The Problem: Automating Quality Assurance (QA) and localized error correction (Audio Inpainting) during the generation of long-form audio using LLM-based TTS models.
Target Audience: B2B segment (audiobook publishers, game-dev dubbing studios, EdTech), indie content creators (AI podcasters).
The Pain Point: Modern SOTA synthesis models (Voicebox, F5-TTS, CosyVoice) sound incredibly natural but lack stability. When processing long texts, they inevitably hallucinate: skipping words, swallowing word endings, generating codec artifacts, or misinterpreting intonation. Currently, an audio editor must manually listen to 100% of the generated material, identify flaws, change the seed, regenerate chunks, and splice them together in a Digital Audio Workstation (DAW). This makes scaling AI voiceovers slow, tedious, and cost-ineffective.
The Proof of Concept (PoC) will showcase a closed-loop generation pipeline:
- The user inputs a complex paragraph of text (up to 1000 characters) and selects a voice.
- A council of agents is triggered. The UI visualizes the log of their "negotiations":
- Director Agent: Analyzes and marks up the text (emotions, pauses).
- Actor Agent: Generates the initial audio draft.
- Critic Agent: Transcribes the audio, compares it with the source text, and identifies errors (e.g., a skipped word).
- Editor Agent: Seamlessly regenerates only the flawed segment (2-3 seconds) using latent inpainting.
- The output is a flawless audio file (WER = 0) delivered "right the first time" without human intervention.
- Real-time streaming: The system operates in an asynchronous batch mode. Latencies < 500 ms are not supported, as agent reflection requires processing time.
- Zero-shot Voice Cloning (User Voice): To mitigate deepfake generation risks, the PoC will strictly use pre-installed (whitelisted), safe voices.
- SFX Generation: Music, singing, and background noises are out of scope.