Feature Extraction
Transformers
Safetensors
qwen2_5_omni_audio_encoder
speech
audio-encoder
flexislm
qwen2.5-omni
Instructions to use FlexiSLM/Qwen2_5-Omni-Audio_Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlexiSLM/Qwen2_5-Omni-Audio_Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FlexiSLM/Qwen2_5-Omni-Audio_Encoder")# Load model directly from transformers import Qwen2_5OmniAudioEncoder model = Qwen2_5OmniAudioEncoder.from_pretrained("FlexiSLM/Qwen2_5-Omni-Audio_Encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: transformers | |
| pipeline_tag: feature-extraction | |
| base_model: Qwen/Qwen2.5-Omni-7B | |
| tags: | |
| - speech | |
| - audio-encoder | |
| - flexislm | |
| - qwen2.5-omni | |
| # Qwen2.5-Omni Audio Encoder | |
| Standalone audio encoder extracted from [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) for [FlexiSLM](https://huggingface.co/FlexiSLM). | |
| This checkpoint keeps only `Qwen2_5OmniAudioEncoder` (`thinker.audio_tower.*`: conv layers, 32 transformer layers, `ln_post`, and `proj`). The Omni thinker LLM, talker, vision tower, and tokenizer are omitted. | |
| | Property | Value | | |
| | --- | --- | | |
| | Parameters | 640M | | |
| | Size | ~1.19 GiB (`bfloat16`) | | |
| | Output dim | 3584 | | |
| | Mel bins | 128 | | |
| - Paper: [arXiv:2606.31247](https://arxiv.org/abs/2606.31247) | |
| - FlexiSLM code: [AmphionTeam/FlexiSLM](https://github.com/AmphionTeam/FlexiSLM) | |
| ## Usage with FlexiSLM | |
| Required for both 7B and 0.5B FlexiSLM inference and training. Shared by all Stage 1 / Stage 2 / Stage 3 recipes. | |
| ### Download | |
| ```bash | |
| MODEL_ROOT="$PWD/models" | |
| hf download FlexiSLM/Qwen2_5-Omni-Audio_Encoder \ | |
| --local-dir "$MODEL_ROOT/Qwen2_5-Omni-Audio_Encoder" | |
| ``` | |
| With FlexiSLM `auto_download=True`, this encoder is fetched automatically together with the Stage 2 SLM, SenseVoice, and FlexiCodec assets. | |
| ### Inference config (Python) | |
| ```python | |
| from pathlib import Path | |
| from src.inference_flexislm import FlexiSLMInferenceConfig | |
| model_root = Path.cwd() / "models" | |
| config = FlexiSLMInferenceConfig( | |
| checkpoint="stage2_7B", # or "stage2_0.5B" | |
| model_path=str(model_root / "FlexiSLM-7B-Stage2"), | |
| qwen25o_encoder_path=str(model_root / "Qwen2_5-Omni-Audio_Encoder"), | |
| qwen25o_encoder_config_path=str( | |
| model_root / "Qwen2_5-Omni-Audio_Encoder/config.json" | |
| ), | |
| # ... FlexiCodec / SenseVoice / flow-matching paths ... | |
| ) | |
| ``` | |
| ### Training / YAML | |
| ```yaml | |
| use_qwen25omni_feature: true | |
| qwen25omni_encoder_path: models/Qwen2_5-Omni-Audio_Encoder | |
| qwen25omni_encoder_config_path: models/Qwen2_5-Omni-Audio_Encoder/config.json | |
| ``` | |
| Full TTS / ASR / audio QA / speech-to-speech examples: [FlexiSLM Inference Guide](https://github.com/AmphionTeam/FlexiSLM#inference). | |
| ## Load with Transformers | |
| Weights are loadable with `transformers.models.qwen2_5_omni.modeling_qwen2_5_omni.Qwen2_5OmniAudioEncoder`. | |
| ## License | |
| Apache 2.0, same as the source Qwen2.5-Omni-7B checkpoint. Copyright 2025 Alibaba Cloud. | |
| ## Citation | |
| If you use this encoder as part of FlexiSLM: | |
| ```bibtex | |
| @misc{li2026flexislmdynamiccontrollableframe, | |
| title={FlexiSLM: A Dynamic and Controllable Frame Rate Spoken Language Model}, | |
| author={Jiaqi Li and Chaoren Wang and Xiaohai Tian and Mingjie Chen and Xinyu Liang and Xu Li and Yufan Lin and Junwen Qiu and Jun Zhang and Lu Lu and Haizhou Li and Zhizheng Wu}, | |
| year={2026}, | |
| eprint={2606.31247}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.SD}, | |
| url={https://arxiv.org/abs/2606.31247}, | |
| } | |
| ``` | |