--- title: MossAudioTokenizer-ONNX canonical_url: "https://www.modelscope.cn/models/KeanuX/MossAudioTokenizer-ONNX" md_url: "https://www.modelscope.cn/models/KeanuX/MossAudioTokenizer-ONNX.md" repository: KeanuX/MossAudioTokenizer-ONNX chinese_name: MossAudioTokenizer-ONNX last_updated: 2026-02-28 license: "Apache License 2.0" pipeline_tag: audio-codec tasks: - audio-codec base_model: - openmoss/MOSS-Audio-Tokenizer base_model_relation: repackage library_name: - onnx - other frameworks: - other language: - zh - en downloads: 12 stars: 0 tags: - onnx - "audio tokenizer" - "语音合成" - "多语言支持" - transformer --- # MossAudioTokenizer-ONNX > MossAudioTokenizer-ONNX - KeanuX 在 ModelScope 开源的模型。MOSSAudioTokenizer 是一种基于 Cat（Causal Audio Tokenizer with Transformer）架构的统一离散音频分词器。该模型参数量达到 16 亿，可作为统一的离散接口，同时实现无损质量重建和高层语义对齐。 KeanuX/MossAudioTokenizer-ONNX 是 ModelScope 魔搭社区上的audio-codec模型，采用 Apache License 2.0 许可，基于 openmoss/MOSS-Audio-Tokenizer 构建。 - **Repository**: KeanuX/MossAudioTokenizer-ONNX - **License**: Apache License 2.0 - **Tasks**: audio-codec - **Base model**: openmoss/MOSS-Audio-Tokenizer - **Tags**: onnx, audio tokenizer, 语音合成, 多语言支持, transformer - **Downloads**: 12 - **Stars**: 0 - **Last updated**: 2026-02-28 Source: https://www.modelscope.cn/models/KeanuX/MossAudioTokenizer-ONNX --- # Moss Audio Tokenizer ONNX ## 模型简介 MOSSAudioTokenizer 是一种基于 Cat（Causal Audio Tokenizer with Transformer）架构的统一离散音频分词器。该模型参数量达到 16 亿，可作为统一的离散接口，同时实现无损质量重建和高层语义对齐。 主要特性： 极致压缩与可变比特率：将 24kHz 原始音频压缩至极低的 12.5Hz 帧率。通过 32 层残差矢量量化器（RVQ），支持从 0.125kbps 到 4kbps 的宽范围比特率下的高保真重建。 纯 Transformer 架构：模型采用“无 CNN”的同质架构，完全由因果 Transformer 模块构建。编码器与解码器合计 16 亿参数，确保卓越的可扩展性，并支持低延迟流式推理。 大规模通用音频训练：在 300 万小时多样化音频数据上进行训练，能够出色地编码和重建所有音频领域内容，包括语音、音效和音乐。 统一的语义-声学表征：在实现当前最优重建质量的同时，Cat 生成的离散 token 具有“丰富的语义”，非常适合下游任务，如语音理解（ASR）和语音合成（TTS）。 完全从零训练：Cat 不依赖任何预训练编码器（如 HuBERT 或 Whisper），也不使用教师模型蒸馏。所有表征均从原始数据中自主学习。 端到端联合优化：所有组件——包括编码器、量化器、解码器、判别器，以及用于语义对齐的仅解码器 LLM——均在单一统一训练流程中联合优化。 总结： 通过结合简洁可扩展的架构与大规模数据，Cat 架构突破了传统音频分词器的瓶颈，为下一代原生音频基础模型提供了鲁棒、高保真且语义扎实的接口。 本仓库包含了 MOSSAudioTokenizer Decoder 的 ONNX 模型文件，以及使用该模型进行音频解码的 Py 代码。 ![架构图](./rep_sources/arch.png) ## 开源Git仓库 - GitHub: [https://github.com/201831771214/MossAudioTokenizer-ONNX](https://github.com/201831771214/MossAudioTokenizer-ONNX) ### 仓库结构 ```shell ./ ├── audio_tokens.npy ├── check_onnx.py ├── export_audio_tokenizer.py ├── export_model_info.py ├── generated_audio.wav ├── infos │   └── audio_tokenizer_decoder.info ├── logs │   ├── check_onnx.log │   └── run_onnx.log ├── models │   └── moss_audio_tokenizer_onnx │   ├── 2005f62a-1458-11f1-80d4-cc28aa3bf0f5.data │   ├── 2799b6f6-1458-11f1-80d4-cc28aa3bf0f5.data │   ├── audio_tokenizer_decoder.onnx │   └── audio_tokenizer_encoder.onnx ├── moss_audio_tokenizer │   ├── config.json │   ├── configuration_moss_audio_tokenizer.py │   ├── demo │   │   ├── demo_gt.wav │   │   └── test_reconstruction.py │   ├── images │   │   ├── arch.png │   │   ├── metrics_on_librispeech_test_clean.png │   │   ├── mosi-logo.png │   │   ├── OpenMOSS_Logo.png │   │   └── reconstruct_comparison_table.png │   ├── __init__.py │   ├── LICENSE │   ├── modeling_moss_audio_tokenizer.py │   ├── __pycache__ │   │   ├── configuration_moss_audio_tokenizer.cpython-310.pyc │   │   ├── __init__.cpython-310.pyc │   │   └── modeling_moss_audio_tokenizer.cpython-310.pyc │   ├── README.md │   └── requirements.txt ├── moss_extra │   ├── audio_tokenizer_decoder.py │   └── audio_tokenizer_encoder.py ├── README_Git.md ├── README_ModelScope.md ├── rep_sources │   ├── arch.png │   ├── License-MIT-yellow.png │   ├── NumPy-1.26.4-blue.png │   ├── ONNX-1.20.1-green.png │   ├── Onnx Runtime-1.23.2-blue.png │   └── Python-3.10+-blue.png ├── requirements.txt ├── run_decoder.py └── run_encoder.py 11 directories, 42 files ``` ## 使用方法 ### 模型信息 ```txt ============================================================ #### Audio Decoder Infos #### ============================================================ ONNX模型基本信息 ============================================================ 模型文件路径: ./models/moss_audio_tokenizer_decoder_onnx/audio_tokenizer_decoder.onnx ONNX版本: 7 生产者信息: pytorch 2.8.0 模型版本: 0 描述: ============================================================ 模型输入信息 (共 1 个输入) ============================================================ Input 1: audio_codes 数据类型: int32 形状: [0, 0, 0] ============================================================ 模型输出信息 (共 1 个输出) ============================================================ Output 1: audio 数据类型: float32 形状: [0, 0] #### Audio Encoder Infos #### ============================================================ ONNX模型基本信息 ============================================================ 模型文件路径: ./models/moss_audio_tokenizer_decoder_onnx/audio_tokenizer_encoder.onnx ONNX版本: 7 生产者信息: pytorch 2.8.0 模型版本: 0 描述: ============================================================ 模型输入信息 (共 1 个输入) ============================================================ Input 1: input_values 数据类型: float32 形状: [0, 0, 0] ============================================================ 模型输出信息 (共 3 个输出) ============================================================ Output 1: audio_codes 数据类型: int64 形状: [32, 0, 0] Output 2: audio_codes_lengths 数据类型: int64 形状: [0] Output 3: encoder_hidden_states 数据类型: float32 形状: [0, 768, 0] ``` 详细信息请参考: [ONNX模型信息](https://github.com/201831771214/MossAudioTokenizer-ONNX/infos/) ### 安装依赖 ```shell # 安装依赖 pip install -r requirements.txt ``` ### 快速入门 #### Run Audio Tokenizer Decoder ```python import onnxruntime as ort import numpy as np from numpy.typing import NDArray import soundfile as sf from typing import Tuple import os import sys import logging logger = logging.getLogger(__name__) file_handler = logging.FileHandler("./logs/run_decoder.log", mode="w", encoding="utf-8") formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") file_handler.setFormatter(formatter) file_handler.setLevel(logging.INFO) logger.addHandler(file_handler) logger.setLevel(logging.INFO) class AudioTokenizerDecoder: def __init__(self, model_path:str, device:str="cuda"): self.model_path = model_path self.device = device.lower() logger.info(f"All available providers: {ort.get_available_providers()}") if device == "cuda" and "CUDAExecutionProvider" in ort.get_available_providers(): self.providers = ["CUDAExecutionProvider"] elif device == "cpu" and "CPUExecutionProvider" in ort.get_available_providers(): self.providers = ["CPUExecutionProvider"] else: logger.warning(f"Device {device} is not supported. Fall back to CPU.") self.providers = ["CPUExecutionProvider"] # Configure session options for memory optimization sess_options = ort.SessionOptions() sess_options.enable_mem_pattern = False sess_options.enable_mem_reuse = False sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL self.session = ort.InferenceSession(model_path, providers=self.providers, sess_options=sess_options) self.input_names = [input.name for input in self.session.get_inputs()] self.output_names = [output.name for output in self.session.get_outputs()] logger.info(f"Session Input names: {self.input_names}") logger.info(f"Session Output names: {self.output_names}") def __decode(self, audio_tokens:np.ndarray) -> Tuple[np.ndarray, int]: """Decode audio tokens into audio waveform. Args: audio_tokens (np.ndarray): Audio tokens of shape (batch_size, channels, audio_tokens_len). Returns: audio (np.ndarray): Audio waveform of shape (batch_size, audio_len). sample_rate (int): Sample rate of the audio waveform. """ input_spec = { self.input_names[0]: audio_tokens } audio = self.session.run(self.output_names, input_spec)[0] return audio, 24000 # TODO: Both support dynamic input shape and static input shape, when the input shape is static, the chunk_size should be the same as the audio_tokens_len. def decode_chunked(self, audio_tokens:np.ndarray, chunk_size:int=200) -> Tuple[np.ndarray, int]: """Decode audio tokens in chunks to manage memory.""" batch_size, channels, seq_len = audio_tokens.shape if seq_len <= chunk_size: return self.__decode(audio_tokens) # Process in chunks chunks = [] for i in range(0, seq_len, chunk_size): chunk = audio_tokens[:, :, i:i+chunk_size] chunk_audio, _ = self.__decode(chunk) chunks.append(chunk_audio) # Concatenate the results (this might need adjustment based on model behavior) full_audio = np.concatenate(chunks, axis=-1) # This depends on the output shape return full_audio, 24000 test_audio_tokens = "./audio_tokens.npy" model_path = "./models/moss_audio_tokenizer_decoder_onnx/audio_tokenizer_decoder.onnx" output_path = "generated_audio.wav" if __name__ == "__main__": audio_decoder = AudioTokenizerDecoder(model_path) audio_tokens:NDArray = np.load(test_audio_tokens) if audio_tokens.ndim != 3: audio_tokens = np.expand_dims(audio_tokens, axis=0).astype(np.int32) logger.info(f"Audio tokens shape: {audio_tokens.shape}") audio, sample_rate = audio_decoder.decode_chunked(audio_tokens) logger.info(f"Generated Audio shape: {audio.shape}") if audio.ndim == 2: audio = audio[0] sf.write(output_path, audio, sample_rate) logger.info(f"Audio waveform saved to {output_path} with sample rate {sample_rate}") ``` ##### Decode Audio Tokens Result - 生成的音频文件: [generated_audio.wav](https://github.com/201831771214/MossAudioTokenizer-ONNX/blob/main/generated_audio.wav) - Audio Text: "MOSS-TTS-Realtime 是一个上下文感知、多轮次流式 TTS 模型，专为实时语音智能体设计。通过结合文本对话历史和用户先前的声学特征，它能在多轮交互中提供低延迟、连贯一致的语音响应。" #### Run Audio Tokenizer Encoder ```python import onnxruntime as ort import numpy as np from numpy.typing import NDArray import librosa from typing import Tuple import os import sys import logging logger = logging.getLogger(__name__) file_handler = logging.FileHandler("./logs/run_encoder.log", mode="w", encoding="utf-8") formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") file_handler.setFormatter(formatter) file_handler.setLevel(logging.INFO) logger.addHandler(file_handler) logger.setLevel(logging.INFO) class AudioTokenizerEncoder: def __init__(self, model_path:str, device:str="cuda"): self.model_path = model_path self.device = device.lower() logger.info(f"All available providers: {ort.get_available_providers()}") if device == "cuda" and "CUDAExecutionProvider" in ort.get_available_providers(): self.providers = ["CUDAExecutionProvider"] elif device == "cpu" and "CPUExecutionProvider" in ort.get_available_providers(): self.providers = ["CPUExecutionProvider"] else: logger.warning(f"Device {device} is not supported. Fall back to CPU.") self.providers = ["CPUExecutionProvider"] # Configure session options for memory optimization sess_options = ort.SessionOptions() sess_options.enable_mem_pattern = False sess_options.enable_mem_reuse = False sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL self.session = ort.InferenceSession(model_path, providers=self.providers, sess_options=sess_options) self.input_names = [input.name for input in self.session.get_inputs()] self.output_names = [output.name for output in self.session.get_outputs()] logger.info(f"Session Input names: {self.input_names}") logger.info(f"Session Output names: {self.output_names}") def encode(self, audio_data:np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Encode audio waveform into audio tokens. Args: audio_data (np.ndarray): Audio waveform of shape (batch_size, channels, audio_len). Returns: audio_codes (np.ndarray): Audio tokens of shape (num_layers, batch_size, seq_len). audio_codes_lengths (np.ndarray): Audio tokens lengths of shape (seq_len,). encoder_hidden_states (np.ndarray): Encoder hidden states of shape (batch_size, d_model, seq_len). """ input_spec = { self.input_names[0]: audio_data } audio_codes, audio_codes_lengths, encoder_hidden_states = self.session.run(self.output_names, input_spec) return audio_codes, audio_codes_lengths, encoder_hidden_states test_audio = "./sources/audios/dubowen.wav" model_path = "./models/moss_audio_tokenizer_decoder_onnx/audio_tokenizer_encoder.onnx" if __name__ == "__main__": audio_encoder = AudioTokenizerEncoder(model_path) audio_data, sample_rate = librosa.load(test_audio, sr=24000, dtype=np.float32) logger.info(f"Audio data shape: {audio_data.shape}") # Add batch size and channel dimension if not present if audio_data.ndim == 1: audio_data = np.expand_dims(audio_data, axis=0) audio_data = np.expand_dims(audio_data, axis=1) audio_codes, audio_codes_lengths, encoder_hidden_states = audio_encoder.encode(audio_data) logger.info(f"Audio codes shape: {audio_codes.shape} ---- {audio_codes}") logger.info(f"Audio codes lengths shape: {audio_codes_lengths.shape} ---- {audio_codes_lengths}") logger.info(f"Encoder hidden states shape: {encoder_hidden_states.shape} ---- {encoder_hidden_states}") ``` ##### Audio Tokenizer Encoder Result ```shell 2026-02-28 14:30:43,491 - INFO - Audio codes shape: (32, 1, 69) ---- [[[ 817 57 132 ... 335 335 950]] [[ 470 18 632 ... 175 667 370]] [[ 224 363 968 ... 516 282 126]] ... [[ 125 958 275 ... 794 610 522]] [[ 963 217 460 ... 468 575 452]] [[1014 367 950 ... 968 688 369]]] 2026-02-28 14:30:43,491 - INFO - Audio codes lengths shape: (1,) ---- [68] 2026-02-28 14:30:43,492 - INFO - Encoder hidden states shape: (1, 768, 69) ---- [[[ 9.686964 -5.875668 -4.5082426 ... 4.122473 4.668351 4.7193575 ] [-3.454163 -1.0035998 2.433176 ... -0.72330576 -1.1036214 -1.0855117 ] [-2.9715493 2.7055 2.9065142 ... -1.2198896 3.042768 2.9706757 ] ... [-6.3878407 -8.618917 8.772977 ... 4.81421 4.5244756 4.5736403 ] [-4.635868 9.056893 6.7735367 ... -3.4137735 -4.1788063 -4.2336264 ] [ 4.5891705 12.405132 -1.1379069 ... -2.1964636 -2.6125443 -2.6521518 ]]] ```