Fliki

Converts written text into natural-sounding speech using neural text-to-speech models with support for multiple AI-generated voices and languages. The system processes input text through linguistic analysis, phoneme generation, and neural vocoding to produce high-quality audio output with controllable parameters like speed, pitch, and emotion. Voices are pre-trained on large speech datasets and can be selected from a library of synthetic personas or custom-cloned voices.

Transforms written scripts or descriptions into complete videos by automatically generating or sourcing visual content, applying transitions, and synchronizing audio narration. The system parses input text to identify key scenes, retrieves or generates matching visual assets (stock footage, AI-generated imagery, or user uploads), arranges them in sequence, applies visual effects and transitions, and syncs the generated voiceover to video timing. This end-to-end pipeline eliminates manual video editing steps.

Stores and manages brand assets (logos, color palettes, fonts, watermarks) in a centralized library, automatically applying them to generated videos for consistent branding. The system detects brand asset types, applies them to appropriate video regions (logo placement, color grading, font selection), and ensures consistency across all videos created by a user or team. Brand guidelines can be enforced to prevent off-brand content.

Analyzes input scripts for clarity, engagement, and video-friendliness, providing suggestions for improvement such as breaking long sentences, adding emphasis markers, improving pacing, or enhancing emotional impact. The system uses NLP to evaluate readability, identifies sections that may be difficult to visualize, suggests scene breaks, and can automatically rewrite scripts to be more suitable for video narration. This ensures scripts are optimized for TTS quality and visual adaptation.

Automatically translates video scripts and generates localized voiceovers in multiple target languages while maintaining audio-video synchronization. The system detects or accepts the source language, translates text content using neural machine translation, generates native-speaker-quality TTS in each target language, and adjusts video timing to accommodate different speech rates across languages. This enables single-source video content to reach global audiences without manual dubbing or subtitle work.

Automatically identifies key concepts in text scripts and retrieves or generates matching visual content from multiple sources (stock footage libraries, AI image generation models, user uploads). The system uses semantic understanding to match text descriptions to visual assets, applies relevance scoring, and selects the best matches for each scene. For gaps in stock footage, it can generate custom images using text-to-image models, ensuring visual continuity even for niche topics.

Automatically synchronizes audio narration, visual transitions, and on-screen text to create coherent video timing without manual timeline editing. The system analyzes audio duration, calculates optimal transition timing, adjusts visual asset display duration to match speech segments, and aligns subtitle timing to audio. This handles variable speech rates, language differences, and ensures smooth visual-audio alignment across the entire video.

Provides pre-designed video templates with customizable layouts, color schemes, fonts, and visual effects that automatically adapt to user content. Templates define regions for video, text, logos, and effects; the system maps generated content into these regions, applies consistent styling, and renders the final video. This enables rapid video creation with professional appearance without design skills, while maintaining brand consistency across multiple videos.

Enables creation of multiple videos from a list of scripts or descriptions in a single operation, with optional scheduling for staggered generation or publishing. The system queues multiple video generation requests, processes them sequentially or in parallel (depending on account tier), and can schedule output delivery or publishing to connected platforms. This is useful for content calendars, bulk content creation, and automated publishing workflows.

Automatically optimizes video output for specific social media platforms (YouTube, TikTok, Instagram, LinkedIn, etc.) by adjusting aspect ratio, duration, bitrate, codec, and subtitle placement to match platform requirements and best practices. The system detects target platform, applies platform-specific optimizations, and exports in the correct format and resolution. This eliminates manual re-encoding or resizing for different platforms.

Converts text scripts into visual storyboards by generating or retrieving images for each scene, displaying them in sequence with timing annotations and voiceover text. This provides a preview of the final video before rendering, allowing users to review visual-audio alignment, pacing, and scene transitions. The storyboard can be edited to adjust scene selection, timing, or visual assets before final video generation.

Automatically generates subtitles and captions from video audio using speech-to-text technology, with precise timing synchronization to audio. The system transcribes audio, detects speaker changes and natural pauses, formats captions for readability (line breaks, character limits), and exports in standard subtitle formats (SRT, VTT, WebVTT). Captions can be customized for accessibility (hearing-impaired) or social media (emoji, hashtags).