Duplicate bosonai/higgs-audio-v2-generation-3B-base model
Browse files- .gitattributes +8 -0
- LICENSE +43 -0
- README.md +202 -0
- config.json +240 -0
- emergent-tts-emotions-win-rate.png +3 -0
- generation_config.json +7 -0
- higgs_audio_tokenizer_architecture.png +3 -0
- higgs_audio_v2_architecture_combined.png +3 -0
- higgs_audio_v2_open_source_delay_pattern.png +0 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +404 -0
- open_source_repo_demo.mp4 +3 -0
- special_tokens_map.json +16 -0
- tokenizer.json +3 -0
- tokenizer_config.json +2062 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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open_source_repo_demo.mp4 filter=lfs diff=lfs merge=lfs -text
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LICENSE
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BOSON HIGGS AUDIO 2 COMMUNITY LICENSE AGREEMENT
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Boson Higgs Audio 2 Version Release Date: June 20, 2025
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This License Agreement (the “Agreement”) is entered into by and between Licensee (as defined below) and Boson AI USA, Inc. (“Boson”) and is based upon the Meta Llama 3 Community License Agreement as of April 18, 2024 (the “Meta License Agreement”), which can be found at https://llama.meta.com/llama3/license/. The terms and conditions of the Meta License Agreement are hereby incorporated herein by reference and Unless stated otherwise below, its terms apply. The Higgs Audio 2 model developed by Boson AI USA, Inc. (“Higgs Materials”) is an audio model derived from Meta Llama 3 software and algorithms.
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“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Higgs Materials set forth herein and the Meta License Agreement.
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“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering into this Agreement on their behalf.
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“Higgs Audio 2” means the foundational large audio language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing developed by Boson AI distributed at https://github.com/boson-ai/boson-multimodal or otherwise.
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“Higgs Materials” means, collectively, Boson’s proprietary modification of Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.
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“Boson” or “we” means Boson AI USA, Inc.
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By clicking “I Accept” below or by using or distributing any portion or element of the Higgs Materials, you agree to be bound by this Agreement.
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1. License Rights and Redistribution.
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a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Boson’s intellectual property or other rights owned by Boson embodied in the Higgs Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Higgs Materials.
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b. Redistribution and Use.
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i. If you distribute or make available the Higgs Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement and the of Meta License ’s Llama 3 agreement with any such Higgs Materials; and (B) prominently display “Built with Higgs Materials licensed from Boson AI USA, Inc., Copyright Boson AI USA, Inc., All Rights Reserved and Meta Llama 3 licensed under the Meta Llama 3 Community License, Copyright Meta Platforms, Inc., All Right Reserved". based on Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Higgs Materials to create, modify, enhance, train, fine tune, or otherwise improve an AI model or similar software, which is distributed or made available, you shall also include “Higgs Audio 2” at the beginning of any such AI model or software name.
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ii. Even if you receive Higgs Materials, or any modifications, enhancements or derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will apply to you.
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iii. You must retain in all copies of the Llama Materials that you distribute and as set forth above, include the following attribution notice within a “Notice” text file distributed as a part of such copies:
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“Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
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“Boson Higgs Audio 2 is licensed under the Boson Community License, Copyright © Boson AI USA, Inc. All Rights Reserved.”
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iv. Your use of the Higgs Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.
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v. You will not use the Higgs Materials or any output or results of the Higgs Materials to improve any other large language model (excluding Boson Higgs Audio 2 or derivative works thereof).
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vi. You hereby acknowledge that Boson is the owner of the Higgs Materials and under no circumstance shall you bring any legal action, claim, charge, demand challenging such ownership rights of Boson.
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2. Additional Commercial Terms. If the annual active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 100,000 annual active users in the preceding calendar year, you must request an expanded license from Boson AI, which Boson AI may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Boson AI otherwise expressly grants you such rights.
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3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE Higgs Materials AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITH ALL FAULTS, WITHOUT WARRANTIES OF ANY KIND EXPRESS, IMPLIED, BASED UPON CUSTOM AND USAGE OR COURSE OF DEALING, AND BOSON AI DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE HIGGS MATERIALS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR USE OF THE HIGGS MATERIALS AND ANY OUTPUT AND RESULTS.
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4. Limitation of Liability. IN NO EVENT WILL BOSON AI OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF BOSON, META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
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5. Intellectual Property.
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a. No trademark licenses are granted under this Agreement, or in connection with the Higgs Materials., nNeither Boson nor Licensee may use any name or mark owned by, or associated with, the other party hereto or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Higgs Materials or as set forth in this Section 5(a). Boson hereby grants you a license to use “Higgs Audio 2” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. All goodwill arising out of your use of the Mark will inure to the benefit of Meta and Boson AI.
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b. Subject to Boson’s ownership of the Higgs Materials and derivatives made by or for Boson AI, with respect to any derivative works and modifications of the Higgs Materials that are made by you, as between you and Boson AI, you are and will be the owner of such derivative works and modifications.
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c. If you institute litigation or other proceedings against Boson AI, Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Higgs Materials or Boson Higgs Audio 2 outputs or results, or any portion thereof any of the foregoing, constitutes infringement of the intellectual property or other rights owned or licensable by you, then any licenses granted to you hereunder this Agreement shall immediately terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Boson AI from and against any claim, charge, demand, cause of action by any third party arising out of or related to your use or distribution of the Higgs Materials.
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6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Higgs Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Boson AI may terminate this Agreement if you are in breach of any term or condition of this Agreement by providing you with written notice. Upon your receipt of written notice of termination of this Agreement, you shall delete the Higgs Materials from any computer, server or IT device and cease use of the Higgs Materials in all respects. Sections 1(b)(vi), 3, 4 and 7 shall survive the termination of this Agreement.
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7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The federal courts in the Northern District of California and the state courts in Santa Clara County, California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
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README.md
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---
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license: other
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language:
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- en
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- zh
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- de
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- ko
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pipeline_tag: text-to-speech
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---
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# Higgs Audio V2: Redefining Expressiveness in Audio Generation
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<div align="center" style="display: flex; justify-content: center; margin-top: 10px; flex-wrap: wrap; gap: 8px;">
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<a href="https://boson.ai/blog/higgs-audio-v2"><img src='https://img.shields.io/badge/🚀-Launch Blogpost-228B22' style="margin-right: 5px;"></a>
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<a href="https://github.com/boson-ai/higgs-audio"><img src="https://img.shields.io/badge/💻-Github%20Repo-9C276A" style="margin-right: 5px;"></a>
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<a href="https://huggingface.co/spaces/smola/higgs_audio_v2"><img src="https://img.shields.io/badge/🎮-HF%20Space%20Playground-8A2BE2" style="margin-right: 5px;"></a>
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<a href="https://huggingface.co/bosonai/higgs-audio-v2-tokenizer"><img src="https://img.shields.io/badge/🎧-Audio%20Tokenizer-6A5ACD.svg" style="margin-right: 5px;"></a>
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</div>
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Check our open-source repository https://github.com/boson-ai/higgs-audio for more details!
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We are open-sourcing Higgs Audio v2, a powerful audio foundation model pretrained on over 10 million hours of audio data and a diverse set of text data.
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Despite having no post-training or fine-tuning, Higgs Audio v2 excels in expressive audio generation, thanks to its deep language and acoustic understanding.
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On [EmergentTTS-Eval](https://github.com/boson-ai/emergenttts-eval-public), the model achieves win rates of **75.7%** and **55.7%** over "gpt-4o-mini-tts" on the "Emotions" and "Questions" categories, respectively. It also obtains state-of-the-art performance on traditional TTS benchmarks like Seed-TTS Eval and Emotional Speech Dataset (ESD). Moreover, the model demonstrates capabilities rarely seen in previous systems, including automatic prosody adaptation during narration, zero-shot generation of natural multi-speaker dialogues in multiple languages, melodic humming with the cloned voice, and simultaneous generation of speech and background music.
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<p>
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<img src="./emergent-tts-emotions-win-rate.png" width=900>
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</p>
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Here's the demo video that shows some of its emergent capabilities (remember to unmute):
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<div align="left">
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<video width="95%" controls>
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<source src="https://cdn-uploads.huggingface.co/production/uploads/64fa072a52e82dd432460767/bjbWGg1IKoMtWXnl0Od8G.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</div>
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Here's another demo video that show-cases the model's multilingual capability and how it enabled live translation (remember to unmute):
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<div align="left">
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<video width="95%" controls>
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<source src="https://cdn-uploads.huggingface.co/production/uploads/64fa072a52e82dd432460767/9cN-ky02GzmUgogsIh1Wg.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</div>
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## Technical Details
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<p>
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<img src="./higgs_audio_v2_architecture_combined.png" width=900>
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</p>
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Higgs Audio v2 adopts the "generation variant" depicted in the architecture figure above. Its strong performance is driven by three key technical innovations:
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- We developed an automated annotation pipeline that leverages multiple ASR models, sound event classification models, and our in-house audio understanding model. Using this pipeline, we cleaned and annotated 10 million hours audio data, which we refer to as AudioVerse. The in-house understanding model is finetuned on top of Higgs Audio v1 Understanding, which adopts the "understanding variant" shown in the architecture figure.
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- We trained a unified audio tokenizer from scratch that captures both semantic and acoustic features.
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- We proposed the DualFFN architecture, which enhances the LLM’s ability to model acoustics tokens with minimal computational overhead.
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### Audio Tokenizer
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<p>
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<img src="./higgs_audio_tokenizer_architecture.png" width=900>
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</p>
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We introduce a new discretized audio tokenizer that runs at just 25 frames per second while keeping—or even improving—audio quality compared to tokenizers with twice the bitrate.
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Our model is the first to train on 24 kHz data covering speech, music, and sound events in one unified system.
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It also uses a simple non-diffusion encoder/decoder for fast, batch inference. It achieves state-of-the-art performance in semantic and acoustic evaluations.
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Check https://huggingface.co/bosonai/higgs-audio-v2-tokenizer for more information about the tokenizer.
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### Model Architecture -- Dual FFN
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Higgs Audio v2 is built on top of [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B). To enhance the model’s ability to process audio tokens,
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we incorporate the "DualFFN" architecture as an audio adapter.
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DualFFN acts as an audio-specific expert, boosting the LLM's performance with minimal computational overhead.
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Our implementation preserves 91% of the original LLM’s training speed with the inclusion of DualFFN, which has 2.2B parameters.
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Thus, the total number of parameter for Higgs Audio v2 is 3.6B (LLM) + 2.2B (Audio Dual FFN), and it has the same training / inference FLOPs as Llama-3.2-3B.
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Ablation study shows that the model equipped with DualFFN consistently outperforms its counterpart in terms of word error rate (WER) and speaker similarity.
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See [our architecture blog](https://github.com/boson-ai/higgs-audio/tech_blogs/ARCHITECTURE_BLOG.md) for more information.
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+
|
| 84 |
+
|
| 85 |
+
## Evaluation
|
| 86 |
+
|
| 87 |
+
Here's the performance of Higgs Audio v2 on four benchmarks, [Seed-TTS Eval](https://github.com/BytedanceSpeech/seed-tts-eval), [Emotional Speech Dataset (ESD)](https://paperswithcode.com/dataset/esd), [EmergentTTS-Eval](https://arxiv.org/abs/2505.23009), and Multi-speaker Eval:
|
| 88 |
+
|
| 89 |
+
#### Seed-TTS Eval & ESD
|
| 90 |
+
|
| 91 |
+
We prompt Higgs Audio v2 with the reference text, reference audio, and target text for zero-shot TTS. We use the standard evaluation metrics from Seed-TTS Eval and ESD.
|
| 92 |
+
|
| 93 |
+
| | SeedTTS-Eval| | ESD | |
|
| 94 |
+
|------------------------------|--------|--------|---------|-------------------|
|
| 95 |
+
| | WER ↓ | SIM ↑ | WER ↓ | SIM (emo2vec) ↑ |
|
| 96 |
+
| Cosyvoice2 | 2.28 | 65.49 | 2.71 | 80.48 |
|
| 97 |
+
| Qwen2.5-omni† | 2.33 | 64.10 | - | - |
|
| 98 |
+
| ElevenLabs Multilingual V2 | **1.43** | 50.00 | 1.66 | 65.87 |
|
| 99 |
+
| Higgs Audio v1 | 2.18 | 66.27 | **1.49** | 82.84 |
|
| 100 |
+
| Higgs Audio v2 (base) | 2.44 | **67.70** | 1.78 | **86.13** |
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
#### EmergentTTS-Eval ("Emotions" and "Questions")
|
| 104 |
+
|
| 105 |
+
Following the [EmergentTTS-Eval Paper](https://arxiv.org/abs/2505.23009), we report the win-rate over "gpt-4o-mini-tts" with the "alloy" voice. Results of Higgs Audio v2 is obtained with the voice of "belinda". The judge model is Gemini 2.5 Pro.
|
| 106 |
+
|
| 107 |
+
| Model | Emotions (%) ↑ | Questions (%) ↑ |
|
| 108 |
+
|------------------------------------|--------------|----------------|
|
| 109 |
+
| Higgs Audio v2 (base) | **75.71%** | **55.71%** |
|
| 110 |
+
| [gpt-4o-audio-preview†](https://platform.openai.com/docs/models/gpt-4o-audio-preview) | 61.64% | 47.85% |
|
| 111 |
+
| [Hume.AI](https://www.hume.ai/research) | 61.60% | 43.21% |
|
| 112 |
+
| **BASELINE:** [gpt-4o-mini-tts](https://platform.openai.com/docs/models/gpt-4o-mini-tts) | 50.00% | 50.00% |
|
| 113 |
+
| [Qwen 2.5 Omni†](https://github.com/QwenLM/Qwen2.5-Omni) | 41.60% | 51.78% |
|
| 114 |
+
| [minimax/speech-02-hd](https://replicate.com/minimax/speech-02-hd) | 40.86% | 47.32% |
|
| 115 |
+
| [ElevenLabs Multilingual v2](https://elevenlabs.io/blog/eleven-multilingual-v2) | 30.35% | 39.46% |
|
| 116 |
+
| [DeepGram Aura-2](https://deepgram.com/learn/introducing-aura-2-enterprise-text-to-speech) | 29.28% | 48.21% |
|
| 117 |
+
| [Sesame csm-1B](https://github.com/SesameAILabs/csm) | 15.96% | 31.78% |
|
| 118 |
+
|
| 119 |
+
<sup><sub>'†' means using the strong-prompting method described in the paper.</sub></sup>
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
#### Multi-speaker Eval
|
| 123 |
+
|
| 124 |
+
We also designed a multi-speaker evaluation benchmark to evaluate the capability of Higgs Audio v2 for multi-speaker dialog generation. The benchmark contains three subsets
|
| 125 |
+
|
| 126 |
+
- `two-speaker-conversation`: 1000 synthetic dialogues involving two speakers. We fix two reference audio clips to evaluate the model's ability in double voice cloning for utterances ranging from 4 to 10 dialogues between two randomly chosen persona.
|
| 127 |
+
- `small talk (no ref)`: 250 synthetic dialogues curated in the same way as above, but are characterized by short utterances and a limited number of turns (4–6), we do not fix reference audios in this case and this set is designed to evaluate the model's ability to automatically assign appropriate voices to speakers.
|
| 128 |
+
- `small talk (ref)`: 250 synthetic dialogues similar to above, but contains even shorter utterances as this set is meant to include reference clips in it's context, similar to `two-speaker-conversation`.
|
| 129 |
+
|
| 130 |
+
|
| 131 |
+
We report the word-error-rate (WER) and the geometric mean between intra-speaker similarity and inter-speaker dis-similarity on these three subsets. Other than Higgs Audio v2, we also evaluated [MoonCast](https://github.com/jzq2000/MoonCast) and [nari-labs/Dia-1.6B-0626](https://huggingface.co/nari-labs/Dia-1.6B-0626), two of the most popular open-source models capable of multi-speaker dialog generation.
|
| 132 |
+
Results are summarized in the following table. We are not able to run [nari-labs/Dia-1.6B-0626](https://huggingface.co/nari-labs/Dia-1.6B-0626) on our "two-speaker-conversation" subset due to its strict limitation on the length of the utterances and output audio.
|
| 133 |
+
|
| 134 |
+
| | two-speaker-conversation | |small talk | | small talk (no ref) | |
|
| 135 |
+
| ---------------------------------------------- | -------------- | ------------------ | ---------- | -------------- | ------------------- | -------------- |
|
| 136 |
+
| | WER ↓ | Mean Sim & Dis-sim ↑ | WER ↓ | Mean Sim & Dis-sim ↑ | WER ↓ | Mean Sim & Dis-sim ↑ |
|
| 137 |
+
| [MoonCast](https://github.com/jzq2000/MoonCast) | 38.77 | 46.02 | **8.33** | 63.68 | 24.65 | 53.94 |
|
| 138 |
+
| [nari-labs/Dia-1.6B-0626](https://huggingface.co/nari-labs/Dia-1.6B-0626) | \- | \- | 17.62 | 63.15 | 19.46 | **61.14** |
|
| 139 |
+
| Higgs Audio v2 (base) | **18.88** | **51.95** | 11.89 | **67.92** | **14.65** | 55.28 |
|
| 140 |
+
|
| 141 |
+
|
| 142 |
+
## Get Started
|
| 143 |
+
|
| 144 |
+
You need to first install the [higgs-audio](https://github.com/boson-ai/higgs-audio):
|
| 145 |
+
|
| 146 |
+
```bash
|
| 147 |
+
git clone https://github.com/boson-ai/higgs-audio.git
|
| 148 |
+
|
| 149 |
+
cd higgs-audio
|
| 150 |
+
python3 -m venv higgs_audio_env
|
| 151 |
+
source higgs_audio_env/bin/activate
|
| 152 |
+
pip install -r requirements.txt
|
| 153 |
+
pip install -e .
|
| 154 |
+
```
|
| 155 |
+
|
| 156 |
+
Afterwards, try to run the following python code snippet to convert text to speech.
|
| 157 |
+
|
| 158 |
+
```python
|
| 159 |
+
from boson_multimodal.serve.serve_engine import HiggsAudioServeEngine, HiggsAudioResponse
|
| 160 |
+
from boson_multimodal.data_types import ChatMLSample, Message, AudioContent
|
| 161 |
+
|
| 162 |
+
import torch
|
| 163 |
+
import torchaudio
|
| 164 |
+
import time
|
| 165 |
+
import click
|
| 166 |
+
|
| 167 |
+
MODEL_PATH = "bosonai/higgs-audio-v2-generation-3B-base"
|
| 168 |
+
AUDIO_TOKENIZER_PATH = "bosonai/higgs-audio-v2-tokenizer"
|
| 169 |
+
|
| 170 |
+
system_prompt = (
|
| 171 |
+
"Generate audio following instruction.\n\n<|scene_desc_start|>\nAudio is recorded from a quiet room.\n<|scene_desc_end|>"
|
| 172 |
+
)
|
| 173 |
+
|
| 174 |
+
messages = [
|
| 175 |
+
Message(
|
| 176 |
+
role="system",
|
| 177 |
+
content=system_prompt,
|
| 178 |
+
),
|
| 179 |
+
Message(
|
| 180 |
+
role="user",
|
| 181 |
+
content="The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.",
|
| 182 |
+
),
|
| 183 |
+
]
|
| 184 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 185 |
+
|
| 186 |
+
serve_engine = HiggsAudioServeEngine(MODEL_PATH, AUDIO_TOKENIZER_PATH, device=device)
|
| 187 |
+
|
| 188 |
+
output: HiggsAudioResponse = serve_engine.generate(
|
| 189 |
+
chat_ml_sample=ChatMLSample(messages=messages),
|
| 190 |
+
max_new_tokens=1024,
|
| 191 |
+
temperature=0.3,
|
| 192 |
+
top_p=0.95,
|
| 193 |
+
top_k=50,
|
| 194 |
+
stop_strings=["<|end_of_text|>", "<|eot_id|>"],
|
| 195 |
+
)
|
| 196 |
+
torchaudio.save(f"output.wav", torch.from_numpy(output.audio)[None, :], output.sampling_rate)
|
| 197 |
+
```
|
| 198 |
+
|
| 199 |
+
You can also check https://github.com/boson-ai/higgs-audio/tree/main/examples for more example scripts.
|
| 200 |
+
## License
|
| 201 |
+
|
| 202 |
+
See [LICENSE](./LICENSE)
|
config.json
ADDED
|
@@ -0,0 +1,240 @@
|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
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|
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|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "model_initialization/higgs_audio_Llama-3.2-3B_dual_ffn_0_1_code8_size1024_rev",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"HiggsAudioModel"
|
| 5 |
+
],
|
| 6 |
+
"audio_adapter_type": "dual_ffn_fast_forward",
|
| 7 |
+
"audio_bos_token": "<|audio_bos|>",
|
| 8 |
+
"audio_codebook_size": 1024,
|
| 9 |
+
"audio_decoder_proj_num_layers": 0,
|
| 10 |
+
"audio_dual_ffn_layers": [
|
| 11 |
+
0,
|
| 12 |
+
1,
|
| 13 |
+
2,
|
| 14 |
+
3,
|
| 15 |
+
4,
|
| 16 |
+
5,
|
| 17 |
+
6,
|
| 18 |
+
7,
|
| 19 |
+
8,
|
| 20 |
+
9,
|
| 21 |
+
10,
|
| 22 |
+
11,
|
| 23 |
+
12,
|
| 24 |
+
13,
|
| 25 |
+
14,
|
| 26 |
+
15,
|
| 27 |
+
16,
|
| 28 |
+
17,
|
| 29 |
+
18,
|
| 30 |
+
19,
|
| 31 |
+
20,
|
| 32 |
+
21,
|
| 33 |
+
22,
|
| 34 |
+
23,
|
| 35 |
+
24,
|
| 36 |
+
25,
|
| 37 |
+
26,
|
| 38 |
+
27
|
| 39 |
+
],
|
| 40 |
+
"audio_embed_avg": false,
|
| 41 |
+
"audio_encoder_config": {
|
| 42 |
+
"_attn_implementation_autoset": false,
|
| 43 |
+
"_name_or_path": "",
|
| 44 |
+
"activation_dropout": 0.0,
|
| 45 |
+
"activation_function": "gelu",
|
| 46 |
+
"add_cross_attention": false,
|
| 47 |
+
"architectures": null,
|
| 48 |
+
"attention_dropout": 0.0,
|
| 49 |
+
"bad_words_ids": null,
|
| 50 |
+
"begin_suppress_tokens": null,
|
| 51 |
+
"bos_token_id": null,
|
| 52 |
+
"chunk_size_feed_forward": 0,
|
| 53 |
+
"cross_attention_hidden_size": null,
|
| 54 |
+
"d_model": 1280,
|
| 55 |
+
"decoder_start_token_id": null,
|
| 56 |
+
"diversity_penalty": 0.0,
|
| 57 |
+
"do_sample": false,
|
| 58 |
+
"dropout": 0.0,
|
| 59 |
+
"early_stopping": false,
|
| 60 |
+
"encoder_attention_heads": 20,
|
| 61 |
+
"encoder_ffn_dim": 5120,
|
| 62 |
+
"encoder_layerdrop": 0.0,
|
| 63 |
+
"encoder_layers": 32,
|
| 64 |
+
"encoder_no_repeat_ngram_size": 0,
|
| 65 |
+
"eos_token_id": null,
|
| 66 |
+
"exponential_decay_length_penalty": null,
|
| 67 |
+
"finetuning_task": null,
|
| 68 |
+
"forced_bos_token_id": null,
|
| 69 |
+
"forced_eos_token_id": null,
|
| 70 |
+
"id2label": {
|
| 71 |
+
"0": "LABEL_0",
|
| 72 |
+
"1": "LABEL_1"
|
| 73 |
+
},
|
| 74 |
+
"init_std": 0.02,
|
| 75 |
+
"is_decoder": false,
|
| 76 |
+
"is_encoder_decoder": false,
|
| 77 |
+
"label2id": {
|
| 78 |
+
"LABEL_0": 0,
|
| 79 |
+
"LABEL_1": 1
|
| 80 |
+
},
|
| 81 |
+
"length_penalty": 1.0,
|
| 82 |
+
"max_length": 20,
|
| 83 |
+
"max_source_positions": 1500,
|
| 84 |
+
"min_length": 0,
|
| 85 |
+
"model_type": "higgs_audio_encoder",
|
| 86 |
+
"no_repeat_ngram_size": 0,
|
| 87 |
+
"num_beam_groups": 1,
|
| 88 |
+
"num_beams": 1,
|
| 89 |
+
"num_hidden_layers": 32,
|
| 90 |
+
"num_mel_bins": 128,
|
| 91 |
+
"num_return_sequences": 1,
|
| 92 |
+
"output_attentions": false,
|
| 93 |
+
"output_hidden_states": false,
|
| 94 |
+
"output_scores": false,
|
| 95 |
+
"pad_token_id": 128001,
|
| 96 |
+
"prefix": null,
|
| 97 |
+
"problem_type": null,
|
| 98 |
+
"pruned_heads": {},
|
| 99 |
+
"remove_invalid_values": false,
|
| 100 |
+
"repetition_penalty": 1.0,
|
| 101 |
+
"return_dict": true,
|
| 102 |
+
"return_dict_in_generate": false,
|
| 103 |
+
"scale_embedding": false,
|
| 104 |
+
"sep_token_id": null,
|
| 105 |
+
"suppress_tokens": null,
|
| 106 |
+
"task_specific_params": null,
|
| 107 |
+
"temperature": 1.0,
|
| 108 |
+
"tf_legacy_loss": false,
|
| 109 |
+
"tie_encoder_decoder": false,
|
| 110 |
+
"tie_word_embeddings": true,
|
| 111 |
+
"tokenizer_class": null,
|
| 112 |
+
"top_k": 50,
|
| 113 |
+
"top_p": 1.0,
|
| 114 |
+
"torch_dtype": null,
|
| 115 |
+
"torchscript": false,
|
| 116 |
+
"typical_p": 1.0,
|
| 117 |
+
"use_bfloat16": false
|
| 118 |
+
},
|
| 119 |
+
"audio_eos_token": "<|audio_eos|>",
|
| 120 |
+
"audio_eos_token_id": 128012,
|
| 121 |
+
"audio_ffn_hidden_size": 3072,
|
| 122 |
+
"audio_ffn_intermediate_size": 8192,
|
| 123 |
+
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"layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
| 399 |
+
"layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
| 400 |
+
"layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
| 401 |
+
"layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
| 402 |
+
"norm.weight": "model-00003-of-00003.safetensors"
|
| 403 |
+
}
|
| 404 |
+
}
|
open_source_repo_demo.mp4
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6dd765d355fffb62861e627373857c01d15fbf95ddaf7a6f5e7dff1d933ceb14
|
| 3 |
+
size 13975450
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|begin_of_text|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|end_of_text|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
}
|
| 16 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1a222563314bf6ffe3471622bff017ff5bb0630f2924faf44216195ebfef2af3
|
| 3 |
+
size 17209675
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2062 @@
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|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"128000": {
|
| 4 |
+
"content": "<|begin_of_text|>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128001": {
|
| 12 |
+
"content": "<|end_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"128002": {
|
| 20 |
+
"content": "<|reserved_special_token_0|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"128003": {
|
| 28 |
+
"content": "<|reserved_special_token_1|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"128004": {
|
| 36 |
+
"content": "<|finetune_right_pad_id|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"128005": {
|
| 44 |
+
"content": "<|reserved_special_token_2|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"128006": {
|
| 52 |
+
"content": "<|start_header_id|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"128007": {
|
| 60 |
+
"content": "<|end_header_id|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"128008": {
|
| 68 |
+
"content": "<|eom_id|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"128009": {
|
| 76 |
+
"content": "<|eot_id|>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"128010": {
|
| 84 |
+
"content": "<|python_tag|>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"128011": {
|
| 92 |
+
"content": "<|audio_bos|>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": true
|
| 98 |
+
},
|
| 99 |
+
"128012": {
|
| 100 |
+
"content": "<|audio_eos|>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": true
|
| 106 |
+
},
|
| 107 |
+
"128013": {
|
| 108 |
+
"content": "<|audio_out_bos|>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": true
|
| 114 |
+
},
|
| 115 |
+
"128014": {
|
| 116 |
+
"content": "<|reserved_special_token_6|>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
|
| 120 |
+
"single_word": false,
|
| 121 |
+
"special": true
|
| 122 |
+
},
|
| 123 |
+
"128015": {
|
| 124 |
+
"content": "<|AUDIO|>",
|
| 125 |
+
"lstrip": false,
|
| 126 |
+
"normalized": false,
|
| 127 |
+
"rstrip": false,
|
| 128 |
+
"single_word": false,
|
| 129 |
+
"special": true
|
| 130 |
+
},
|
| 131 |
+
"128016": {
|
| 132 |
+
"content": "<|AUDIO_OUT|>",
|
| 133 |
+
"lstrip": false,
|
| 134 |
+
"normalized": false,
|
| 135 |
+
"rstrip": false,
|
| 136 |
+
"single_word": false,
|
| 137 |
+
"special": true
|
| 138 |
+
},
|
| 139 |
+
"128017": {
|
| 140 |
+
"content": "<|recipient|>",
|
| 141 |
+
"lstrip": false,
|
| 142 |
+
"normalized": false,
|
| 143 |
+
"rstrip": false,
|
| 144 |
+
"single_word": false,
|
| 145 |
+
"special": true
|
| 146 |
+
},
|
| 147 |
+
"128018": {
|
| 148 |
+
"content": "<|scene_desc_start|>",
|
| 149 |
+
"lstrip": false,
|
| 150 |
+
"normalized": false,
|
| 151 |
+
"rstrip": false,
|
| 152 |
+
"single_word": false,
|
| 153 |
+
"special": true
|
| 154 |
+
},
|
| 155 |
+
"128019": {
|
| 156 |
+
"content": "<|scene_desc_end|>",
|
| 157 |
+
"lstrip": false,
|
| 158 |
+
"normalized": false,
|
| 159 |
+
"rstrip": false,
|
| 160 |
+
"single_word": false,
|
| 161 |
+
"special": true
|
| 162 |
+
},
|
| 163 |
+
"128020": {
|
| 164 |
+
"content": "<|generation_instruction_start|>",
|
| 165 |
+
"lstrip": false,
|
| 166 |
+
"normalized": false,
|
| 167 |
+
"rstrip": false,
|
| 168 |
+
"single_word": false,
|
| 169 |
+
"special": true
|
| 170 |
+
},
|
| 171 |
+
"128021": {
|
| 172 |
+
"content": "<|generation_instruction_end|>",
|
| 173 |
+
"lstrip": false,
|
| 174 |
+
"normalized": false,
|
| 175 |
+
"rstrip": false,
|
| 176 |
+
"single_word": false,
|
| 177 |
+
"special": true
|
| 178 |
+
},
|
| 179 |
+
"128022": {
|
| 180 |
+
"content": "<SE>",
|
| 181 |
+
"lstrip": false,
|
| 182 |
+
"normalized": false,
|
| 183 |
+
"rstrip": false,
|
| 184 |
+
"single_word": false,
|
| 185 |
+
"special": true
|
| 186 |
+
},
|
| 187 |
+
"128023": {
|
| 188 |
+
"content": "</SE>",
|
| 189 |
+
"lstrip": false,
|
| 190 |
+
"normalized": false,
|
| 191 |
+
"rstrip": false,
|
| 192 |
+
"single_word": false,
|
| 193 |
+
"special": true
|
| 194 |
+
},
|
| 195 |
+
"128024": {
|
| 196 |
+
"content": "<SE_s>",
|
| 197 |
+
"lstrip": false,
|
| 198 |
+
"normalized": false,
|
| 199 |
+
"rstrip": false,
|
| 200 |
+
"single_word": false,
|
| 201 |
+
"special": true
|
| 202 |
+
},
|
| 203 |
+
"128025": {
|
| 204 |
+
"content": "</SE_s>",
|
| 205 |
+
"lstrip": false,
|
| 206 |
+
"normalized": false,
|
| 207 |
+
"rstrip": false,
|
| 208 |
+
"single_word": false,
|
| 209 |
+
"special": true
|
| 210 |
+
},
|
| 211 |
+
"128026": {
|
| 212 |
+
"content": "<SE_e>",
|
| 213 |
+
"lstrip": false,
|
| 214 |
+
"normalized": false,
|
| 215 |
+
"rstrip": false,
|
| 216 |
+
"single_word": false,
|
| 217 |
+
"special": true
|
| 218 |
+
},
|
| 219 |
+
"128027": {
|
| 220 |
+
"content": "</SE_e>",
|
| 221 |
+
"lstrip": false,
|
| 222 |
+
"normalized": false,
|
| 223 |
+
"rstrip": false,
|
| 224 |
+
"single_word": false,
|
| 225 |
+
"special": true
|
| 226 |
+
},
|
| 227 |
+
"128028": {
|
| 228 |
+
"content": "<|reserved_special_token_20|>",
|
| 229 |
+
"lstrip": false,
|
| 230 |
+
"normalized": false,
|
| 231 |
+
"rstrip": false,
|
| 232 |
+
"single_word": false,
|
| 233 |
+
"special": true
|
| 234 |
+
},
|
| 235 |
+
"128029": {
|
| 236 |
+
"content": "<|reserved_special_token_21|>",
|
| 237 |
+
"lstrip": false,
|
| 238 |
+
"normalized": false,
|
| 239 |
+
"rstrip": false,
|
| 240 |
+
"single_word": false,
|
| 241 |
+
"special": true
|
| 242 |
+
},
|
| 243 |
+
"128030": {
|
| 244 |
+
"content": "<|reserved_special_token_22|>",
|
| 245 |
+
"lstrip": false,
|
| 246 |
+
"normalized": false,
|
| 247 |
+
"rstrip": false,
|
| 248 |
+
"single_word": false,
|
| 249 |
+
"special": true
|
| 250 |
+
},
|
| 251 |
+
"128031": {
|
| 252 |
+
"content": "<|reserved_special_token_23|>",
|
| 253 |
+
"lstrip": false,
|
| 254 |
+
"normalized": false,
|
| 255 |
+
"rstrip": false,
|
| 256 |
+
"single_word": false,
|
| 257 |
+
"special": true
|
| 258 |
+
},
|
| 259 |
+
"128032": {
|
| 260 |
+
"content": "<|reserved_special_token_24|>",
|
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| 1500 |
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| 1850 |
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| 1860 |
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| 1866 |
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| 1868 |
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| 1882 |
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| 1884 |
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| 1885 |
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| 1886 |
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| 1887 |
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| 1890 |
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| 1892 |
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| 1893 |
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| 1898 |
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| 1900 |
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| 1901 |
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| 1903 |
+
"rstrip": false,
|
| 1904 |
+
"single_word": false,
|
| 1905 |
+
"special": true
|
| 1906 |
+
},
|
| 1907 |
+
"128238": {
|
| 1908 |
+
"content": "<|reserved_special_token_230|>",
|
| 1909 |
+
"lstrip": false,
|
| 1910 |
+
"normalized": false,
|
| 1911 |
+
"rstrip": false,
|
| 1912 |
+
"single_word": false,
|
| 1913 |
+
"special": true
|
| 1914 |
+
},
|
| 1915 |
+
"128239": {
|
| 1916 |
+
"content": "<|reserved_special_token_231|>",
|
| 1917 |
+
"lstrip": false,
|
| 1918 |
+
"normalized": false,
|
| 1919 |
+
"rstrip": false,
|
| 1920 |
+
"single_word": false,
|
| 1921 |
+
"special": true
|
| 1922 |
+
},
|
| 1923 |
+
"128240": {
|
| 1924 |
+
"content": "<|reserved_special_token_232|>",
|
| 1925 |
+
"lstrip": false,
|
| 1926 |
+
"normalized": false,
|
| 1927 |
+
"rstrip": false,
|
| 1928 |
+
"single_word": false,
|
| 1929 |
+
"special": true
|
| 1930 |
+
},
|
| 1931 |
+
"128241": {
|
| 1932 |
+
"content": "<|reserved_special_token_233|>",
|
| 1933 |
+
"lstrip": false,
|
| 1934 |
+
"normalized": false,
|
| 1935 |
+
"rstrip": false,
|
| 1936 |
+
"single_word": false,
|
| 1937 |
+
"special": true
|
| 1938 |
+
},
|
| 1939 |
+
"128242": {
|
| 1940 |
+
"content": "<|reserved_special_token_234|>",
|
| 1941 |
+
"lstrip": false,
|
| 1942 |
+
"normalized": false,
|
| 1943 |
+
"rstrip": false,
|
| 1944 |
+
"single_word": false,
|
| 1945 |
+
"special": true
|
| 1946 |
+
},
|
| 1947 |
+
"128243": {
|
| 1948 |
+
"content": "<|reserved_special_token_235|>",
|
| 1949 |
+
"lstrip": false,
|
| 1950 |
+
"normalized": false,
|
| 1951 |
+
"rstrip": false,
|
| 1952 |
+
"single_word": false,
|
| 1953 |
+
"special": true
|
| 1954 |
+
},
|
| 1955 |
+
"128244": {
|
| 1956 |
+
"content": "<|reserved_special_token_236|>",
|
| 1957 |
+
"lstrip": false,
|
| 1958 |
+
"normalized": false,
|
| 1959 |
+
"rstrip": false,
|
| 1960 |
+
"single_word": false,
|
| 1961 |
+
"special": true
|
| 1962 |
+
},
|
| 1963 |
+
"128245": {
|
| 1964 |
+
"content": "<|reserved_special_token_237|>",
|
| 1965 |
+
"lstrip": false,
|
| 1966 |
+
"normalized": false,
|
| 1967 |
+
"rstrip": false,
|
| 1968 |
+
"single_word": false,
|
| 1969 |
+
"special": true
|
| 1970 |
+
},
|
| 1971 |
+
"128246": {
|
| 1972 |
+
"content": "<|reserved_special_token_238|>",
|
| 1973 |
+
"lstrip": false,
|
| 1974 |
+
"normalized": false,
|
| 1975 |
+
"rstrip": false,
|
| 1976 |
+
"single_word": false,
|
| 1977 |
+
"special": true
|
| 1978 |
+
},
|
| 1979 |
+
"128247": {
|
| 1980 |
+
"content": "<|reserved_special_token_239|>",
|
| 1981 |
+
"lstrip": false,
|
| 1982 |
+
"normalized": false,
|
| 1983 |
+
"rstrip": false,
|
| 1984 |
+
"single_word": false,
|
| 1985 |
+
"special": true
|
| 1986 |
+
},
|
| 1987 |
+
"128248": {
|
| 1988 |
+
"content": "<|reserved_special_token_240|>",
|
| 1989 |
+
"lstrip": false,
|
| 1990 |
+
"normalized": false,
|
| 1991 |
+
"rstrip": false,
|
| 1992 |
+
"single_word": false,
|
| 1993 |
+
"special": true
|
| 1994 |
+
},
|
| 1995 |
+
"128249": {
|
| 1996 |
+
"content": "<|reserved_special_token_241|>",
|
| 1997 |
+
"lstrip": false,
|
| 1998 |
+
"normalized": false,
|
| 1999 |
+
"rstrip": false,
|
| 2000 |
+
"single_word": false,
|
| 2001 |
+
"special": true
|
| 2002 |
+
},
|
| 2003 |
+
"128250": {
|
| 2004 |
+
"content": "<|reserved_special_token_242|>",
|
| 2005 |
+
"lstrip": false,
|
| 2006 |
+
"normalized": false,
|
| 2007 |
+
"rstrip": false,
|
| 2008 |
+
"single_word": false,
|
| 2009 |
+
"special": true
|
| 2010 |
+
},
|
| 2011 |
+
"128251": {
|
| 2012 |
+
"content": "<|reserved_special_token_243|>",
|
| 2013 |
+
"lstrip": false,
|
| 2014 |
+
"normalized": false,
|
| 2015 |
+
"rstrip": false,
|
| 2016 |
+
"single_word": false,
|
| 2017 |
+
"special": true
|
| 2018 |
+
},
|
| 2019 |
+
"128252": {
|
| 2020 |
+
"content": "<|reserved_special_token_244|>",
|
| 2021 |
+
"lstrip": false,
|
| 2022 |
+
"normalized": false,
|
| 2023 |
+
"rstrip": false,
|
| 2024 |
+
"single_word": false,
|
| 2025 |
+
"special": true
|
| 2026 |
+
},
|
| 2027 |
+
"128253": {
|
| 2028 |
+
"content": "<|reserved_special_token_245|>",
|
| 2029 |
+
"lstrip": false,
|
| 2030 |
+
"normalized": false,
|
| 2031 |
+
"rstrip": false,
|
| 2032 |
+
"single_word": false,
|
| 2033 |
+
"special": true
|
| 2034 |
+
},
|
| 2035 |
+
"128254": {
|
| 2036 |
+
"content": "<|reserved_special_token_246|>",
|
| 2037 |
+
"lstrip": false,
|
| 2038 |
+
"normalized": false,
|
| 2039 |
+
"rstrip": false,
|
| 2040 |
+
"single_word": false,
|
| 2041 |
+
"special": true
|
| 2042 |
+
},
|
| 2043 |
+
"128255": {
|
| 2044 |
+
"content": "<|reserved_special_token_247|>",
|
| 2045 |
+
"lstrip": false,
|
| 2046 |
+
"normalized": false,
|
| 2047 |
+
"rstrip": false,
|
| 2048 |
+
"single_word": false,
|
| 2049 |
+
"special": true
|
| 2050 |
+
}
|
| 2051 |
+
},
|
| 2052 |
+
"bos_token": "<|begin_of_text|>",
|
| 2053 |
+
"clean_up_tokenization_spaces": true,
|
| 2054 |
+
"eos_token": "<|end_of_text|>",
|
| 2055 |
+
"model_input_names": [
|
| 2056 |
+
"input_ids",
|
| 2057 |
+
"attention_mask"
|
| 2058 |
+
],
|
| 2059 |
+
"model_max_length": 131072,
|
| 2060 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
| 2061 |
+
"trust_remote": true
|
| 2062 |
+
}
|