li>
<li>
<p><strong>embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.xvector_output_dim)</code>) — Utterance embeddings used for vector similarity-based retrieval.</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>) — Tuple of <code>torch.FloatTensor</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(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/v4.43.0/en/main_classes/output#transformers.modeling_outputs.XVectorOutput"
>transformers.modeling_outputs.XVectorOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),de=new Vo({props:{$$slots:{default:[Dn]},$$scope:{ctx:T}}}),me=new Ee({props:{anchor:"transformers.Wav2Vec2ConformerForXVector.forward.example",$$slots:{default:[On]},$$scope:{ctx:T}}}),Ye=new Q({props:{title:"Wav2Vec2ConformerForPreTraining",local:"transformers.Wav2Vec2ConformerForPreTraining",headingTag:"h2"}}),He=new X({props:{name:"class transformers.Wav2Vec2ConformerForPreTraining",anchor:"transformers.Wav2Vec2ConformerForPreTraining",parameters:[{name:"config",val:": Wav2Vec2ConformerConfig"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</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/v4.43.0/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/v4.43.0/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py#L1367"}}),Le=new X({props:{name:"forward",anchor:"transformers.Wav2Vec2ConformerForPreTraining.forward",parameters:[{name:"input_values",val:": Optional"},{name:"attention_mask",val:": Optional = None"},{name:"mask_time_indices",val:": Optional = None"},{name:"sampled_negative_indices",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.Wav2Vec2ConformerForPreTraining.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/v4.43.0/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/v4.43.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.Wav2Vec2ConformerForPreTraining.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/wav2vec2-conformer-rel-pos-large" rel="nofollow">wav2vec2-conformer-rel-pos-large</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.Wav2Vec2ConformerForPreTraining.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<p>A <a
  href="/docs/transformers/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput"
>transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput</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/v4.43.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig"
>Wav2Vec2ConformerConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<em>optional</em>, returned when <code>sample_negative_indices</code> are passed, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) — Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the <a
  href="https://arxiv.org/pdf/2006.11477.pdf"
  rel="nofollow"
>official
paper</a> . (classification) loss.</p>
</li>
<li>
<p><strong>projected_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.proj_codevector_dim)</code>) — Hidden-states of the model projected to <em>config.proj_codevector_dim</em> that can be used to predict the masked
projected quantized states.</p>
</li>
<li>
<p><strong>projected_quantized_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.proj_codevector_dim)</code>) — Quantized extracted feature vectors projected to <em>config.proj_codevector_dim</em> representing the positive
target vectors for contrastive loss.</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>) — Tuple of <code>torch.FloatTensor</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(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — 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>
<li>
<p><strong>contrastive_loss</strong> (<em>optional</em>, returned when <code>sample_negative_indices</code> are passed, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) — The contrastive loss (L_m) as stated in the <a
  href="https://arxiv.org/pdf/2006.11477.pdf"
  rel="nofollow"
>official paper</a> .</p>
</li>
<li>
<p><strong>diversity_loss</strong> (<em>optional</em>, returned when <code>sample_negative_indices</code> are passed, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) — The diversity loss (L_d) as stated in the <a
  href="https://arxiv.org/pdf/2006.11477.pdf"
  rel="nofollow"
>official paper</a> .</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
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>transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
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