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A lightweight real-time captioning application for macOS, powered by whisper.cpp and DeepSeek-V3.

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// repository documentation

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## Real-time Subtitles

An open-source, lightweight macOS real-time subtitle application that provides high-quality bilingual streaming subtitles when listening to podcasts or watching videos. Utilizes Silero-Vad + Whisper for automatic speech recognition (ASR), and DeepSeek-V3 model API for subtitle translation.

[![YouTube Video](https://raw.githubusercontent.com/ZuoFuhong/subtitle/master/docs/subtitle_window.png)](https://www.youtube.com/watch?v=32uLnLP_coU)

Demo without window style:

[![YouTube Video](https://raw.githubusercontent.com/ZuoFuhong/subtitle/master/docs/subtitle_youtube.png)](https://www.youtube.com/watch?v=GUIAOTWzZTc)

Build the project:

```shell
git clone -b master git@github.com:ZuoFuhong/subtitle.git

# Disable Nano memory allocator, required for macOS only
export MallocNanoZone=0

mkdir -p build
cd build
cmake ..
make
```

Best experience in fullscreen mode:

```shell
# DeepSeek API_KEY (optional)
export DEEPSEEK_API_KEY=sk-xxxxx
# or
export OPENAI_API_KEY=sk-xxxxx

cd build

# Offline mode
./main -mode offline -f ../resources/model -m parakeet-tdt-0.6b-v2 -llm gpt-4.1-mini

# Server ASR mode
./main -mode server -s 9.135.97.184:8000
```

### 2. Speech-to-Text

[Whisper](https://github.com/openai/whisper) offers various model sizes, including small, medium, and large. Different model sizes vary in accuracy, speed, and computational resource requirements.

```shell
# Model download
# small.en supports English only, fast speed, suitable for real-time speech recognition
curl -L --output ggml-small.en.bin https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-small.en.bin

# medium.en supports English only, moderate speed, excellent accuracy, recommended for subtitle conversion
curl -L --output ggml-medium.en.bin https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-medium.en.bin
```

Note: Whisper models may hallucinate severely on long silences, background music, or noisy English audio.

Below is an example of running the ASR service offline using [whisper.cpp](https://github.com/ggerganov/whisper.cpp):

```text
whisper_init_with_params_no_state: use gpu    = 1
whisper_init_with_params_no_state: flash attn = 0
whisper_init_with_params_no_state: gpu_device = 0
whisper_init_with_params_no_state: dtw        = 0
whisper_init_with_params_no_state: backends   = 3
whisper_backend_init_gpu: using Metal backend
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1 Pro
ggml_metal_init: picking default device: Apple M1 Pro
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name:   Apple M1 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple7  (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)

And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
```

### 3. Voice Activity Detection

Voice Activity Detection (VAD) determines the presence of speech in audio signals by analyzing their features. It effectively distinguishes and separates speech from non-speech signals.
Here, VAD is used to segment speech portions for Whisper transcription, which greatly reduces hallucinations and improves the efficiency and accuracy of the speech processing system.

![doc](https://raw.githubusercontent.com/ZuoFuhong/subtitle/master/docs/jfk_waveform.png)

Below is the result from a commercial model running offline:

```json
{
	"sentences": [
		{"seId":"1", "seTime":1980, "sourceText":"AND SO MY FELLOW AMERICANS", "startTime":230, "endTime":2210},
		{"seId":"2", "seTime":900, "sourceText":"ASK NOT", "startTime":3290, "endTime":4190},
		{"seId":"3", "seTime":2020, "sourceText":"WHAT YOUR COUNTRY CAN DO FOR YOU", "startTime":5290, "endTime":7310},
		{"seId":"4", "seTime":2300, "sourceText":"ASK WHAT YOU CAN DO FOR YOUR COUNTRY", "startTime":8150, "endTime":10450}
	]
}
```

Use ffmpeg to split audio segments for playback verification:

```shell
ffmpeg -i jfk.wav -ss 00:00:00.230 -to 00:00:02.210 -acodec copy output1.wav
ffmpeg -i jfk.wav -ss 00:00:03.290 -to 00:00:04.190 -acodec copy output2.wav
ffmpeg -i jfk.wav -ss 00:00:05.290 -to 00:00:07.310 -acodec copy output3.wav
ffmpeg -i jfk.wav -ss 00:00:08.150 -to 00:00:10.450 -acodec copy output4.wav
```

Below are segments recognized in streaming mode by Silero-Vad + Whisper:

```shell
{"end":37376,"se_id":1,"start":5120,"text":"And so, my fellow Americans."}
{"end":86016,"se_id":2,"start":53760,"text":"Ask not."}
{"end":129024,"se_id":3,"start":87552,"text":"what your country can do for you."}
{"end":171008,"se_id":4,"start":131072,"text":"Ask what you can do for your country."}
```

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