Results for “automatic-speech-recognition”

Analyze text with Azure AI Language service for sentiment, entities, key phrases, language detection, PII redaction, and healthcare NLP using the Python SDK.

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Transcribes audio from URLs or local files to text using the Speech is Cheap API, with options for speaker diarization, timestamps, and multiple output formats.

Fast, accurate, and incredibly inexpensive automatic speech-to-text transcription service.

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features and classifying samples with machine learning models.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Transcribe audio files to text using the z-ai-web-dev-sdk, with CLI and SDK examples for single files, batches, and directories.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Transcribes audio from URLs or local files into text with speaker diarization, word timestamps, and multiple output formats via a command-line tool.

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.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

ASR: Whisper for Video Transcription

Converts text to speech and speech to text using fal.ai audio models.

Voice Agents

Analyze conversation intent and entities using the Azure AI Language Conversations Python SDK with best practices for authentication and error handling.

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

Comprehensive guide to asr. Master the concepts, implementation, best practices, and real-world applications of asr in professional environments.

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.

Text-to-speech and speech-to-text using fal.ai audio models

Conversational Pattern Restoration — Fix flat, robotic AI responses across any model and any personality. Restore YOUR natural conversational texture without triggering hype drift. Universal framework tested on 8+ models (Claude, GPT-4o, Grok, Gemini).

Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.

Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.

Transcribe and translate speech across 99 languages using OpenAI's Whisper model, with support for multiple model sizes, batch processing, and subtitle generation.

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.

Audit a codebase, UI, or copy for machine-generated tells across prose, visual/UI, code, and structure/IA, then produce a phased de-slop burndown. Use when the user says "feels AI-generated", "looks like AI slop", "reads like ChatGPT", "feels generic/soulless", or wants an authenticity/voice pass before launch.

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

Text-to-speech and speech-to-text using fal.ai audio models

Applies dialogue MA, portrait reframing, and loudness/sync QA to an interview or talking-head rough cut using canonical timeline metadata and a shared renderer.

Text-to-speech and speech-to-text using fal.ai audio models

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

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.

Text-to-speech and speech-to-text using fal.ai audio models

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

Detect and eliminate predictable AI writing tells from prose. Use when the user asks to "make this sound less like AI", "remove AI tells", "make it more human", "this sounds robotic", "clean up the writing", "too formal", "sounds generated", or requests a writing/copy review. Do NOT use for code — this skill targets prose, copy, and documentation only.

Generates speech, transcribes audio, clones voices, and builds real-time voice agents using ElevenLabs, OpenAI TTS, Whisper, and Vapi.