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Found 89 Skills
Transcribe audio and video files to text using a remote ASR service (Qwen3-ASR or OpenAI-compatible endpoint). Extracts audio from video, sends to configurable ASR endpoint, outputs clean text. Use 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, or has a meeting recording, lecture, interview, or screen recording to transcribe.
Use when deploying ANY machine learning model on-device, converting models to CoreML, compressing models, or implementing speech-to-text. Covers CoreML conversion, MLTensor, model compression (quantization/palettization/pruning), stateful models, KV-cache, multi-function models, async prediction, SpeechAnalyzer, SpeechTranscriber.
ElevenLabs speech-to-text with Scribe models and forced alignment via inference.sh CLI. Models: Scribe v1/v2 (98%+ accuracy, 90+ languages). Capabilities: transcription, speaker diarization, audio event tagging, word-level timestamps, forced alignment, subtitle generation. Use for: meeting transcription, subtitles, podcast transcripts, lip-sync timing, karaoke. Triggers: elevenlabs stt, elevenlabs transcription, scribe, elevenlabs speech to text, forced alignment, word alignment, subtitle timing, diarization, speaker identification, audio event detection, eleven labs transcribe
Find the right Deepgram documentation for any task. Use whenever someone needs help locating docs, understanding which API to use, or wants to ask questions about Deepgram. Covers all product areas: speech-to-text, text-to-speech, voice agents, audio intelligence, and self-hosted deployments.
Speech-to-text transcription using Whisper with word-level timestamps. Use when users ask to transcribe audio or video to text, generate subtitles, or recognize speech.
Clone a ready-to-run Deepgram demo app and start building on top of it. Use whenever someone wants a quick working demo, needs to prototype with Deepgram, or is starting a new project that uses speech-to-text, text-to-speech, voice agents, audio intelligence, or live streaming. Match the user's language, framework, and desired Deepgram feature to the right starter.
Deepgram API reference for speech-to-text, text-to-speech, voice agents, audio intelligence, and account management. Use whenever building with Deepgram APIs — REST or WebSocket. Covers authentication, all endpoints, query parameters, request/response schemas, and WebSocket message formats. Reference files are organized by domain: listen (STT), speak (TTS), agent (voice agents), read (text/audio intelligence), models, projects, auth, and self-hosted.
Terminal transcription of audio files and URLs with the Gladia CLI (gladia speech-to-text). Use when the user has gladia-cli installed, wants shell-based transcription, or asks an agent to transcribe audio then answer questions about the content. For audio intelligence features not available as CLI flags, use the SDK skills instead.
Transcribe audio to text using local whisper.cpp. Use when user wants to convert audio/video to text, get transcription, or speech-to-text.
Chunked sliding-window streaming speech-to-text via OpenAI Whisper HTTP API — compatible with local Faster-Whisper, Groq, and OpenRouter endpoints.
Add real-time voice conversations to a custom LLM, OpenClaw, or similar agent runtime with ElevenLabs Speech Engine. Use when building Speech Engine servers, WebSocket handlers, WebRTC browser clients, conversation token endpoints, interruption-aware streaming responses, or voice-enabled chat agents that connect a developer-owned LLM to ElevenLabs speech-to-text and text-to-speech.
Transcribe audio and video files to text using OpenAI Whisper or compatible speech-to-text APIs.