OpenAI: GPT Audio

Converts input text to natural-sounding audio output using an upgraded neural decoder architecture that maintains consistent voice characteristics across multiple utterances. The model applies voice embedding techniques to preserve speaker identity and prosody patterns, enabling multi-turn conversations with stable vocal properties. Supports streaming output for real-time audio generation without waiting for full synthesis completion.

Transcribes audio input to text using a Whisper-based architecture enhanced with speaker diarization capabilities that identify and label different speakers in multi-speaker audio. The model processes audio frames through a sequence-to-sequence transformer decoder that outputs both transcribed text and speaker turn boundaries, enabling conversation analysis and meeting minutes generation. Supports variable audio lengths up to 25MB and multiple audio formats through unified preprocessing pipeline.

Translates spoken audio from one language to another while preserving the original speaker's voice characteristics, accent patterns, and emotional tone. The system chains speech-to-text transcription, text translation, and voice-preserving TTS synthesis, using speaker embedding extraction from the source audio to guide the target language synthesis. Supports 99+ language pairs with automatic language detection on input audio.

Analyzes audio input to detect and flag harmful content including hate speech, explicit language, violence references, and policy violations using a fine-tuned classifier trained on moderation guidelines. The system transcribes audio, applies multi-modal safety checks (combining acoustic features and semantic content), and returns confidence scores for each violation category. Supports custom policy definitions and threshold tuning for different use cases.

Analyzes audio input to detect speaker emotional state, sentiment polarity, and engagement level using acoustic feature extraction combined with semantic content analysis. The system extracts prosodic features (pitch, tempo, energy), voice quality markers (breathiness, tension), and transcribed text sentiment, then fuses these signals through a multi-modal classifier to output emotion labels and confidence scores. Supports fine-grained emotion categories (joy, anger, frustration, confusion, etc.) and speaker engagement metrics.

Processes continuous audio streams with sub-second latency using a streaming decoder architecture that processes audio frames incrementally without buffering entire audio files. The system maintains state across frame boundaries to preserve context for speaker diarization and emotion detection, enabling live transcription, translation, and moderation of audio feeds. Supports WebSocket connections for bidirectional streaming and automatic reconnection with state recovery.