| --- |
| language: en |
| tags: |
| - speech-recognition |
| - asr |
| - speaker-diarization |
| - audio-tagging |
| - speech-enhancement |
| - onnx |
| - sherpa-onnx |
| - flutter |
| - android |
| library_name: sherpa-onnx |
| pipeline_tag: automatic-speech-recognition |
| --- |
| |
| # Mum's Ears — ONNX Model Repository |
|
|
| Central model store for Mum's Ears, an Android speech-to-text app built with Flutter and [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx). |
|
|
| All models are downloaded at runtime with SHA-256 checksum verification. |
|
|
| --- |
|
|
| ## Folder Structure |
| mums-ears-models/ ├── en/ # English streaming ASR + punctuation ├── de/ # German streaming ASR ├── es/ # Spanish streaming ASR ├── fr/ # French streaming ASR ├── zh/ # Chinese streaming ASR ├── qwen3-asr/ # Qwen3-ASR (offline, 938 MB) ├── audio_tagging/ # CED Tiny audio event classification ├── denoiser/ # DPDFNet8 speech enhancement ├── speaker_embedder/ # CAM++ speaker embedding └── pyannote_model.onnx # Speaker diarization segmentation |
| |
| |
| --- |
| |
| ## Model Inventory |
| |
| ### Streaming ASR (per-language) |
| |
| Each language folder contains a streaming Zipformer transducer model fine-tuned for real-time captioning. |
| |
| | File | Size | Purpose | |
| |------|------|---------| |
| | `encoder.onnx` | 70 MB (154 MB zh) | Acoustic encoder | |
| | `decoder.onnx` | 1 MB (5 MB zh) | Prediction decoder | |
| | `joiner.onnx` | 1 MB | Joint network | |
| | `tokens.txt` | < 1 MB | Token vocabulary | |
| |
| **Languages:** `en`, `de`, `es`, `fr`, `zh` |
| |
| ### Punctuation (English) |
| |
| | File | Size | Purpose | |
| |------|------|---------| |
| | `en/model_punct.int8.onnx` | 7 MB | Online punctuation model (int8 quantized) | |
| | `en/bpe_punct.vocab` | < 1 MB | BPE vocabulary | |
|
|
| ### Speaker Diarization |
|
|
| | File | Size | Purpose | Original Source | |
| |------|------|---------|-----------------| |
| | `pyannote_model.onnx` | 14 MB | Speaker segmentation (pyannote 3.0) | [csukuangfj/sherpa-onnx-pyannote-segmentation-3-0](https://huggingface.co/csukuangfj/sherpa-onnx-pyannote-segmentation-3-0) | |
| | `speaker_embedder/<filename>` | 27 MB | CAM++ speaker embeddings (zh+en, 192-dim) | [k2-fsa/sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-recongition-models) | |
|
|
| ### Audio Tagging |
|
|
| | File | Size | Purpose | Original Source | |
| |------|------|---------|-----------------| |
| | `audio_tagging/model.int8.onnx` | 27 MB | CED Tiny audio event classifier (int8) | [k2-fsa/sherpa-onnx-ced-tiny-audio-tagging-2024-04-19](https://huggingface.co/k2-fsa/sherpa-onnx-ced-tiny-audio-tagging-2024-04-19) | |
| | `audio_tagging/class_labels_indices.csv` | < 1 MB | Audio event class labels | Same | |
|
|
| ### Speech Enhancement (Denoiser) |
|
|
| | File | Size | Purpose | Original Source | |
| |------|------|---------|-----------------| |
| | `denoiser/dpdfnet8.onnx` | 14 MB | DPDFNet8 speech denoiser | [k2-fsa/sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx/releases/download/speech-enhancement-models/dpdfnet8.onnx) | |
|
|
| ### Qwen3-ASR (Offline) |
|
|
| Large offline ASR model (int4 quantized, ~938 MB total). Optional — requires ≥ 4 GB free RAM. |
|
|
| | File | Size | Purpose | |
| |------|------|---------| |
| | `qwen3-asr/encoder.int8.onnx` | ~400 MB | Audio encoder | |
| | `qwen3-asr/decoder.int8.onnx` | ~400 MB | Language model decoder | |
| | `qwen3-asr/conv_frontend.onnx` | ~100 MB | Convolutional frontend | |
| | `qwen3-asr/tokenizer/vocab.json` | < 1 MB | Tokenizer vocabulary | |
| | `qwen3-asr/tokenizer/merges.txt` | < 1 MB | BPE merge rules | |
| | `qwen3-asr/tokenizer/tokenizer_config.json` | < 1 MB | Tokenizer config | |
|
|
| --- |
|
|
| ## Attribution |
|
|
| Models in this repository are sourced from the following open-source projects: |
|
|
| - **[k2-fsa/sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx)** — Streaming ASR, speaker embedding, diarization, punctuation, denoiser, audio tagging (Apache-2.0) |
| - **[pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0)** — Speaker diarization segmentation (MIT) |
| - **[Qwen/Qwen3-ASR](https://huggingface.co/Qwen/Qwen3-ASR)** — Offline ASR model (Apache-2.0) |
| - **[frankyoujian/Edge-Punct-Casing](https://github.com/frankyoujian/Edge-Punct-Casing)** — Punctuation model ([paper](https://arxiv.org/abs/2407.13142)) |
|
|
| The "kroko" streaming ASR models (en/de/es/fr) are custom fine-tuned variants. The Chinese streaming model is int8-quantized. |
|
|
| --- |
|
|
| ## Download & Verification |
|
|
| Models are downloaded by the app's built-in download manager with: |
|
|
| - **Resumable downloads** via HTTP Range headers (`.part` files) |
| - **SHA-256 checksum verification** before accepting any file |
| - **Mirror fallback** — if the primary URL fails, the original source URL is tried automatically |
| - **WiFi-only mode** (default, toggleable) |
|
|
| All checksums are defined in `lib/services/model_checksums.dart`. |
