AI Radio Labs
Pretrained & instruction-tuned foundation models for broadcast audio
7B-parameter large language models with retrieval-augmented generation, fine-tuned on 47K+ hours of professional radio for real-time audio intelligence
Pretrained · Instruction-Tuned · RAG-Enabled
Parameters
7B
Dense transformer
Training Data
47K+
Hours of broadcast audio
Context Window
128K
Token context length
Model Status
Stable
Latest checkpoint
Research Capabilities
End-to-end transformer pipeline from pretraining through RLHF alignment, purpose-built for broadcast audio understanding
Harmonic Flow Engine
Multi-head attention layers with spectral embeddings for temporal pattern detection across broadcast audio sequences
Voice Personality Synthesis
Speaker-conditioned decoder with LoRA adapters for zero-shot voice cloning and prosody-aware text-to-speech generation
RAG-Powered Audio Search
Retrieval-augmented generation with vector embeddings over broadcast archives for semantic search and grounded content generation
Adaptive Flow Programming
Reinforcement learning from human feedback (RLHF) for real-time content sequencing with chain-of-thought reasoning over scheduling constraints
Content Intelligence Network
Multi-modal embedding pipeline with cross-attention fusion layers for joint audio-text representation learning across broadcast formats
Training Infrastructure
Mixed-precision training with DeepSpeed ZeRO-3 and FlashAttention-2 for efficient large-scale pretraining
Cloud GPU Cluster
8x A100 80GB
NVLink-connected SXM4 instances for distributed pretraining with bf16 mixed precision
Distributed Training
DeepSpeed ZeRO Stage 3 with tensor parallelism, gradient checkpointing, and FSDP for memory-efficient 7B parameter training
Labs Access
Access our pretrained and instruction-tuned models, RAG pipelines, and inference APIs. Available to approved broadcast partners and research institutions.