Add support for WavlmForXVector

src/models.js

@@ -4735,6 +4735,47 @@ export class WavLMForSequenceClassification extends WavLMPreTrainedModel {
     }
 }
 
+/**
+ * WavLM Model with an XVector feature extraction head on top for tasks like Speaker Verification.
+ * 
+ * **Example:** Extract speaker embeddings with `WavLMForXVector`.
+ * ```javascript
+ * import { AutoProcessor, AutoModel, read_audio } from '@xenova/transformers';
+ * 
+ * const processor = await AutoProcessor.from_pretrained('D4ve-R/wavlm-base-plus-sv');
+ * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
+ * const audio = await read_audio(url, 16000);
+ * const inputs = await processor(audio);
+
+ * const model = await AutoModel.from_pretrained('D4ve-R/wavlm-base-plus-sv');
+ * const embeddings = await model(inputs);
+ * // {
+ * //   embeddings: Tensor {
+ * //     dims: [ 1, 512 ],
+ * //     type: 'float32',
+ * //     data: Float32Array(512) [-0.349443256855011, ...],
+ * //     size: 512
+ * //   },
+ * //   logits: Tensor {
+ * //     dims: [ 1, 512 ],
+ * //     type: 'float32',
+ * //     data: Float32Array(512) [0.022836603224277496, ...],
+ * //     size: 512
+ * //   }
+ * // }
+ * ```
+ */
+export class WavLMForXVector extends WavLMPreTrainedModel {
+    /**
+     * Calls the model on new inputs.
+     * @param {Object} model_inputs The inputs to the model.
+     * @returns {Promise<XVectorOutput>} An object containing the model's output logits for sequence classification.
+     */
+    async _call(model_inputs) {
+        return new XVectorOutput(await super._call(model_inputs));
+    }
+}
+
 //////////////////////////////////////////////////
 // SpeechT5 models
 /**
@@ -5483,6 +5524,10 @@ const MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = new Map([
     ['audio-spectrogram-transformer', ['ASTForAudioClassification', ASTForAudioClassification]],
 ]);
 
+const MODEL_FOR_XVECTOR_MAPPING_NAMES = new Map([
+    ['wavlm', ['WavLMForXVector', WavLMForXVector]],
+]);
+
 const MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES = new Map([
     ['vitmatte', ['VitMatteForImageMatting', VitMatteForImageMatting]],
 ]);
@@ -5523,6 +5568,7 @@ const MODEL_CLASS_TYPE_MAPPING = [
     [MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
     [MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES, MODEL_TYPES.Seq2Seq],
     [MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
+    [MODEL_FOR_XVECTOR_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
 ];
 
 for (const [mappings, type] of MODEL_CLASS_TYPE_MAPPING) {
@@ -5741,6 +5787,10 @@ export class AutoModelForAudioClassification extends PretrainedMixin {
     static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES];
 }
 
+export class AutoModelForXVector extends PretrainedMixin {
+    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_XVECTOR_MAPPING_NAMES];
+}
+
 export class AutoModelForDocumentQuestionAnswering extends PretrainedMixin {
     static MODEL_CLASS_MAPPINGS = [MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES];
 }
@@ -5793,6 +5843,22 @@ export class SequenceClassifierOutput extends ModelOutput {
     }
 }
 
+/**
+ * Base class for outputs of x-vector models.
+ */
+export class XVectorOutput extends ModelOutput {
+    /**
+     * @param {Object} output The output of the model.
+     * @param {Tensor} output.logits classification (or regression if config.num_labels==1) scores (before SoftMax).
+     * @param {Tensor} output.embeddings The embeddings of the input sequence.
+     */
+    constructor({ logits, embeddings }) {
+        super();
+        this.logits = logits;
+        this.embeddings = embeddings;
+    }
+}
+
 /**
  * Base class for outputs of token classification models.
  */