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speech-to-speech-model

realtime native speech-to-speech model

GPT realtime, Gemini Live

SystemTechniques · 23Primitives · 98 speech-to-speech-model Deterministic simulation testingdistributed · medium: Run the whole system in one thread under a seeded scheduler with simulated network, disk and clock, so every failure replays from its seed.›Primary-backup replicationdistributed · medium: One node orders all writes and ships them to standbys; the acknowledgement rule (none, one, all) sets what a crash loses, and an external arbiter decides who takes over.›Chunked streaming attentionmachine-learning · medium: Chunk-limited attention masks with cached left context so encoders (Conformer, Whisper-style) run streaming at bounded latencyData loading pipelinesmachine-learning · medium: Turn files on disk into shuffled, sharded, resumable GPU batches fast enough that the accelerator never waits.›Forced alignmentmachine-learning · medium: Align transcripts to audio for word timestamps (CTC/Viterbi alignment)Speaker diarizationmachine-learning · medium: Segment audio by speaker via embeddings and clusteringAcoustic echo cancellationmedia · medium: Adaptive filter (NLMS/frequency-domain) removing far-end echo from the microphone signalTransformer blockml-training · low: The decoder-only pre-norm block (attention plus MLP on a residual stream) behind every modern LLM, with exact shapes, parameter, FLOP and KV-cache counts.›Speaker embeddingsmachine-learning · low: Fixed-size speaker vectors (x-vector, ECAPA) for verification, diarization and voice cloningVoice activity detectionmachine-learning · low: Per-frame speech/non-speech decision with smoothing, from an energy threshold over a tracked noise floor up to small neural nets.›Vector quantizationcompression · low: Codebook and pyramid vector quantization of coefficient vectorsHdr histogramdata-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentilesProcess supervisionhardware-os · low: A parent process starts services, reaps their exits and restarts them under a delay and burst-limit policy, then stops them with SIGTERM before SIGKILL.›End of turn detectionmachine-learning · low: Deciding when a speaker has finished (silence plus prosody/semantic models) to start a reply with low latencyRotary position embedding (RoPE)machine-learning · low: Encode position by rotating pairs of query and key dimensions by position-dependent angles, so attention scores depend only on relative distance; the LLM default.›Text normalizationmachine-learning · low: Rewrite messy text into one canonical form for matchers and models, or into spoken words for speech output (and back), keeping a map to the original offsets.›Opus codecmedia · low: Opus audio codec: SILK/CELT modes, frame sizes, bitrate and FEC settings for real-time voiceContinuous batchingml-training · low: Schedule LLM inference per iteration, not per request; finished sequences leave and new ones join the running batch every step, which keeps the GPU full.›FlashAttentionml-training · low: Exact attention computed tile by tile in on-chip SRAM with online softmax, so the N×N score matrix never touches HBM; 2-4x faster, O(N) memory.›Mixed-precision trainingml-training · low: Run matmuls in bf16/fp16/fp8 on tensor cores while keeping master weights, optimizer state and reductions in fp32; about 2x speed, half the activation memory.›Paged KV cacheml-training · low: Store each sequence's attention keys/values in fixed-size blocks mapped through a block table, like virtual memory pages; near-zero fragmentation and copy-on-write sharing.›ZeRO and FSDP shardingml-training · low: Data parallelism that shards optimizer state, gradients and weights across ranks and gathers weights just in time; per-GPU model memory drops from 16Ψ to 16Ψ/N.›WebSocketsnetworking · low: Full-duplex message framing over one upgraded HTTP connection; the standard way to push real-time data to browsers.›+ 16 more techniques+ 82 more primitives

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