Add support for Supertonic TTS

README.md

@@ -432,7 +432,8 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 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://huggingface.co/papers/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.
 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://huggingface.co/papers/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries.
-1. StyleTTS 2 (from Columbia University) released with the paper [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models](https://huggingface.co/papers/2306.07691) by Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, Nima Mesgarani.
+1. **StyleTTS 2** (from Columbia University) released with the paper [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models](https://huggingface.co/papers/2306.07691) by Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, Nima Mesgarani.
+1. **Supertonic** (from Supertone) released with the paper [Training Flow Matching Models with Reliable Labels via Self-Purification](https://huggingface.co/papers/2509.19091) by Hyeongju Kim, Yechan Yu, June Young Yi, Juheon Lee.
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://huggingface.co/papers/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
 1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://huggingface.co/papers/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
 1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://huggingface.co/papers/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.

docs/snippets/6_supported-models.snippet

@@ -146,7 +146,8 @@
 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://huggingface.co/papers/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.
 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://huggingface.co/papers/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries.
-1. StyleTTS 2 (from Columbia University) released with the paper [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models](https://huggingface.co/papers/2306.07691) by Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, Nima Mesgarani.
+1. **StyleTTS 2** (from Columbia University) released with the paper [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models](https://huggingface.co/papers/2306.07691) by Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, Nima Mesgarani.
+1. **Supertonic** (from Supertone) released with the paper [Training Flow Matching Models with Reliable Labels via Self-Purification](https://huggingface.co/papers/2509.19091) by Hyeongju Kim, Yechan Yu, June Young Yi, Juheon Lee.
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://huggingface.co/papers/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
 1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://huggingface.co/papers/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
 1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://huggingface.co/papers/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.

src/models.js

@@ -109,6 +109,7 @@ import {
     std_mean,
     Tensor,
     DataTypeMap,
+    randn,
 } from './utils/tensor.js';
 import { RawImage } from './utils/image.js';
 
@@ -136,6 +137,7 @@ const MODEL_TYPES = {
     AudioTextToText: 10,
     AutoEncoder: 11,
     ImageAudioTextToText: 12,
+    Supertonic: 13,
 }
 //////////////////////////////////////////////////
 
@@ -1262,6 +1264,14 @@ export class PreTrainedModel extends Callable {
                     decoder_model: 'decoder_model',
                 }, options),
             ]);
+        } else if (modelType === MODEL_TYPES.Supertonic) {
+            info = await Promise.all([
+                constructSessions(pretrained_model_name_or_path, {
+                    text_encoder: 'text_encoder',
+                    latent_denoiser: 'latent_denoiser',
+                    voice_decoder: 'voice_decoder',
+                }, options),
+            ]);
         } else { // should be MODEL_TYPES.EncoderOnly
             if (modelType !== MODEL_TYPES.EncoderOnly) {
                 const type = modelName ?? config?.model_type;
@@ -6861,6 +6871,61 @@ export class SpeechT5HifiGan extends PreTrainedModel {
 }
 //////////////////////////////////////////////////
 
+export class SupertonicPreTrainedModel extends PreTrainedModel { }
+export class SupertonicForConditionalGeneration extends SupertonicPreTrainedModel {
+
+    async generate_speech({
+        // Required inputs
+        input_ids,
+        attention_mask,
+        style,
+
+        // Optional inputs
+        num_inference_steps = 5,
+        speed = 1.05,
+    }) {
+        // @ts-expect-error TS2339
+        const { sampling_rate, chunk_compress_factor, base_chunk_size, latent_dim } = this.config;
+
+        // 1. Text Encoder
+        const { last_hidden_state, durations } = await sessionRun(this.sessions['text_encoder'], {
+            input_ids, attention_mask, style,
+        });
+        durations.div_(speed); // Apply speed factor to duration
+
+        // 2. Latent Denoiser
+        const wav_len_max = durations.max().item() * sampling_rate;
+        const chunk_size = base_chunk_size * chunk_compress_factor;
+        const latent_len = Math.floor((wav_len_max + chunk_size - 1) / chunk_size);
+        const batch_size = input_ids.dims[0];
+        const latent_mask = ones([batch_size, latent_len]);
+        const num_steps = full([batch_size], num_inference_steps);
+
+        let noisy_latents = randn([batch_size, latent_dim * chunk_compress_factor, latent_len]);
+        for (let step = 0; step < num_inference_steps; ++step) {
+            const timestep = full([batch_size], step);
+            ({ denoised_latents: noisy_latents } = await sessionRun(this.sessions['latent_denoiser'], {
+                style,
+                noisy_latents,
+                latent_mask,
+                encoder_outputs: last_hidden_state,
+                attention_mask,
+                timestep,
+                num_inference_steps: num_steps,
+            }));
+        }
+
+        // 3. Voice Decoder
+        const { waveform } = await sessionRun(this.sessions['voice_decoder'], {
+            latents: noisy_latents,
+        });
+        return {
+            waveform,
+            durations,
+        }
+    }
+}
+
 
 //////////////////////////////////////////////////
 // TrOCR models
@@ -8000,6 +8065,7 @@ const MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES = new Map([
 const MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES = new Map([
     ['vits', ['VitsModel', VitsModel]],
     ['musicgen', ['MusicgenForConditionalGeneration', MusicgenForConditionalGeneration]],
+    ['supertonic', ['SupertonicForConditionalGeneration', SupertonicForConditionalGeneration]],
 ]);
 
 const MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = new Map([
@@ -8386,6 +8452,7 @@ const CUSTOM_MAPPING = [
     ['SnacDecoderModel', SnacDecoderModel, MODEL_TYPES.EncoderOnly],
 
     ['Gemma3nForConditionalGeneration', Gemma3nForConditionalGeneration, MODEL_TYPES.ImageAudioTextToText],
+    ['SupertonicForConditionalGeneration', SupertonicForConditionalGeneration, MODEL_TYPES.Supertonic],
 ]
 for (const [name, model, type] of CUSTOM_MAPPING) {
     MODEL_TYPE_MAPPING.set(name, type);

src/pipelines.js

@@ -2792,6 +2792,9 @@ export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options:
  *
  * @typedef {Object} TextToAudioPipelineOptions Parameters specific to text-to-audio pipelines.
  * @property {Tensor|Float32Array|string|URL} [speaker_embeddings=null] The speaker embeddings (if the model requires it).
+ * @property {number} [num_inference_steps] The number of denoising steps (if the model supports it).
+ * More denoising steps usually lead to higher quality audio but slower inference.
+ * @property {number} [speed] The speed of the generated audio (if the model supports it).
  *
  * @callback TextToAudioPipelineCallback Generates speech/audio from the inputs.
  * @param {string|string[]} texts The text(s) to generate.
@@ -2805,31 +2808,24 @@ export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options:
  * Text-to-audio generation pipeline using any `AutoModelForTextToWaveform` or `AutoModelForTextToSpectrogram`.
  * This pipeline generates an audio file from an input text and optional other conditional inputs.
  *
- * **Example:** Generate audio from text with `Xenova/speecht5_tts`.
+ * **Example:** Generate audio from text with `onnx-community/Supertonic-TTS-ONNX`.
  * ```javascript
- * const synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { quantized: false });
- * const speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
- * const out = await synthesizer('Hello, my dog is cute', { speaker_embeddings });
+ * const synthesizer = await pipeline('text-to-speech', 'onnx-community/Supertonic-TTS-ONNX');
+ * const speaker_embeddings = 'https://huggingface.co/onnx-community/Supertonic-TTS-ONNX/resolve/main/voices/F1.bin';
+ * const output = await synthesizer('Hello there, how are you doing?', { speaker_embeddings });
  * // RawAudio {
- * //   audio: Float32Array(26112) [-0.00005657337896991521, 0.00020583874720614403, ...],
- * //   sampling_rate: 16000
+ * //   audio: Float32Array(95232) [-0.000482565927086398, -0.0004853440332226455, ...],
+ * //   sampling_rate: 44100
  * // }
- * ```
- *
- * You can then save the audio to a .wav file with the `wavefile` package:
- * ```javascript
- * import wavefile from 'wavefile';
- * import fs from 'fs';
- *
- * const wav = new wavefile.WaveFile();
- * wav.fromScratch(1, out.sampling_rate, '32f', out.audio);
- * fs.writeFileSync('out.wav', wav.toBuffer());
+ * 
+ * // Optional: Save the audio to a .wav file or Blob
+ * await output.save('output.wav'); // You can also use `output.toBlob()` to access the audio as a Blob
  * ```
  *
  * **Example:** Multilingual speech generation with `Xenova/mms-tts-fra`. See [here](https://huggingface.co/models?pipeline_tag=text-to-speech&other=vits&sort=trending) for the full list of available languages (1107).
  * ```javascript
  * const synthesizer = await pipeline('text-to-speech', 'Xenova/mms-tts-fra');
- * const out = await synthesizer('Bonjour');
+ * const output = await synthesizer('Bonjour');
  * // RawAudio {
  * //   audio: Float32Array(23808) [-0.00037693005288019776, 0.0003325853613205254, ...],
  * //   sampling_rate: 16000
@@ -2850,20 +2846,76 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
         this.vocoder = options.vocoder ?? null;
     }
 
+    async _prepare_speaker_embeddings(speaker_embeddings) {
+        // Load speaker embeddings as Float32Array from path/URL
+        if (typeof speaker_embeddings === 'string' || speaker_embeddings instanceof URL) {
+            // Load from URL with fetch
+            speaker_embeddings = new Float32Array(
+                await (await fetch(speaker_embeddings)).arrayBuffer()
+            );
+        }
+
+        if (speaker_embeddings instanceof Float32Array) {
+            speaker_embeddings = new Tensor(
+                'float32',
+                speaker_embeddings,
+                [speaker_embeddings.length]
+            )
+        } else if (!(speaker_embeddings instanceof Tensor)) {
+            throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.")
+        }
+
+        return speaker_embeddings;
+    }
 
     /** @type {TextToAudioPipelineCallback} */
     async _call(text_inputs, {
         speaker_embeddings = null,
+        num_inference_steps,
+        speed,
     } = {}) {
 
         // If this.processor is not set, we are using a `AutoModelForTextToWaveform` model
         if (this.processor) {
             return this._call_text_to_spectrogram(text_inputs, { speaker_embeddings });
+        } else if (
+            this.model.config.model_type === "supertonic"
+        ) {
+            return this._call_supertonic(text_inputs, { speaker_embeddings, num_inference_steps, speed });
         } else {
             return this._call_text_to_waveform(text_inputs);
         }
     }
 
+    async _call_supertonic(text_inputs, { speaker_embeddings, num_inference_steps, speed }) {
+        if (!speaker_embeddings) {
+            throw new Error("Speaker embeddings must be provided for Supertonic models.");
+        }
+        speaker_embeddings = await this._prepare_speaker_embeddings(speaker_embeddings);
+
+        // @ts-expect-error TS2339
+        const { sampling_rate, style_dim } = this.model.config;
+
+        speaker_embeddings = (/** @type {Tensor} */ (speaker_embeddings)).view(1, -1, style_dim);
+        const inputs = this.tokenizer(text_inputs, {
+            padding: true,
+            truncation: true,
+        });
+
+        // @ts-expect-error TS2339
+        const { waveform } = await this.model.generate_speech({
+            ...inputs,
+            style: speaker_embeddings,
+            num_inference_steps,
+            speed,
+        });
+
+        return new RawAudio(
+            waveform.data,
+            sampling_rate,
+        )
+    }
+
     async _call_text_to_waveform(text_inputs) {
 
         // Run tokenization
@@ -2891,32 +2943,16 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
             this.vocoder = await AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID, { dtype: 'fp32' });
         }
 
-        // Load speaker embeddings as Float32Array from path/URL
-        if (typeof speaker_embeddings === 'string' || speaker_embeddings instanceof URL) {
-            // Load from URL with fetch
-            speaker_embeddings = new Float32Array(
-                await (await fetch(speaker_embeddings)).arrayBuffer()
-            );
-        }
-
-        if (speaker_embeddings instanceof Float32Array) {
-            speaker_embeddings = new Tensor(
-                'float32',
-                speaker_embeddings,
-                [1, speaker_embeddings.length]
-            )
-        } else if (!(speaker_embeddings instanceof Tensor)) {
-            throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.")
-        }
-
         // Run tokenization
         const { input_ids } = this.tokenizer(text_inputs, {
             padding: true,
             truncation: true,
         });
 
-        // NOTE: At this point, we are guaranteed that `speaker_embeddings` is a `Tensor`
-        // @ts-ignore
+        speaker_embeddings = await this._prepare_speaker_embeddings(speaker_embeddings);
+        speaker_embeddings = speaker_embeddings.view(1, -1);
+
+        // @ts-expect-error TS2339
         const { waveform } = await this.model.generate_speech(input_ids, speaker_embeddings, { vocoder: this.vocoder });
 
         const sampling_rate = this.processor.feature_extractor.config.sampling_rate;