Results for “speech”

Transcribes audio from URLs or local files into text using a low-cost speech-to-text API, with options for speaker diarization, word timestamps, and multiple output formats.

Transcribe audio to text using ElevenLabs Scribe and Whisper models via the inference.sh CLI, supporting timestamps, speaker diarization, translation, and multi-language transcription.

Create AI-powered podcasts and audio content using text-to-speech, music generation, and audio editing via the inference.sh CLI.

Generates text, images, video, speech, and music, and performs web searches via the MiniMax AI platform using the mmx terminal CLI.

Local speech-to-text with the Whisper CLI (no API key).

Local text-to-speech via sherpa-onnx (offline, no cloud)

Local speech-to-text with the Whisper CLI (no API key).

Local speech-to-text with the Whisper CLI (no API key).

Local text-to-speech via sherpa-onnx (offline, no cloud)

Local speech-to-text with the Whisper CLI (no API key).

Enables voice-driven invocation of Maxim's capabilities via hotword routing, intent classification, and decision capture, with graceful fallback when the voicemode plugin is absent.

Generates images, videos, and audio using fal.ai models via MCP, covering text-to-image, text/image-to-video, text-to-speech, and video-to-audio.

Search, explore, and run fal.ai generative AI models for image, video, audio, and 3D generation, including queue management and file uploads.

Generates images, videos, and audio using fal.ai models via MCP, covering text-to-image, text/image-to-video, text-to-speech, and video-to-audio.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Build server-side call automation workflows with Azure Communication Services Call Automation Java SDK, including IVR systems, call routing, recording, DTMF recognition, text-to-speech, and AI-powered call flows.

Generates images, videos, and audio using fal.ai models via MCP, covering text-to-image, text/image-to-video, text-to-speech, and video-to-audio.

Generate natural AI voices, text-to-speech, and voice synthesis using the inference.sh CLI with models like Inworld TTS, ElevenLabs, and Kokoro TTS for voiceovers, audiobooks, podcasts, and more.

Search, explore, and run fal.ai generative AI models for image, video, audio, and 3D generation, including schema lookup, job submission, status polling, result retrieval, and file uploads.

Generate images, videos, and audio using fal.ai models via MCP tools, with support for text-to-image, text/image-to-video, text-to-speech, and video-to-audio.

Transcribe audio files to text using local speech recognition. Triggers on: "转录", "transcribe", "语音转文字", "ASR", "识别音频", "把这段音频转成文字".

Use quando o usuário solicita narração em texto-para-fala, voiceovers de acessibilidade, prompts de áudio ou geração em lote via OpenAI Audio API; execute a CLI incluída (`scripts/text_to_speech.py`) com vozes integradas e requer `OPENAI_API_KEY` para chamadas diretas. Criação de vozes customizadas está fora do escopo.

Text-to-speech conversion using GLM-TTS service via the `uvx zai-tts` command for generating audio from text. Use when (1) User requests audio/voice output with the "tts" trigger or keyword. (2) Content needs to be spoken rather than read (multitasking, accessibility, podcast, driving, cooking). (3) Using pre-cloned voices for speech.

Transcribe audio files to text with optional speaker diarization and known-speaker hints using OpenAI models.

Build real-time voice AI applications with bidirectional WebSocket communication using the Azure AI Voice Live SDK for .NET.

Generate multi-speaker dialogue audio with different voices in a single file using the inference.sh CLI.