import{S as Bc,i as Ic,s as qc,e as n,k as p,w as y,t as r,M as Rc,c as a,d as o,m as h,a as s,x as S,h as i,b as d,G as e,g as _,y as w,q as T,o as U,B as M,v as Pc,L as so}from"../../chunks/vendor-hf-doc-builder.js";import{T as Bo}from"../../chunks/Tip-hf-doc-builder.js";import{D as E}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ro}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as R}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as ao}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Yc(k){let c,b,g,u,v;return u=new ro({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> UniSpeechSatModel, UniSpeechSatConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a UniSpeechSat microsoft/unispeech-sat-base-100h-libri-ft style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = UniSpeechSatConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the microsoft/unispeech-sat-base-100h-libri-ft style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UniSpeechSatModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){c=n("p"),b=r("Example:"),g=p(),y(u.$$.fragment)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Example:"),m.forEach(o),g=h(l),S(u.$$.fragment,l)},m(l,m){_(l,c,m),e(c,b),_(l,g,m),w(u,l,m),v=!0},p:so,i(l){v||(T(u.$$.fragment,l),v=!0)},o(l){U(u.$$.fragment,l),v=!1},d(l){l&&o(c),l&&o(g),M(u,l)}}}function Qc(k){let c,b,g,u,v;return{c(){c=n("p"),b=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),u=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a(m,"CODE",{});var j=s(g);u=i(j,"Module"),j.forEach(o),v=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,c,m),e(c,b),e(c,g),e(g,u),e(c,v)},d(l){l&&o(c)}}}function Lc(k){let c,b,g,u,v;return u=new ro({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, UniSpeechSatModel
<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>)
<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;microsoft/unispeech-sat-base-100h-libri-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UniSpeechSatModel.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-100h-libri-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">768</span>]`}}),{c(){c=n("p"),b=r("Example:"),g=p(),y(u.$$.fragment)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Example:"),m.forEach(o),g=h(l),S(u.$$.fragment,l)},m(l,m){_(l,c,m),e(c,b),_(l,g,m),w(u,l,m),v=!0},p:so,i(l){v||(T(u.$$.fragment,l),v=!0)},o(l){U(u.$$.fragment,l),v=!1},d(l){l&&o(c),l&&o(g),M(u,l)}}}function Dc(k){let c,b,g,u,v;return{c(){c=n("p"),b=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),u=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a(m,"CODE",{});var j=s(g);u=i(j,"Module"),j.forEach(o),v=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,c,m),e(c,b),e(c,g),e(g,u),e(c,v)},d(l){l&&o(c)}}}function Hc(k){let c,b,g,u,v;return u=new ro({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, UniSpeechSatForCTC
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

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

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-100h-libri-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UniSpeechSatForCTC.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-100h-libri-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 QUILDER 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">39.88</span>`}}),{c(){c=n("p"),b=r("Example:"),g=p(),y(u.$$.fragment)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Example:"),m.forEach(o),g=h(l),S(u.$$.fragment,l)},m(l,m){_(l,c,m),e(c,b),_(l,g,m),w(u,l,m),v=!0},p:so,i(l){v||(T(u.$$.fragment,l),v=!0)},o(l){U(u.$$.fragment,l),v=!1},d(l){l&&o(c),l&&o(g),M(u,l)}}}function Oc(k){let c,b,g,u,v;return{c(){c=n("p"),b=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),u=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a(m,"CODE",{});var j=s(g);u=i(j,"Module"),j.forEach(o),v=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,c,m),e(c,b),e(c,g),e(g,u),e(c,v)},d(l){l&&o(c)}}}function Kc(k){let c,b,g,u,v;return u=new ro({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, UniSpeechSatForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

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

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-100h-libri-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UniSpeechSatForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-100h-libri-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`}}),{c(){c=n("p"),b=r("Example:"),g=p(),y(u.$$.fragment)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Example:"),m.forEach(o),g=h(l),S(u.$$.fragment,l)},m(l,m){_(l,c,m),e(c,b),_(l,g,m),w(u,l,m),v=!0},p:so,i(l){v||(T(u.$$.fragment,l),v=!0)},o(l){U(u.$$.fragment,l),v=!1},d(l){l&&o(c),l&&o(g),M(u,l)}}}function ed(k){let c,b,g,u,v;return{c(){c=n("p"),b=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),u=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a(m,"CODE",{});var j=s(g);u=i(j,"Module"),j.forEach(o),v=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,c,m),e(c,b),e(c,g),e(g,u),e(c,v)},d(l){l&&o(c)}}}function td(k){let c,b,g,u,v;return u=new ro({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, UniSpeechSatForAudioFrameClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

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

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-plus-sd&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UniSpeechSatForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-plus-sd&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()
<span class="hljs-meta">&gt;&gt;&gt; </span>labels[<span class="hljs-number">0</span>].tolist()
[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>]`}}),{c(){c=n("p"),b=r("Example:"),g=p(),y(u.$$.fragment)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Example:"),m.forEach(o),g=h(l),S(u.$$.fragment,l)},m(l,m){_(l,c,m),e(c,b),_(l,g,m),w(u,l,m),v=!0},p:so,i(l){v||(T(u.$$.fragment,l),v=!0)},o(l){U(u.$$.fragment,l),v=!1},d(l){l&&o(c),l&&o(g),M(u,l)}}}function od(k){let c,b,g,u,v;return{c(){c=n("p"),b=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),u=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a(m,"CODE",{});var j=s(g);u=i(j,"Module"),j.forEach(o),v=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,c,m),e(c,b),e(c,g),e(g,u),e(c,v)},d(l){l&&o(c)}}}function nd(k){let c,b,g,u,v;return u=new ro({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9GZWF0dXJlRXh0cmFjdG9yJTJDJTIwVW5pU3BlZWNoU2F0Rm9yWFZlY3RvciUwQWZyb20lMjBkYXRhc2V0cyUyMGltcG9ydCUyMGxvYWRfZGF0YXNldCUwQWltcG9ydCUyMHRvcmNoJTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJoZi1pbnRlcm5hbC10ZXN0aW5nJTJGbGlicmlzcGVlY2hfYXNyX2RlbW8lMjIlMkMlMjAlMjJjbGVhbiUyMiUyQyUyMHNwbGl0JTNEJTIydmFsaWRhdGlvbiUyMiklMEFkYXRhc2V0JTIwJTNEJTIwZGF0YXNldC5zb3J0KCUyMmlkJTIyKSUwQXNhbXBsaW5nX3JhdGUlMjAlM0QlMjBkYXRhc2V0LmZlYXR1cmVzJTVCJTIyYXVkaW8lMjIlNUQuc2FtcGxpbmdfcmF0ZSUwQSUwQWZlYXR1cmVfZXh0cmFjdG9yJTIwJTNEJTIwQXV0b0ZlYXR1cmVFeHRyYWN0b3IuZnJvbV9wcmV0cmFpbmVkKCUyMm1pY3Jvc29mdCUyRnVuaXNwZWVjaC1zYXQtYmFzZS1wbHVzLXN2JTIyKSUwQW1vZGVsJTIwJTNEJTIwVW5pU3BlZWNoU2F0Rm9yWFZlY3Rvci5mcm9tX3ByZXRyYWluZWQoJTIybWljcm9zb2Z0JTJGdW5pc3BlZWNoLXNhdC1iYXNlLXBsdXMtc3YlMjIpJTBBJTBBJTIzJTIwYXVkaW8lMjBmaWxlJTIwaXMlMjBkZWNvZGVkJTIwb24lMjB0aGUlMjBmbHklMEFpbnB1dHMlMjAlM0QlMjBmZWF0dXJlX2V4dHJhY3RvciglMEElMjAlMjAlMjAlMjAlNUJkJTVCJTIyYXJyYXklMjIlNUQlMjBmb3IlMjBkJTIwaW4lMjBkYXRhc2V0JTVCJTNBMiU1RCU1QiUyMmF1ZGlvJTIyJTVEJTVEJTJDJTIwc2FtcGxpbmdfcmF0ZSUzRHNhbXBsaW5nX3JhdGUlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyJTJDJTIwcGFkZGluZyUzRFRydWUlMEEpJTBBd2l0aCUyMHRvcmNoLm5vX2dyYWQoKSUzQSUwQSUyMCUyMCUyMCUyMGVtYmVkZGluZ3MlMjAlM0QlMjBtb2RlbCgqKmlucHV0cykuZW1iZWRkaW5ncyUwQSUwQWVtYmVkZGluZ3MlMjAlM0QlMjB0b3JjaC5ubi5mdW5jdGlvbmFsLm5vcm1hbGl6ZShlbWJlZGRpbmdzJTJDJTIwZGltJTNELTEpLmNwdSgpJTBBJTBBJTIzJTIwdGhlJTIwcmVzdWx0aW5nJTIwZW1iZWRkaW5ncyUyMGNhbiUyMGJlJTIwdXNlZCUyMGZvciUyMGNvc2luZSUyMHNpbWlsYXJpdHktYmFzZWQlMjByZXRyaWV2YWwlMEFjb3NpbmVfc2ltJTIwJTNEJTIwdG9yY2gubm4uQ29zaW5lU2ltaWxhcml0eShkaW0lM0QtMSklMEFzaW1pbGFyaXR5JTIwJTNEJTIwY29zaW5lX3NpbShlbWJlZGRpbmdzJTVCMCU1RCUyQyUyMGVtYmVkZGluZ3MlNUIxJTVEKSUwQXRocmVzaG9sZCUyMCUzRCUyMDAuNyUyMCUyMCUyMyUyMHRoZSUyMG9wdGltYWwlMjB0aHJlc2hvbGQlMjBpcyUyMGRhdGFzZXQtZGVwZW5kZW50JTBBaWYlMjBzaW1pbGFyaXR5JTIwJTNDJTIwdGhyZXNob2xkJTNBJTBBJTIwJTIwJTIwJTIwcHJpbnQoJTIyU3BlYWtlcnMlMjBhcmUlMjBub3QlMjB0aGUlMjBzYW1lISUyMiklMEFyb3VuZChzaW1pbGFyaXR5Lml0ZW0oKSUyQyUyMDIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, UniSpeechSatForXVector
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

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

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-plus-sv&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UniSpeechSatForXVector.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base-plus-sv&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>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(similarity.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">0.97</span>`}}),{c(){c=n("p"),b=r("Example:"),g=p(),y(u.$$.fragment)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Example:"),m.forEach(o),g=h(l),S(u.$$.fragment,l)},m(l,m){_(l,c,m),e(c,b),_(l,g,m),w(u,l,m),v=!0},p:so,i(l){v||(T(u.$$.fragment,l),v=!0)},o(l){U(u.$$.fragment,l),v=!1},d(l){l&&o(c),l&&o(g),M(u,l)}}}function ad(k){let c,b,g,u,v;return{c(){c=n("p"),b=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),u=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a(m,"CODE",{});var j=s(g);u=i(j,"Module"),j.forEach(o),v=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,c,m),e(c,b),e(c,g),e(g,u),e(c,v)},d(l){l&&o(c)}}}function sd(k){let c,b,g,u,v;return u=new ro({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, UniSpeechSatForPreTraining
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers.models.unispeech_sat.modeling_unispeech_sat <span class="hljs-keyword">import</span> _compute_mask_indices

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UniSpeechSatForPreTraining.from_pretrained(<span class="hljs-string">&quot;microsoft/unispeech-sat-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># <span class="hljs-doctag">TODO:</span> Add full pretraining example</span>`}}),{c(){c=n("p"),b=r("Example:"),g=p(),y(u.$$.fragment)},l(l){c=a(l,"P",{});var m=s(c);b=i(m,"Example:"),m.forEach(o),g=h(l),S(u.$$.fragment,l)},m(l,m){_(l,c,m),e(c,b),_(l,g,m),w(u,l,m),v=!0},p:so,i(l){v||(T(u.$$.fragment,l),v=!0)},o(l){U(u.$$.fragment,l),v=!1},d(l){l&&o(c),l&&o(g),M(u,l)}}}function rd(k){let c,b,g,u,v,l,m,j,Xa,Vn,H,ve,Io,De,za,qo,Aa,Nn,be,Ga,He,Ba,Ia,Xn,io,qa,zn,lo,Ro,Ra,An,co,Pa,Gn,P,Oe,Ya,po,Qa,La,Da,Ke,Ha,ho,Oa,Ka,es,Po,ts,Bn,Y,os,et,ns,as,tt,ss,rs,In,O,ye,Yo,ot,is,Qo,ls,qn,Se,Lo,mo,cs,ds,Do,uo,ps,Rn,K,we,Ho,nt,hs,Oo,ms,Pn,Z,at,us,ee,fs,fo,gs,_s,st,vs,bs,ys,te,Ss,go,ws,Ts,_o,Us,Ms,ks,Te,Yn,oe,Ue,Ko,rt,js,en,Fs,Qn,ne,it,Cs,lt,$s,tn,Js,Ws,Ln,ae,Me,on,ct,xs,nn,Es,Dn,J,dt,Zs,pt,Vs,ht,Ns,Xs,zs,mt,As,vo,Gs,Bs,Is,ut,qs,ft,Rs,Ps,Ys,V,gt,Qs,se,Ls,bo,Ds,Hs,an,Os,Ks,er,ke,tr,je,Hn,re,Fe,sn,_t,or,rn,nr,On,W,vt,ar,ie,sr,ln,rr,ir,bt,lr,cr,dr,yt,pr,yo,hr,mr,ur,St,fr,wt,gr,_r,vr,N,Tt,br,le,yr,So,Sr,wr,cn,Tr,Ur,Mr,Ce,kr,$e,Kn,ce,Je,dn,Ut,jr,pn,Fr,ea,F,Mt,Cr,hn,$r,Jr,kt,Wr,jt,xr,Er,Zr,Ft,Vr,wo,Nr,Xr,zr,Ct,Ar,$t,Gr,Br,Ir,X,Jt,qr,de,Rr,To,Pr,Yr,mn,Qr,Lr,Dr,We,Hr,xe,ta,pe,Ee,un,Wt,Or,fn,Kr,oa,C,xt,ei,gn,ti,oi,Et,ni,Zt,ai,si,ri,Vt,ii,Uo,li,ci,di,Nt,pi,Xt,hi,mi,ui,z,zt,fi,he,gi,Mo,_i,vi,_n,bi,yi,Si,Ze,wi,Ve,na,me,Ne,vn,At,Ti,bn,Ui,aa,$,Gt,Mi,yn,ki,ji,Bt,Fi,It,Ci,$i,Ji,qt,Wi,ko,xi,Ei,Zi,Rt,Vi,Pt,Ni,Xi,zi,A,Yt,Ai,ue,Gi,jo,Bi,Ii,Sn,qi,Ri,Pi,Xe,Yi,ze,sa,fe,Ae,wn,Qt,Qi,Tn,Li,ra,x,Lt,Di,ge,Hi,Un,Oi,Ki,Dt,el,tl,ol,Ht,nl,Fo,al,sl,rl,Ot,il,Kt,ll,cl,dl,G,eo,pl,_e,hl,Co,ml,ul,Mn,fl,gl,_l,Ge,vl,Be,ia;return l=new R({}),De=new R({}),ot=new R({}),nt=new R({}),at=new E({props:{name:"class transformers.UniSpeechSatConfig",anchor:"transformers.UniSpeechSatConfig",parameters:[{name:"vocab_size",val:" = 32"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout",val:" = 0.1"},{name:"activation_dropout",val:" = 0.1"},{name:"attention_dropout",val:" = 0.1"},{name:"feat_proj_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:"do_stable_layer_norm",val:" = False"},{name:"apply_spec_augment",val:" = True"},{name:"mask_time_prob",val:" = 0.05"},{name:"mask_time_length",val:" = 10"},{name:"mask_time_min_masks",val:" = 2"},{name:"mask_feature_prob",val:" = 0.0"},{name:"mask_feature_length",val:" = 10"},{name:"mask_feature_min_masks",val:" = 0"},{name:"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:" = 'mean'"},{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:"num_clusters",val:" = 504"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.UniSpeechSatConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Vocabulary size of the UniSpeechSat model. Defines the number of different tokens that can be represented
by the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a>.`,name:"vocab_size"},{anchor:"transformers.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a>.`,name:"final_dropout"},{anchor:"transformers.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.feat_quantizer_dropout",description:`<strong>feat_quantizer_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probabilitiy for quantized feature encoder states.`,name:"feat_quantizer_dropout"},{anchor:"transformers.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.do_stable_layer_norm",description:`<strong>do_stable_layer_norm</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to apply <em>stable</em> layer norm architecture of the Transformer encoder. <code>do_stable_layer_norm is True</code> corresponds to applying layer norm before the attention layer, whereas <code>do_stable_layer_norm is False</code> corresponds to applying layer norm after the attention layer.`,name:"do_stable_layer_norm"},{anchor:"transformers.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.feat_quantizer_dropout",description:`<strong>feat_quantizer_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probabilitiy for the output of the feature encoder that&#x2019;s used by the quantizer.`,name:"feat_quantizer_dropout"},{anchor:"transformers.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.ctc_loss_reduction",description:`<strong>ctc_loss_reduction</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;mean&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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a>.`,name:"ctc_loss_reduction"},{anchor:"transformers.UniSpeechSatConfig.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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a>.`,name:"ctc_zero_infinity"},{anchor:"transformers.UniSpeechSatConfig.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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a>.`,name:"use_weighted_layer_sum"},{anchor:"transformers.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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.UniSpeechSatConfig.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"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/unispeech_sat/configuration_unispeech_sat.py#L34"}}),Te=new ao({props:{anchor:"transformers.UniSpeechSatConfig.example",$$slots:{default:[Yc]},$$scope:{ctx:k}}}),rt=new R({}),it=new E({props:{name:"class transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput",anchor:"transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput",parameters:[{name:"loss",val:": typing.Optional[torch.FloatTensor] = None"},{name:"logits",val:": FloatTensor = None"},{name:"projected_states",val:": FloatTensor = None"},{name:"projected_quantized_states",val:": FloatTensor = None"},{name:"codevector_perplexity",val:": FloatTensor = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}],parametersDescription:[{anchor:"transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput.loss",description:`<strong>loss</strong> (<em>optional</em>, returned when model is in train mode, <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.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput.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.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput.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.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput.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.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput.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"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L80"}}),ct=new R({}),dt=new E({props:{name:"class transformers.UniSpeechSatModel",anchor:"transformers.UniSpeechSatModel",parameters:[{name:"config",val:": UniSpeechSatConfig"}],parametersDescription:[{anchor:"transformers.UniSpeechSatModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1123"}}),gt=new E({props:{name:"forward",anchor:"transformers.UniSpeechSatModel.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"mask_time_indices",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.UniSpeechSatModel.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/v4.32.0/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/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.UniSpeechSatModel.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/microsoft/unispeech-sat-base-100h-libri-ft" rel="nofollow">microsoft/unispeech-sat-base-100h-libri-ft</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.UniSpeechSatModel.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.UniSpeechSatModel.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.UniSpeechSatModel.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/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1187",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig"
>UniSpeechSatConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>extract_features</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, conv_dim[-1])</code>) \u2014 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>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.modeling_outputs.Wav2Vec2BaseModelOutput"
>transformers.modeling_outputs.Wav2Vec2BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ke=new Bo({props:{$$slots:{default:[Qc]},$$scope:{ctx:k}}}),je=new ao({props:{anchor:"transformers.UniSpeechSatModel.forward.example",$$slots:{default:[Lc]},$$scope:{ctx:k}}}),_t=new R({}),vt=new E({props:{name:"class transformers.UniSpeechSatForCTC",anchor:"transformers.UniSpeechSatForCTC",parameters:[{name:"config",val:""},{name:"target_lang",val:": typing.Optional[str] = None"}],parametersDescription:[{anchor:"transformers.UniSpeechSatForCTC.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1386"}}),Tt=new E({props:{name:"forward",anchor:"transformers.UniSpeechSatForCTC.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"transformers.UniSpeechSatForCTC.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/v4.32.0/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/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.UniSpeechSatForCTC.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/microsoft/unispeech-sat-base-100h-libri-ft" rel="nofollow">microsoft/unispeech-sat-base-100h-libri-ft</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.UniSpeechSatForCTC.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.UniSpeechSatForCTC.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.UniSpeechSatForCTC.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/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.UniSpeechSatForCTC.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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1458",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig"
>UniSpeechSatConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutput"
>transformers.modeling_outputs.CausalLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ce=new Bo({props:{$$slots:{default:[Dc]},$$scope:{ctx:k}}}),$e=new ao({props:{anchor:"transformers.UniSpeechSatForCTC.forward.example",$$slots:{default:[Hc]},$$scope:{ctx:k}}}),Ut=new R({}),Mt=new E({props:{name:"class transformers.UniSpeechSatForSequenceClassification",anchor:"transformers.UniSpeechSatForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.UniSpeechSatForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1545"}}),Jt=new E({props:{name:"forward",anchor:"transformers.UniSpeechSatForSequenceClassification.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"transformers.UniSpeechSatForSequenceClassification.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/v4.32.0/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/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.UniSpeechSatForSequenceClassification.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/microsoft/unispeech-sat-base-100h-libri-ft" rel="nofollow">microsoft/unispeech-sat-base-100h-libri-ft</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.UniSpeechSatForSequenceClassification.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.UniSpeechSatForSequenceClassification.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.UniSpeechSatForSequenceClassification.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/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.UniSpeechSatForSequenceClassification.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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1593",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig"
>UniSpeechSatConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),We=new Bo({props:{$$slots:{default:[Oc]},$$scope:{ctx:k}}}),xe=new ao({props:{anchor:"transformers.UniSpeechSatForSequenceClassification.forward.example",$$slots:{default:[Kc]},$$scope:{ctx:k}}}),Wt=new R({}),xt=new E({props:{name:"class transformers.UniSpeechSatForAudioFrameClassification",anchor:"transformers.UniSpeechSatForAudioFrameClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.UniSpeechSatForAudioFrameClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1670"}}),zt=new E({props:{name:"forward",anchor:"transformers.UniSpeechSatForAudioFrameClassification.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.UniSpeechSatForAudioFrameClassification.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/v4.32.0/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/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.UniSpeechSatForAudioFrameClassification.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/microsoft/unispeech-sat-base-100h-libri-ft" rel="nofollow">microsoft/unispeech-sat-base-100h-libri-ft</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.UniSpeechSatForAudioFrameClassification.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.UniSpeechSatForAudioFrameClassification.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.UniSpeechSatForAudioFrameClassification.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/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.UniSpeechSatForAudioFrameClassification.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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1714",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig"
>UniSpeechSatConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided)  \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ze=new Bo({props:{$$slots:{default:[ed]},$$scope:{ctx:k}}}),Ve=new ao({props:{anchor:"transformers.UniSpeechSatForAudioFrameClassification.forward.example",$$slots:{default:[td]},$$scope:{ctx:k}}}),At=new R({}),Gt=new E({props:{name:"class transformers.UniSpeechSatForXVector",anchor:"transformers.UniSpeechSatForXVector",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.UniSpeechSatForXVector.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1834"}}),Yt=new E({props:{name:"forward",anchor:"transformers.UniSpeechSatForXVector.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"transformers.UniSpeechSatForXVector.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/v4.32.0/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/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.UniSpeechSatForXVector.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/microsoft/unispeech-sat-base-100h-libri-ft" rel="nofollow">microsoft/unispeech-sat-base-100h-libri-ft</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.UniSpeechSatForXVector.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.UniSpeechSatForXVector.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.UniSpeechSatForXVector.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/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.UniSpeechSatForXVector.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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1896",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig"
>UniSpeechSatConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.xvector_output_dim)</code>) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.modeling_outputs.XVectorOutput"
>transformers.modeling_outputs.XVectorOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Xe=new Bo({props:{$$slots:{default:[od]},$$scope:{ctx:k}}}),ze=new ao({props:{anchor:"transformers.UniSpeechSatForXVector.forward.example",$$slots:{default:[nd]},$$scope:{ctx:k}}}),Qt=new R({}),Lt=new E({props:{name:"class transformers.UniSpeechSatForPreTraining",anchor:"transformers.UniSpeechSatForPreTraining",parameters:[{name:"config",val:": UniSpeechSatConfig"}],parametersDescription:[{anchor:"transformers.UniSpeechSatForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1244"}}),eo=new E({props:{name:"forward",anchor:"transformers.UniSpeechSatForPreTraining.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor]"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.UniSpeechSatForPreTraining.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/v4.32.0/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/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__">Wav2Vec2Processor.<strong>call</strong>()</a> for details.`,name:"input_values"},{anchor:"transformers.UniSpeechSatForPreTraining.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/microsoft/unispeech-sat-base-100h-libri-ft" rel="nofollow">microsoft/unispeech-sat-base-100h-libri-ft</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.UniSpeechSatForPreTraining.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.UniSpeechSatForPreTraining.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.UniSpeechSatForPreTraining.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/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py#L1312",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput"
>transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput</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/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig"
>UniSpeechSatConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<em>optional</em>, returned when model is in train mode, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput"
>transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
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