菜单
Use for audio processing nodes in dora. Triggers on: dora-microphone, dora-vad, dora-distil-whisper, dora-pyaudio, dora-kokoro-tts, microphone, VAD, voice activity, speech-to-text, STT, text-to-speech, TTS, whisper, kokoro, audio, speaker, silero, speech recognition, 麦克风, 语音识别, 语音合成, 音频, VAD
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use for dora CLI command questions. Triggers on: dora run, dora build, dora start, dora stop, dora new, dora up, dora destroy, dora list, dora ps, dora logs, dora check, dora graph, dora daemon, dora coordinator, CLI命令, 命令行, 运行数据流, 构建, 启动, 停止
Use for data recording and replay questions with dora. Triggers on: recording, replay, lerobot, data collection, dataset, rosbag, record data, play back, training data, HDF5, parquet, 数据记录, 数据回放, 数据收集, 训练数据
Use for dora-rs dataflow YAML configuration questions. Triggers on: dataflow.yml, dataflow.yaml, nodes:, inputs:, outputs:, timer, dora/timer, queue_size, env:, build:, path:, git:, branch:, tag:, restart_policy, send_stdout_as, operator:, operators:, YAML配置, 数据流配置, 节点配置, 输入输出, 定时器
Use for audio processing pipeline questions with dora. Triggers on: speech, TTS, STT, voice, audio, Whisper, Kokoro, text-to-speech, speech-to-text, voice activity, VAD, microphone, 语音, 音频, 文本转语音, 语音转文本, 语音识别
Use for robot control pipeline questions with dora. Triggers on: robot, arm, chassis, actuator, servo, motor, gripper, kinematics, FK, IK, URDF, joint, position control, velocity control, serial port, 机器人, 机械臂, 底盘, 执行器, 舵机, 电机, 运动学
Use for ML/Vision pipeline questions with dora. Triggers on: YOLO, detection, object detection, segmentation, tracking, VLM, SAM, camera, webcam, image processing, bbox, bounding box, rerun, visualization, 视觉, 目标检测, 分割, 跟踪, 摄像头, 图像处理
| name | hub-audio |
| description | Use for audio processing nodes in dora. Triggers on: dora-microphone, dora-vad, dora-distil-whisper, dora-pyaudio, dora-kokoro-tts, microphone, VAD, voice activity, speech-to-text, STT, text-to-speech, TTS, whisper, kokoro, audio, speaker, silero, speech recognition, 麦克风, 语音识别, 语音合成, 音频, VAD |
| globs | ["**/dataflow.yml","**/dataflow.yaml"] |
| source | https://github.com/dora-rs/dora-hub |
Microphone input, voice activity detection, speech-to-text, and text-to-speech
Microphone → VAD → Whisper STT → LLM → Kokoro TTS → Speaker
| Node | Install | Description |
|---|---|---|
| dora-microphone | pip install dora-microphone | Microphone input with VAD |
| dora-vad | pip install dora-vad | Silero voice activity detection |
| dora-distil-whisper | pip install dora-distil-whisper | Distil-Whisper STT |
| dora-kokoro-tts | pip install dora-kokoro-tts | Kokoro text-to-speech |
| dora-pyaudio | pip install dora-pyaudio | Audio playback |
Capture audio from microphone with built-in voice activity detection.
- id: microphone
build: pip install dora-microphone
path: dora-microphone
inputs:
tick: dora/timer/millis/100
outputs:
- audio # 16kHz Float32Array
# audio: Float32Array at 16kHz sample rate
metadata = {"sample_rate": 16000}
Silero Voice Activity Detection - filters audio to speech-only segments.
- id: vad
build: pip install dora-vad
path: dora-vad
inputs:
audio: microphone/audio # 8kHz or 16kHz
outputs:
- audio # truncated to speech only
Speech-to-text using Distil-Whisper for efficient transcription.
- id: whisper
build: pip install dora-distil-whisper
path: dora-distil-whisper
inputs:
input: vad/audio
outputs:
- text # StringArray
env:
TARGET_LANGUAGE: english # or other supported languages
# text: StringArray containing transcribed text
text = event["value"][0].as_py() # Get string
Efficient text-to-speech using Kokoro.
- id: tts
build: pip install dora-kokoro-tts
path: dora-kokoro-tts
inputs:
text: llm/text
outputs:
- audio # Float32Array
Audio playback through speakers.
- id: speaker
build: pip install dora-pyaudio
path: dora-pyaudio
inputs:
audio: tts/audio
macOS:
brew install portaudio
Linux:
sudo apt-get install portaudio19-dev python-all-dev
nodes:
# Microphone input
- id: microphone
build: pip install dora-microphone
path: dora-microphone
inputs:
tick: dora/timer/millis/100
outputs:
- audio
# Voice activity detection
- id: vad
build: pip install dora-vad
path: dora-vad
inputs:
audio: microphone/audio
outputs:
- audio
# Speech to text
- id: whisper
build: pip install dora-distil-whisper
path: dora-distil-whisper
inputs:
input: vad/audio
outputs:
- text
env:
TARGET_LANGUAGE: english
# Visualization
- id: rerun
build: pip install dora-rerun
path: dora-rerun
inputs:
transcription:
source: whisper/text
metadata:
primitive: "text"
nodes:
# Audio input
- id: microphone
build: pip install dora-microphone
path: dora-microphone
inputs:
tick: dora/timer/millis/100
outputs:
- audio
# VAD filtering
- id: vad
build: pip install dora-vad
path: dora-vad
inputs:
audio: microphone/audio
outputs:
- audio
# Speech to text
- id: whisper
build: pip install dora-distil-whisper
path: dora-distil-whisper
inputs:
input: vad/audio
outputs:
- text
# LLM processing
- id: llm
build: pip install dora-qwen
path: dora-qwen
inputs:
text: whisper/text
outputs:
- text
# Text to speech
- id: tts
build: pip install dora-kokoro-tts
path: dora-kokoro-tts
inputs:
text: llm/text
outputs:
- audio
# Audio output
- id: speaker
build: pip install dora-pyaudio
path: dora-pyaudio
inputs:
audio: tts/audio
import pyarrow as pa
import numpy as np
# Audio at 16kHz
sample_rate = 16000
audio_samples = np.array([...], dtype=np.float32)
# Send audio
audio_data = pa.array(audio_samples)
node.send_output("audio", audio_data, {"sample_rate": sample_rate})
audio = event["value"].to_numpy()
sample_rate = event["metadata"].get("sample_rate", 16000)
# List audio devices
python -c "import sounddevice; print(sounddevice.query_devices())"
# macOS
brew install portaudio
# Linux
sudo apt-get install portaudio19-dev