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Found 71 Skills
Transcribe audio to text with Whisper models via inference.sh CLI. Models: Fast Whisper Large V3, Whisper V3 Large. Capabilities: transcription, translation, multi-language, timestamps. Use for: meeting transcription, subtitles, podcast transcripts, voice notes. Triggers: speech to text, transcription, whisper, audio to text, transcribe audio, voice to text, stt, automatic transcription, subtitles generation, transcribe meeting, audio transcription, whisper ai
Transcribe audio and video files to text using OpenAI Whisper or compatible speech-to-text APIs.
Transcribe audio files to text using OpenAI Whisper
Build with OpenAI's stateless APIs - Chat Completions (GPT-5, GPT-4o), Embeddings, Images (DALL-E 3), Audio (Whisper + TTS), and Moderation. Includes Node.js SDK and fetch-based approaches for Cloudflare Workers. Use when: implementing chat completions with GPT-5/GPT-4o, streaming responses with SSE, using function calling/tools, creating structured outputs with JSON schemas, generating embeddings for RAG (text-embedding-3-small/large), generating images with DALL-E 3, editing images with GPT-Image-1, transcribing audio with Whisper, synthesizing speech with TTS (11 voices), moderating content (11 safety categories), or troubleshooting rate limits (429), invalid API keys (401), function calling failures, streaming parse errors, embeddings dimension mismatches, or token limit exceeded.
Expert skill for implementing speech-to-text with Faster Whisper. Covers audio processing, transcription optimization, privacy protection, and secure handling of voice data for JARVIS voice assistant.
Local speech-to-text with the Whisper CLI (no API key).
Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers dataset preparation, Whisper-based validation, training configuration, and ONNX export.
Turn meeting audio into three artifacts (a corrected transcript, an HTML visual canvas, and a Markdown summary with AI insights), transcribed locally with Whisper on any platform: no cloud, no external transcription app. Use when the user hands over a meeting recording or wants meeting notes from one ("处理这个录音", "会议纪要", "把录音转成文字", or an audio file lands in the configured drop-zone), wants an existing transcript corrected or cleaned ("处理 transcript", "转录纠错", "转写文本", or a .txt lands in the drop-zone), wants a recording pulled from their connected PLAUD account ("process my Plaud recording", "从 Plaud 拿"), asks for edits to a transcript, canvas, or summary this skill just produced, or asks for cross-meeting Insights on their communication patterns instead of processing one recording ("analyze my meetings", "how do I come across in meetings", "分析我的沟通模式", "复盘我最近的会议", "跨会议分析").
Build tone-adaptive captions from whisper transcripts. Detects script energy (hype, corporate, tutorial, storytelling, social) and applies matching typography, color, and animation. Supports per-word styling for brand names, ALL CAPS, numbers, and CTAs. Use when adding captions or subtitles to a HyperFrames composition.
OpenAI API via curl. Use this skill for GPT chat completions, DALL-E image generation, Whisper audio transcription, embeddings, and text-to-speech.
Understand video content locally using ffmpeg frame extraction and Whisper transcription. No API keys needed. Use when: (1) Understanding what a video contains, (2) Transcribing video audio locally, (3) Extracting key frames for visual analysis, (4) Getting video content without API keys.
Convert audio/video to text using Whisper, with support for word-level timestamps. Use this when users need speech-to-text conversion, audio-to-text transcription, video-to-text extraction, subtitle generation, transcribe audio, speech to text, generate subtitles, or speech recognition.