---
title: Streaming Zipformer
emoji: 👀
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
short_description: Streaming zipformer
---

# 🎙️ Real-Time Streaming ASR Demo (FastAPI + Sherpa-ONNX)

This project demonstrates a real-time speech-to-text (ASR) web application with:

* 🧠 [Sherpa-ONNX](https://github.com/k2-fsa/sherpa-onnx) streaming Zipformer model
* 🚀 FastAPI backend with WebSocket support
* 🎛️ Configurable browser-based UI using vanilla HTML/JS
* ☁️ Docker-compatible deployment (CPU-only) on Hugging Face Spaces

## 📦 Model

The app uses the bilingual (Chinese-English) streaming Zipformer model:

🔗 **Model Source:** [Zipformer Small Bilingual zh-en (2023-02-16)](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/zipformer-transducer-models.html#sherpa-onnx-streaming-zipformer-small-bilingual-zh-en-2023-02-16-bilingual-chinese-english)

Model files (ONNX) are located under:

```
models/zipformer_bilingual/
```

## 🚀 Features

* 🎤 **Real-Time Microphone Input:** capture audio directly in the browser.
* 🎛️ **Recognition Settings:** select ASR model and precision; view supported languages and model size.
* 🔑 **Hotword Biasing:** input custom hotwords (one per line) and adjust boost score. See [Sherpa-ONNX Hotwords Guide](https://k2-fsa.github.io/sherpa/onnx/hotwords/index.html).
* ⏱️ **Endpoint Detection:** configure silence-based rules (Rule 1 threshold, Rule 2 threshold, minimum utterance length) to control segmentation. See [Sherpa-NCNN Endpoint Detection](https://k2-fsa.github.io/sherpa/ncnn/endpoint.html).
* 📊 **Volume Meter:** real-time volume indicator based on RMS.
* 💬 **Streaming Transcription:** display partial (in red) and final (in green) results with automatic scrolling.
* 🛠️ **Debug Logging:** backend logs configuration steps and endpoint detection events.
* 🐳 **Deployment:** Dockerfile provided for CPU-only deployment on Hugging Face Spaces.

## 🛠️ Configuration Guide

### 🔑 Hotword Biasing Configuration

* **Hotwords List** (`hotwordsList`): Enter one hotword or phrase per line. These are words/phrases the ASR will preferentially recognize. For multilingual models, you can mix scripts according to your model’s `modeling-unit` (e.g., `cjkchar+bpe`).
* **Boost Score** (`boostScore`): A global score applied at the token level for each matched hotword (range: `0.0`–`10.0`). You may also specify per-hotword scores inline in the list using `:`, for example:

  ```
  语音识别 :3.5
  深度学习 :2.0
  SPEECH RECOGNITION :1.5
  ```
* **Decoding Method**: Ensure your model uses `modified_beam_search` (not the default `greedy_search`) to enable hotword biasing.
* **Applying**: Click **Apply Hotwords** in the UI to send the following JSON payload to the backend:

  ```json
  {
    "type": "config",
    "hotwords": ["..."],
    "hotwordsScore": 2.0
  }
  ```

(For full details, see the [Sherpa-ONNX Hotwords Guide](https://k2-fsa.github.io/sherpa/onnx/hotwords/index.html) ([k2-fsa.github.io](https://k2-fsa.github.io/sherpa/onnx/hotwords/index.html)).)

### ⏱️ Endpoint Detection Configuration

The system supports three endpointing rules borrowed from Kaldi:

* **Rule 1** (`epRule1`): Minimum duration of trailing silence to trigger an endpoint, in **seconds** (default: `2.4`). Fires whether or not any token has been decoded.
* **Rule 2** (`epRule2`): Minimum duration of trailing silence to trigger an endpoint *only after* at least one token is decoded, in **seconds** (default: `1.2`).
* **Rule 3** (`epRule3`): Maximum utterance length before forcing an endpoint, in **milliseconds** (default: `300`). Disable by setting a very large value.
* **Applying**: Click **Apply Endpoint Config** in the UI to send the following JSON payload to the backend:

  ```json
  {
    "type": "config",
    "epRule1": 2.4,
    "epRule2": 1.2,
    "epRule3": 300
  }
  ```

(See the [Sherpa-NCNN Endpointing documentation](https://k2-fsa.github.io/sherpa/ncnn/endpoint.html) ([k2-fsa.github.io](https://k2-fsa.github.io/sherpa/ncnn/endpoint.html)).)

## 🧪 Local Development

1. **Install dependencies**

```bash
pip install -r requirements.txt
```

2. **Run the app locally**

```bash
uvicorn app.main:app --reload --host 0.0.0.0 --port 8501
```

Open [http://localhost:8501](http://localhost:8501) in your browser.

[https://k2-fsa.github.io/sherpa/ncnn/endpoint.html](https://k2-fsa.github.io/sherpa/ncnn/endpoint.html)

## 📁 Project Structure

```
.
├── app
│   ├── main.py               # FastAPI + WebSocket endpoint, config parsing, debug logging
│   ├── asr_worker.py         # Audio resampling, inference, endpoint detection, OpenCC conversion
│   └── static/index.html     # Client-side UI: recognition, hotword, endpoint, mic, transcript
├── models/zipformer_bilingual/
│   └── ... (onnx, tokens.txt)
├── requirements.txt
├── Dockerfile
└── README.md
```

## 🔧 Credits

* [Sherpa-ONNX](https://github.com/k2-fsa/sherpa-onnx)
* [OpenCC](https://github.com/BYVoid/OpenCC)
* [FastAPI](https://fastapi.tiangolo.com/)
* [Hugging Face Spaces](https://huggingface.co/docs/hub/spaces)