Feature Extraction
Transformers
Safetensors
sensevoice_encoder_portable
duplexjev
audio
asr-encoder
sensevoice
custom_code
Instructions to use adventists-ai-research/SenseVoice-Small-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adventists-ai-research/SenseVoice-Small-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="adventists-ai-research/SenseVoice-Small-Encoder", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("adventists-ai-research/SenseVoice-Small-Encoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from adventists-ai-research/SenseVoice-Small-Encoder: direct link, hf CLI and curl.
- Browser
- Download file 2.48 kB
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https://huggingface.co/adventists-ai-research/SenseVoice-Small-Encoder/resolve/main/README.md
- Command line
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hf download hf://adventists-ai-research/SenseVoice-Small-Encoder/README.md
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curl -L -o README.md https://huggingface.co/adventists-ai-research/SenseVoice-Small-Encoder/resolve/main/README.md
2.48 kB
| license: other | |
| license_name: funasr-model-license | |
| license_link: https://github.com/modelscope/FunASR/blob/main/MODEL_LICENSE | |
| library_name: transformers | |
| base_model: | |
| - FunAudioLLM/SenseVoiceSmall | |
| tags: | |
| - duplexjev | |
| - audio | |
| - asr-encoder | |
| - sensevoice | |
| # SenseVoice-Small-Encoder | |
| The audio encoder of [FunAudioLLM/SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall), packaged as a | |
| standalone `transformers` model (`trust_remote_code=True`) so that it can feed a frozen LLM through a DuplexJev | |
| connector. It needs no FunASR installation. The weights are those of SenseVoiceSmall, unchanged: the SANM encoder | |
| (50 + 20 blocks, d = 512), the task-query embeddings and the CMVN statistics from `am.mvn`. The CTC head is not included. | |
| What the model does inside `forward`: | |
| 1. SenseVoice's frontend: Kaldi fbank (80 bins, 25 ms / 10 ms, Hamming, dither 0), LFR (7 frames stacked, stride 6), | |
| CMVN. | |
| 2. The four task queries SenseVoice prepends (language = auto, event, emotion, text-norm = woitn). | |
| 3. The encoder. The four query frames are dropped from the output, so one output frame covers 60 ms of audio. | |
| The feature extractor only cuts 16 kHz audio into 10 ms frames of raw samples; the encoder computes the features. This | |
| keeps the Ultravox processor's length arithmetic unchanged (`encoder_ds_factor = 6`). | |
| Port check: greedy CTC decoding with SenseVoiceSmall's own CTC head on top of this encoder reproduces correct | |
| transcripts of Chinese and English test clips, in fp32 and bf16. | |
| Requires `torchaudio` (for the Kaldi fbank): `pip install torchaudio`. | |
| Used by the `DuplexJev-B[-Para]-SenseVoice-Small-*` connectors; `Decider.from_pretrained("adventists-ai/DuplexJev-...")` | |
| fetches it automatically. Source of the port: [github.com/adventists-ai/duplexjev](https://github.com/adventists-ai/duplexjev). | |
| ## License and attribution | |
| The weights are **SenseVoiceSmall by Alibaba Group (Tongyi Lab / FunAudioLLM)**, redistributed under the | |
| [FunASR Model Open Source License v1.1](https://github.com/modelscope/FunASR/blob/main/MODEL_LICENSE) (copy in | |
| `MODEL_LICENSE`), which requires attributing the source and author and keeping the model name. The model is provided | |
| by its authors for reference and learning; see the licence for the full terms. The SANM layer code is copied from | |
| [FunASR](https://github.com/modelscope/FunASR) (MIT). The packaging code (feature extractor, wrapper) is Apache-2.0. | |
| Please cite SenseVoice / FunAudioLLM when you use it. | |