daymade-audio

Transcribes audio and video files to text using Qwen3-ASR. Supports two modes — local MLX inference on macOS Apple Silicon (no API key, 15-27x realtime) and remote API via vLLM/OpenAI-compatible endpoints. Auto-detects platform and recommends the best path. Triggers when the user wants to transcribe recordings, convert audio/video to text, do speech-to-text, or mentions ASR, Qwen ASR, 转录, 语音转文字, 录音转文字. Also triggers for meeting recordings, lectures, interviews, podcasts, screen recordings, or any audio/video file the user wants converted to text.

Transcribe audio with StepFun's stepaudio-2.5-asr — an SSE endpoint (NOT /v1/audio/transcriptions) with 32K context, ~85-101x RTF on long audio, and a single-call ceiling around 30 minutes (no client-side chunking). Use when transcribing Chinese / English audio with StepFun, when long-form recordings (5-30 min) need to land in one request, when migrating from step-asr / step-asr-1.1, or when hitting the misleading `model stepaudio-2.5-asr not supported` error (which actually means wrong endpoint). Triggers on 阶跃 ASR, StepFun ASR, stepaudio-2.5-asr, 转录, 语音识别, 长音频转写, 语音转文字. For TTS with the sibling stepaudio-2.5-tts model, use the stepfun-tts skill instead.

Corrects speech-to-text transcription errors using dictionary rules and AI-powered analysis. Builds personalized correction databases that learn from each fix. Triggers when working with ASR/STT output containing recognition errors, homophones, garbled technical terms, or Chinese/English mixed content. Also triggers on requests to clean up meeting notes, lecture transcripts, interview recordings, or any text produced by speech recognition. Use this skill even when the user just says "fix this transcript" or "clean up these meeting notes" without mentioning ASR specifically.

Transforms raw meeting transcripts into high-fidelity, structured meeting minutes with iterative review for completeness. This skill should be used when (1) a meeting transcript is provided and meeting minutes, notes, or summaries are requested, (2) multiple versions of meeting minutes need to be merged without losing content, (3) existing minutes need to be reviewed against the original transcript for missing items, (4) transcript has anonymous speakers like "Speaker 1/2/3" that need identification. Features include: speaker identification via feature analysis (word count, speaking style, topic focus) with context.md team directory mapping, intelligent file naming from content, integration with transcript-fixer for pre-processing, evidence-based recording with speaker quotes, Mermaid diagrams for architecture discussions, multi-turn parallel generation to avoid content loss, and iterative human-in-the-loop refinement.

Generate Chinese / Japanese speech with StepFun's stepaudio-2.5-tts — Contextual TTS that replaces step-tts-2's `voice_label` with natural-language `instruction` (≤200 chars) plus inline `()` parentheses for句内 prosody. Use when the user wants emotional / prosody control over voice synthesis (whisper, pause, stress, mood pivot mid-sentence), batch-generates game / app voice lines, migrates from `step-tts-2` (the `voice_label → instruction` breaking change), or hits StepFun's stricter 2.5-era censorship (死/消失/political terms). Triggers on 阶跃 TTS, StepAudio 合成, 语音合成, 配音, 文本转语音, TTS 升级, 迁移 step-tts-2. For transcription with the sibling stepaudio-2.5-asr model, use the stepfun-asr skill instead.