Results for “speech-recognition”

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

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.

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

Packages an existing talking-head, interview, or podcast video with timed, designed graphic overlay cards—kinetic titles, lower-thirds, data callouts, quotes, side panels, picture-in-picture—synced to the transcript, on a 16:9, 9:16, or 4:5 canvas.

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

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

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

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

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

Open-Vocabulary Object Detection Using Captions

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

从真实的帖子、文章、发布说明、文档或网站文案中构建基于源材料的写作风格档案，然后在内容、外展和社交工作流中重复使用该档案。当用户希望保持声音一致性而不使用通用的AI写作套路时使用。

ASR: Whisper for Video Transcription

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

Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings.

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

Voice Agents

Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings.

Run state-of-the-art machine learning models directly in JavaScript/TypeScript across browsers and server-side runtimes using Transformers.js.

Bootstraps a clinical ASR evaluation environment by verifying NVIDIA_API_KEY, installing Python dependencies, and running a smoke test against hosted TTS/ASR services.