Add `ClapAudioModelWithProjection` and `ClapTextModelWithProjection`

src/models.js

@@ -4043,6 +4043,77 @@ export class FalconForCausalLM extends FalconPreTrainedModel { }
 export class ClapPreTrainedModel extends PreTrainedModel { }
 
 export class ClapModel extends ClapPreTrainedModel { }
+
+/**
+ * CLAP Text Model with a projection layer on top (a linear layer on top of the pooled output).
+ * 
+ * **Example:** Compute text embeddings with `ClapTextModelWithProjection`.
+ * 
+ * ```javascript
+ * import { AutoTokenizer, ClapTextModelWithProjection } from '@xenova/transformers';
+ * 
+ * // Load tokenizer and text model
+ * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clap-htsat-unfused');
+ * const text_model = await ClapTextModelWithProjection.from_pretrained('Xenova/clap-htsat-unfused');
+ * 
+ * // Run tokenization
+ * const texts = ['a sound of a cat', 'a sound of a dog'];
+ * const text_inputs = tokenizer(texts, { padding: true, truncation: true });
+ * 
+ * // Compute embeddings
+ * const { text_embeds } = await text_model(text_inputs);
+ * // Tensor {
+ * //   dims: [ 2, 512 ],
+ * //   type: 'float32',
+ * //   data: Float32Array(1024) [ ... ],
+ * //   size: 1024
+ * // }
+ * ```
+ */
+export class ClapTextModelWithProjection extends ClapPreTrainedModel {
+
+    /** @type {PreTrainedModel.from_pretrained} */
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
+        // Update default model file name if not provided
+        options.model_file_name ??= 'text_model';
+        return super.from_pretrained(pretrained_model_name_or_path, options);
+    }
+}
+
+/**
+ * CLAP Audio Model with a projection layer on top (a linear layer on top of the pooled output).
+ * 
+ * **Example:** Compute audio embeddings with `ClapAudioModelWithProjection`.
+ * 
+ * ```javascript
+ * import { AutoProcessor, ClapAudioModelWithProjection, read_audio } from '@xenova/transformers';
+ * 
+ * // Load processor and audio model
+ * const processor = await AutoProcessor.from_pretrained('Xenova/clap-htsat-unfused');
+ * const audio_model = await ClapAudioModelWithProjection.from_pretrained('Xenova/clap-htsat-unfused');
+ * 
+ * // Read audio and run processor
+ * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cat_meow.wav');
+ * const audio_inputs = await processor(audio);
+ * 
+ * // Compute embeddings
+ * const { audio_embeds } = await audio_model(audio_inputs);
+ * // Tensor {
+ * //   dims: [ 1, 512 ],
+ * //   type: 'float32',
+ * //   data: Float32Array(512) [ ... ],
+ * //   size: 512
+ * // }
+ * ```
+ */
+export class ClapAudioModelWithProjection extends ClapPreTrainedModel {
+    /** @type {PreTrainedModel.from_pretrained} */
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
+        // Update default model file name if not provided
+        options.model_file_name ??= 'audio_model';
+        return super.from_pretrained(pretrained_model_name_or_path, options);
+    }
+}
 //////////////////////////////////////////////////
 
 
@@ -4372,6 +4443,9 @@ for (const [mappings, type] of MODEL_CLASS_TYPE_MAPPING) {
 const CUSTOM_MAPPING = [
     ['CLIPTextModelWithProjection', CLIPTextModelWithProjection, MODEL_TYPES.EncoderOnly],
     ['CLIPVisionModelWithProjection', CLIPVisionModelWithProjection, MODEL_TYPES.EncoderOnly],
+    
+    ['ClapTextModelWithProjection', ClapTextModelWithProjection, MODEL_TYPES.EncoderOnly],
+    ['ClapAudioModelWithProjection', ClapAudioModelWithProjection, MODEL_TYPES.EncoderOnly],
 ]
 for (const [name, model, type] of CUSTOM_MAPPING) {
     MODEL_TYPE_MAPPING.set(name, type);