Add support for SNAC

README.md

@@ -407,6 +407,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
 1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (from Google AI) released with the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
 1. **[SmolVLM](https://huggingface.co/docs/transformers/main/model_doc/smolvlm) (from Hugging Face) released with the blog posts [SmolVLM - small yet mighty Vision Language Model](https://huggingface.co/blog/smolvlm) and [SmolVLM Grows Smaller – Introducing the 250M & 500M Models!](https://huggingface.co/blog/smolervlm) by the Hugging Face TB Research team.
+1. **SNAC** (from Papla Media, ETH Zurich) released with the paper [SNAC: Multi-Scale Neural Audio Codec](https://arxiv.org/abs/2410.14411) by Hubert Siuzdak, Florian Grötschla, Luca A. Lanzendörfer.
 1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.

docs/snippets/6_supported-models.snippet

@@ -121,6 +121,7 @@
 1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
 1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (from Google AI) released with the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
 1. **[SmolVLM](https://huggingface.co/docs/transformers/main/model_doc/smolvlm) (from Hugging Face) released with the blog posts [SmolVLM - small yet mighty Vision Language Model](https://huggingface.co/blog/smolvlm) and [SmolVLM Grows Smaller – Introducing the 250M & 500M Models!](https://huggingface.co/blog/smolervlm) by the Hugging Face TB Research team.
+1. **SNAC** (from Papla Media, ETH Zurich) released with the paper [SNAC: Multi-Scale Neural Audio Codec](https://arxiv.org/abs/2410.14411) by Hubert Siuzdak, Florian Grötschla, Luca A. Lanzendörfer.
 1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.

src/models.js

@@ -7287,6 +7287,60 @@ export class DacDecoderModel extends DacPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
+
+//////////////////////////////////////////////////
+// Snac models
+export class SnacPreTrainedModel extends PreTrainedModel {
+    main_input_name = 'input_values';
+    forward_params = ['input_values'];
+}
+
+/**
+ * The SNAC (Multi-Scale Neural Audio Codec) model.
+ */
+export class SnacModel extends SnacPreTrainedModel {
+    /**
+     * Encodes the input audio waveform into discrete codes.
+     * @param {Object} inputs Model inputs
+     * @param {Tensor} [inputs.input_values] Float values of the input audio waveform, of shape `(batch_size, channels, sequence_length)`).
+     * @returns {Promise<Record<string, Tensor>>} The output tensors of shape `(batch_size, num_codebooks, sequence_length)`.
+     */
+    async encode(inputs) {
+        return await sessionRun(this.sessions['encoder_model'], inputs);
+    }
+
+    /**
+     * Decodes the given frames into an output audio waveform.
+     * @param {Record<string, Tensor>} inputs The encoded audio codes.
+     * @returns {Promise<{audio_values: Tensor}>} The output tensor of shape `(batch_size, num_channels, sequence_length)`.
+     */
+    async decode(inputs) {
+        return await sessionRun(this.sessions['decoder_model'], inputs);
+    }
+}
+
+export class SnacEncoderModel extends SnacPreTrainedModel {
+    /** @type {typeof PreTrainedModel.from_pretrained} */
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
+        return super.from_pretrained(pretrained_model_name_or_path, {
+            ...options,
+            // Update default model file name if not provided
+            model_file_name: options.model_file_name ?? 'encoder_model',
+        });
+    }
+}
+export class SnacDecoderModel extends SnacPreTrainedModel {
+    /** @type {typeof PreTrainedModel.from_pretrained} */
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
+        return super.from_pretrained(pretrained_model_name_or_path, {
+            ...options,
+            // Update default model file name if not provided
+            model_file_name: options.model_file_name ?? 'decoder_model',
+        });
+    }
+}
+//////////////////////////////////////////////////
+
 //////////////////////////////////////////////////
 // AutoModels, used to simplify construction of PreTrainedModels
 // (uses config to instantiate correct class)
@@ -7468,6 +7522,7 @@ const MODEL_MAPPING_NAMES_ENCODER_DECODER = new Map([
 const MODEL_MAPPING_NAMES_AUTO_ENCODER = new Map([
     ['mimi', ['MimiModel', MimiModel]],
     ['dac', ['DacModel', DacModel]],
+    ['snac', ['SnacModel', SnacModel]],
 ]);
 
 const MODEL_MAPPING_NAMES_DECODER_ONLY = new Map([
@@ -7873,6 +7928,8 @@ const CUSTOM_MAPPING = [
     ['DacDecoderModel', DacDecoderModel, MODEL_TYPES.EncoderOnly],
     ['MimiEncoderModel', MimiEncoderModel, MODEL_TYPES.EncoderOnly],
     ['MimiDecoderModel', MimiDecoderModel, MODEL_TYPES.EncoderOnly],
+    ['SnacEncoderModel', SnacEncoderModel, MODEL_TYPES.EncoderOnly],
+    ['SnacDecoderModel', SnacDecoderModel, MODEL_TYPES.EncoderOnly],
 ]
 for (const [name, model, type] of CUSTOM_MAPPING) {
     MODEL_TYPE_MAPPING.set(name, type);

src/models/feature_extractors.js

@@ -6,6 +6,7 @@ export * from './dac/feature_extraction_dac.js';
 export * from './moonshine/feature_extraction_moonshine.js';
 export * from './pyannote/feature_extraction_pyannote.js';
 export * from './seamless_m4t/feature_extraction_seamless_m4t.js';
+export * from './snac/feature_extraction_snac.js';
 export * from './speecht5/feature_extraction_speecht5.js';
 export * from './wav2vec2/feature_extraction_wav2vec2.js';
 export * from './wespeaker/feature_extraction_wespeaker.js';

src/models/snac/feature_extraction_snac.js

@@ -0,0 +1,3 @@
+import { DacFeatureExtractor } from '../dac/feature_extraction_dac.js';
+
+export class SnacFeatureExtractor extends DacFeatureExtractor { }