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.gitattributes

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LICENSE

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+Tencent is pleased to support the open source community by making Universal_Audio_Tokenizer available.
+
+Copyright (C) 2026 Tencent.  All rights reserved. 
+
+The open-source software and/or models included in this distribution may have been modified by Tencent (“Tencent Modifications”). All Tencent Modifications are Copyright (C) Tencent.
+
+Universal_Audio_Tokenizer is licensed under the License Terms of Universal_Audio_Tokenizer, except for the third-party components listed below, which remain licensed under their respective original terms. Universal_Audio_Tokenizer does not impose any additional restrictions beyond those specified in the original licenses of these third-party components. Users are required to comply with all applicable terms and conditions of the original licenses and to ensure that the use of these third-party components conforms to all relevant laws and regulations.
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+For the avoidance of doubt, Universal_Audio_Tokenizer refers solely to code, parameters, and weights made publicly available by Tencent in accordance with the License Terms of Universal_Audio_Tokenizer. 
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+Terms of the License Terms of Universal_Audio_Tokenizer:
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README.md

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+---
+license: other
+license_name: license-term-of-universal-audio-tokenizer
+language:
+- en
+- zh
+tags:
+- audio
+- audio-tokenizer
+- speech-tokenizer
+- speech
+- sound
+- music
+---
+# Universal Audio Tokenizer: Empowering Semantic Speech Tokenizers with General Audio Perception
+
+**Universal Audio Tokenizer** is a compact single-codebook audio tokenizer that unifies general audio perception and
+linguistic alignment for downstream Audio-LLMs.
+
+📄 [Paper](https://arxiv.org/abs/2605.31521) | 💻 [GitHub](https://github.com/Tencent/Universal_Audio_Tokenizer)
+
+For code and more detailed information, please refer to the corresponding [GitHub repository](https://github.com/Tencent/Universal_Audio_Tokenizer).
+
+## 💡 Highlights
+
+Existing semantic speech tokenizers often suffer from *acoustic blindness*, while acoustic tokenizers typically lack *linguistic alignment*.
+
+**Universal Audio Tokenizer** bridges this gap through:
+-   🧩 **Semantic-Acoustic Primitives (SAP) supervision** that decomposes raw audio into fundamental linguistic content, vocal attributes, and auditory-scene primitives
+-   ⚖️ **Semantic-Acoustic Equilibrium (SAE) mechanism** that adaptively injects fine-grained acoustic details from shallow encoder layers into deep semantic streams
+
+This results in a compact single-codebook audio tokenizer that **simultaneously** enables:
+*   🧠 **Seamless LLM Integration**: A unified audio input/output interface in Audio-LLMs
+*   🗣️ **Linguistic Alignment**: Superior performance on speech reconstruction and TTS synthesis tasks
+*   🎯 **General Audio Perception**: Discriminative representations for diverse audio events and strong performance on downstream audio understanding benchmarks
+
+## 📌 Model Details
+
+| Attribute | Value |
+|:----------|:------|
+| Frame Rate | 25 Hz |
+| Codebook Size | 8,192 |
+| Bits Per Second (BPS) | 325 |
+
+## 🚀 Quick Start
+
+To use Universal Audio Tokenizer, please clone the official repository and install dependencies.
+
+### Installation
+
+```bash
+# 1. Clone the repository with all submodules
+git clone --recursive https://github.com/Tencent/Universal_Audio_Tokenizer.git
+cd Universal_Audio_Tokenizer
+
+# If you have already cloned the repository without --recursive,
+# initialize submodules with:
+git submodule update --init --recursive
+
+# 2. Create a conda environment
+conda create -n universal-audio-tokenizer python=3.10.13 -y
+conda activate universal-audio-tokenizer
+
+# 3. Install dependencies
+conda install -c conda-forge libsndfile -y
+pip install -r requirements.txt
+```
+
+### Download Pretrained Checkpoints
+
+Using `huggingface-cli`:
+
+```bash
+huggingface-cli download tencent/Universal_Audio_Tokenizer \
+  --local-dir checkpoints/Universal_Audio_Tokenizer
+```
+
+Or using Python:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="tencent/Universal_Audio_Tokenizer", 
+    local_dir="checkpoints/Universal_Audio_Tokenizer"
+)
+```
+
+### Run Inference
+
+We provide a simple inference demo in `example_usage.py`.
+
+```bash
+python example_usage.py \
+  --device auto \
+  --model_path checkpoints/Universal_Audio_Tokenizer \
+  --audio_path /path/to/audio.wav
+```
+
+The script will:
+
+- load the tokenizer and feature extractor;
+- extract discrete audio tokens from input audio clips;
+- reconstruct waveforms from the tokens and save reconstructed audio under `reconstruction/`.
+
+Also, you can directly run the inference code snippet below:
+
+```python
+import os
+import torch
+from transformers import WhisperFeatureExtractor
+from src.model.modeling_whisper import WhisperVQEncoder
+from src.model.flow_inference import AudioDecoder
+from src.model.utils import extract_audio_token, speech_token_to_wav
+
+# 1. Download & Load Models
+model_dir = snapshot_download("tencent/Universal_Audio_Tokenizer")
+
+# Load tokenizer and feature extractor
+tokenizer_path = os.path.join(model_dir, "tokenizer")
+tokenizer = WhisperVQEncoder.from_pretrained(tokenizer_path).eval().cuda()
+feature_extractor = WhisperFeatureExtractor.from_pretrained(tokenizer_path)
+
+# Load decoder
+decoder_path = os.path.join(model_dir, "decoder")
+decoder = AudioDecoder(
+	config_path=os.path.join(decoder_path, "config.yaml"),
+	flow_ckpt_path=os.path.join(decoder_path, "flow.pt"),
+	hift_ckpt_path=os.path.join(decoder_path, "hift.pt"),
+	device="cuda"
+)
+
+# 2. Tokenize
+tokens = extract_audio_token(tokenizer, feature_extractor, ["/path/to/audio.wav"], device="cuda")[0]
+
+# 3. Reconstruct
+tts_speech, sampling_rate = speech_token_to_wav(decoder, tokens)
+```
+
+## 📊 Performance
+
+Universal Audio Tokenizer learns discriminative representations for diverse audio events, and achieves strong performance on speech reconstruction, downstream audio understanding, and TTS synthesis tasks.
+
+### Latent Space Disentanglement
+
+We use high-dimensional token histogram vectors for cluster analysis. The results (Silhouette Score and Cluster Purity) show that our model effectively encodes general audio, with clearer cluster separation in the latent space.
+
+| Model | ESC-10 Sil. (↑) | ESC-10 Purity (↑) | ESC-50 Sil. (↑) | ESC-50 Purity (↑) |
+|:---|:---:|:---:|:---:|:---:|
+| [WavTokenizer](https://github.com/jishengpeng/WavTokenizer) | -0.030 | 0.450 | -0.108 | 0.215 |
+| [GLM-4-Voice-Tokenizer](https://github.com/zai-org/GLM-4-Voice) | -0.182 | 0.373 | -0.304 | 0.133 |
+| [CosyVoice2](https://github.com/FunAudioLLM/CosyVoice) | -0.016 | 0.413 | -0.100 | 0.216 |
+| [StableToken](https://github.com/Tencent/StableToken) | -0.035 | 0.468 | -0.096 | 0.174 |
+| **Ours** | **0.091** | **0.730** | **0.023** | **0.390** |
+
+### High-Quality Speech Reconstruction
+
+Our Universal Audio Tokenizer achieves high-quality speech reconstruction with a compact single-codebook design, significantly improving Word Error Rate (WER) and Mean Opinion Score (MOS) compared to existing supervised semantic tokenizers.
+
+| Model | Frame<br>Rate | BPS | WER (↓)<br>LS-clean | WER (↓)<br>LS-other | WER (↓)<br>SEED-en | WER (↓)<br>SEED-zh | MOS (↑)<br>LS-clean | MOS (↑)<br>LS-other | MOS (↑)<br>SEED-en | MOS (↑)<br>SEED-zh |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [WavTokenizer](https://github.com/jishengpeng/WavTokenizer) | 75Hz | 900 | 5.07 | 13.09 | 5.60 | 4.02 | 3.37 | 3.09 | 3.01 | 3.13 |
+| [GLM-4-Voice-Tokenizer](https://github.com/zai-org/GLM-4-Voice) | 12.5Hz | 175 | 4.04 | 9.33 | 3.54 | 3.23 | 4.07 | 3.99 | **4.16** | 4.10 |
+| [CosyVoice2](https://github.com/FunAudioLLM/CosyVoice) | 25Hz | 325 | 4.25 | 9.68 | 4.34 | 2.75 | 3.36 | 3.25 | 3.31 | 3.58 |
+| [StableToken](https://github.com/Tencent/StableToken) | 25Hz | 325 | 3.84 | 7.99 | 3.44 | 2.62 | 4.09 | 3.83 | 4.01 | 4.18 |
+| **Ours** | 25Hz | 325 | **3.47** | **6.79** | **2.55** | **1.90** | **4.19** | **4.18** | 4.13 | **4.25** |
+
+### Superior Downstream Audio-LLM Performance
+
+When integrated with the Qwen2.5 LLM backbone, our Universal Audio Tokenizer yields superior performance on a wide range of downstream audio understanding benchmarks and controllable TTS synthesis tasks, demonstrating its effectiveness as a unified audio input/output interface for Audio-LLMs.
+
+#### Audio Understanding
+
+Accuracy on audio understanding benchmarks:
+
+| **Tokenizer** | MMAU<br>(Speech) | MMAU<br>(Sound) | MMAU<br>(Music) | **MMAU<br>(Overall)** | MMAR<br>(Speech) | MMAR<br>(Sound) | MMAR<br>(Music) | **MMAR<br>(Overall)** | MMSU<br>(Perception) | MMSU<br>(Reasoning) | **MMSU<br>(Overall)** |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [WavTokenizer](https://github.com/jishengpeng/WavTokenizer) | 36.94 | 60.36 | 57.78 | 51.70 | 39.80 | 31.52 | 29.61 | 36.30 | 32.83 | 45.37 | 38.90 |
+| [CosyVoice2](https://github.com/FunAudioLLM/CosyVoice) | 39.94 | 61.56 | 62.57 | 54.70 | 41.50 | 35.76 | 30.58 | 38.10 | 27.44 | 45.83 | 36.34 |
+| [GLM-4-Voice-Tokenizer](https://github.com/zai-org/GLM-4-Voice) | 43.24 | 60.06 | 62.28 | 55.20 | 39.46 | 40.00 | 36.89 | 40.10 | 32.40 | 47.64 | 39.78 |
+| [StableToken](https://github.com/Tencent/StableToken) | **45.05** | 58.56 | 55.99 | 53.20 | 42.18 | 39.39 | 31.07 | 39.10 | 31.98 | 49.71 | 40.56 |
+| **Ours** | **45.05** | **70.27** | **67.96** | **61.10** (+5.90) | **45.24** | **43.64** | **40.29** | **45.80** (+5.70) | **35.54** | **52.07** | **43.54** (+2.98) |
+
+#### Controllable TTS Synthesis
+
+Results on SEED-TTS, measured by speaker similarity (SIM), word error rate (WER), and mean opinion score (MOS).
+
+| Tokenizer | SIM (↑) | WER (↓) | MOS (↑) |
+|:---|:---:|:---:|:---:|
+| [CosyVoice2](https://github.com/FunAudioLLM/CosyVoice) | .758 \| **.762** \| .760 | 2.71 \| 1.39 \| 2.05 | 3.75 \| 3.37 \| 3.56 |
+| **Ours** | **.792** \| .742 \| **.767** | **1.78** \| **1.29** \| **1.54** | **4.07** \| **3.68** \| **3.88** |
+
+## Citation
+
+If you find our code or model useful for your research, please cite:
+
+```bibtex
+@misc{song2026uniaudiotokenempoweringsemanticspeech,
+      title={UniAudio-Token: Empowering Semantic Speech Tokenizers with General Audio Perception}, 
+      author={Yuhan Song and Linhao Zhang and Aiwei Liu and Chuhan Wu and Sijun Zhang and Wei Jia and Yuan Liu and Houfeng Wang and Xiao Zhou},
+      year={2026},
+      eprint={2605.31521},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2605.31521}, 
+}
+```
+
+## License
+
+This project is licensed under the [License Term of Universal_Audio_Tokenizer](LICENSE).

decoder/config.yaml

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+# set random seed, so that you may reproduce your result.
+__set_seed1: !apply:random.seed [1986]
+__set_seed2: !apply:numpy.random.seed [1986]
+__set_seed3: !apply:torch.manual_seed [1986]
+__set_seed4: !apply:torch.cuda.manual_seed_all [1986]
+
+# fixed params
+sample_rate: 22050
+text_encoder_input_size: 512
+llm_input_size: 1024
+llm_output_size: 1024
+spk_embed_dim: 192
+
+# model params
+# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
+# for system/third_party class/function, we do not require this.
+llm: !new:cosyvoice.llm.llm.TransformerLM
+    text_encoder_input_size: !ref <text_encoder_input_size>
+    llm_input_size: !ref <llm_input_size>
+    llm_output_size: !ref <llm_output_size>
+    text_token_size: 51866
+    speech_token_size: 4096
+    length_normalized_loss: True
+    lsm_weight: 0
+    spk_embed_dim: !ref <spk_embed_dim>
+    text_encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
+        input_size: !ref <text_encoder_input_size>
+        output_size: 1024
+        attention_heads: 8
+        linear_units: 2048
+        num_blocks: 3
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0
+        normalize_before: True
+        input_layer: 'linear'
+        pos_enc_layer_type: 'rel_pos_espnet'
+        selfattention_layer_type: 'rel_selfattn'
+        use_cnn_module: False
+        macaron_style: False
+        use_dynamic_chunk: False
+        use_dynamic_left_chunk: False
+        static_chunk_size: 1
+    llm: !new:cosyvoice.transformer.encoder.TransformerEncoder
+        input_size: !ref <llm_input_size>
+        output_size: !ref <llm_output_size>
+        attention_heads: 8
+        linear_units: 2048
+        num_blocks: 7
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0
+        input_layer: 'linear_legacy'
+        pos_enc_layer_type: 'rel_pos_espnet'
+        selfattention_layer_type: 'rel_selfattn'
+        static_chunk_size: 1
+
+flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
+    input_size: 512
+    output_size: 80
+    spk_embed_dim: !ref <spk_embed_dim>
+    output_type: 'mel'
+    vocab_size: 8192
+    input_frame_rate: 25
+    only_mask_loss: True
+    encoder: !new:cosyvoice.transformer.encoder.BlockConformerEncoder
+        output_size: 512
+        attention_heads: 8
+        linear_units: 2048
+        num_blocks: 6
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0.1
+        normalize_before: True
+        input_layer: 'linear'
+        pos_enc_layer_type: 'rel_pos_espnet'
+        selfattention_layer_type: 'block_rel_selfattn'
+        block_size: 10
+        input_size: 512
+        use_cnn_module: False
+        macaron_style: False
+    length_regulator: !new:cosyvoice.flow.length_regulator.InterpolateRegulator
+        channels: 80
+        sampling_ratios: [1, 1, 1, 1]
+    decoder: !new:cosyvoice.flow.flow_matching.ConditionalCFM
+        in_channels: 240
+        n_spks: 1
+        spk_emb_dim: 80
+        cfm_params: !new:omegaconf.DictConfig
+            content:
+                sigma_min: 1e-06
+                solver: 'euler'
+                t_scheduler: 'cosine'
+                training_cfg_rate: 0.2
+                inference_cfg_rate: 0.7
+                reg_loss_type: 'l1'
+        estimator: !new:cosyvoice.flow.decoder.ConditionalDecoder
+            in_channels: 320
+            out_channels: 80
+            channels: [256, 256]
+            dropout: 0
+            attention_head_dim: 64
+            n_blocks: 4
+            num_mid_blocks: 12
+            num_heads: 8
+            act_fn: 'gelu'
+
+hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
+    in_channels: 80
+    base_channels: 512
+    nb_harmonics: 8
+    sampling_rate: !ref <sample_rate>
+    nsf_alpha: 0.1
+    nsf_sigma: 0.003
+    nsf_voiced_threshold: 10
+    upsample_rates: [8, 8]
+    upsample_kernel_sizes: [16, 16]
+    istft_params:
+        n_fft: 16
+        hop_len: 4
+    resblock_kernel_sizes: [3, 7, 11]
+    resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
+    source_resblock_kernel_sizes: [7, 11]
+    source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
+    lrelu_slope: 0.1
+    audio_limit: 0.99
+    f0_predictor: !new:cosyvoice.hifigan.f0_predictor.ConvRNNF0Predictor
+        num_class: 1
+        in_channels: 80
+        cond_channels: 512

decoder/flow.pt

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+size 428399772

decoder/hift.pt

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+size 81897015

tokenizer/config.json

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+{
+  "_name_or_path": "openai/whisper-large-v3",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperVQEncoder"
+  ],
+  "attention_dropout": 0.0,
+  "batch_maximization_weight": 0.3,
+  "classifier_proj_size": 256,
+  "codebook_gaussian_init": false,
+  "d_model": 1280,
+  "dropout": 0.0,
+  "encoder_attention_heads": 20,
+  "encoder_causal_attention": false,
+  "encoder_causal_convolution": false,
+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "encoder_residual_source_position": 3,
+  "encoder_residual_target_position": 32,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 2048,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 128,
+  "pooling_kernel_size": 2,
+  "pooling_position": 0,
+  "pooling_type": "avg",
+  "quantize_causal_block_size": null,
+  "quantize_causal_encoder": false,
+  "quantize_commit_coefficient": 0.25,
+  "quantize_ema_decay": 0.95,
+  "quantize_encoder_only": true,
+  "quantize_loss_scale": 10.0,
+  "quantize_position": 16,
+  "quantize_restart_interval": 10,
+  "quantize_vocab_size": 8192,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0",
+  "use_cache": true,
+  "use_commit_loss": true,
+  "use_residual_adapter": true,
+  "use_residual_gate": true,
+  "vocab_size": 51869
+}

tokenizer/model.safetensors

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tokenizer/preprocessor_config.json

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+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}