Add WavLM- & Wav2Vec2ForAudioFrameClassification support (#611)

* Add WavLMForXVector support

* fix model docs

* Add WavLMForAudioFrameClassification

* Add missing wWav2Vec2ForAudioFrameCl.

* Add doc comment

* Add doc string wav2vec2

* update comment

* make example like python

* Update src/models.js

---------

Co-authored-by: Joshua Lochner <[email protected]>

src/models.js

@@ -4571,6 +4571,20 @@ export class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel {
         return new SequenceClassifierOutput(await super._call(model_inputs));
     }
 }
+
+/**
+ * Wav2Vec2 Model with a frame classification head on top for tasks like Speaker Diarization.
+ */
+export class Wav2Vec2ForAudioFrameClassification extends Wav2Vec2PreTrainedModel {
+    /**
+     * Calls the model on new inputs.
+     * @param {Object} model_inputs The inputs to the model.
+     * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for sequence classification.
+     */
+    async _call(model_inputs) {
+        return new TokenClassifierOutput(await super._call(model_inputs));
+    }
+}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
@@ -4868,6 +4882,54 @@ export class WavLMForXVector extends WavLMPreTrainedModel {
     }
 }
 
+/**
+ * WavLM Model with a frame classification head on top for tasks like Speaker Diarization.
+ * 
+ * **Example:** Perform speaker diarization with `WavLMForAudioFrameClassification`.
+ * ```javascript
+ * import { AutoProcessor, AutoModelForAudioFrameClassification, read_audio } from '@xenova/transformers';
+ * 
+ * // Read and preprocess audio
+ * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base-plus-sd');
+ * 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);
+ * 
+ * // Run model with inputs
+ * const model = await AutoModelForAudioFrameClassification.from_pretrained('Xenova/wavlm-base-plus-sd');
+ * const { logits } = await model(inputs);
+ * // {
+ * //   logits: Tensor {
+ * //     dims: [ 1, 549, 2 ],  // [batch_size, num_frames, num_speakers]
+ * //     type: 'float32',
+ * //     data: Float32Array(1098) [-3.5301010608673096, ...],
+ * //     size: 1098
+ * //   }
+ * // }
+ * 
+ * const labels = logits[0].sigmoid().tolist().map(
+ *     frames => frames.map(speaker => speaker > 0.5 ? 1 : 0)
+ * );
+ * console.log(labels); // labels is a one-hot array of shape (num_frames, num_speakers)
+ * // [
+ * //     [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ * //     [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
+ * //     [0, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1],
+ * //     ...
+ * // ]
+ * ```
+ */
+export class WavLMForAudioFrameClassification extends WavLMPreTrainedModel {
+    /**
+     * Calls the model on new inputs.
+     * @param {Object} model_inputs The inputs to the model.
+     * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for sequence classification.
+     */
+    async _call(model_inputs) {
+        return new TokenClassifierOutput(await super._call(model_inputs));
+    }
+}
+
 //////////////////////////////////////////////////
 // SpeechT5 models
 /**
@@ -5695,6 +5757,8 @@ const MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = new Map([
 
 const MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = new Map([
     ['unispeech-sat', ['UniSpeechSatForAudioFrameClassification', UniSpeechSatForAudioFrameClassification]],
+    ['wavlm', ['WavLMForAudioFrameClassification', WavLMForAudioFrameClassification]],
+    ['wav2vec2', ['Wav2Vec2ForAudioFrameClassification', Wav2Vec2ForAudioFrameClassification]],
 ]);
 
 const MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES = new Map([
@@ -5961,6 +6025,10 @@ export class AutoModelForXVector extends PretrainedMixin {
     static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES];
 }
 
+export class AutoModelForAudioFrameClassification extends PretrainedMixin {
+    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES];
+}
+
 export class AutoModelForDocumentQuestionAnswering extends PretrainedMixin {
     static MODEL_CLASS_MAPPINGS = [MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES];
 }