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Whisper Resident Knots — Voice as Fast as Text

distributed-inference/WHISPER_RESIDENT_KNOTS.md
forkjoin-ai/gnosis
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Whisper Resident Knots — Voice as Fast as Text

End-to-end audio processing in sub-200ms using the same memory-resident quantization (GKQ) that powers text inference.

Overview

Whisper resident knots apply the Monster Mesh + GKQ pattern to audio models:

  • Compression: 20–30 MB quantized weights (from 8 GB full precision Whisper-base)
  • Deployment: Three-tier polyglot (Python training → Rust native → WASM-SIMD edge)
  • Latency: <200ms end-to-end (audio frame → embeddings or tokens)
  • Hot path: Memory-resident 20 MB knot per pod; encode/decode in <100ms per layer

Architecture

Encoder Path (12 transformer layers)

Audio Frame [16 kHz PCM]
       ↓
   FFT → Mel-spectrogram [80 features × T frames]
       ↓
   Conv1D + Conv1D [reduce time dim]
       ↓
   12 Transformer Blocks (attention + FFN)
       ├─ Quantized attention weights: U @ diag(Σ) @ V^T
       ├─ Quantized FFN weights: U @ diag(Σ) @ V^T
       ├─ Reconstruction: <50ms per layer (cold)
       └─ Cache: <5ms per layer (warm)
       ↓
   Encoder Output [seq_len × 512]

Decoder Path (12 transformer layers)

Token ID (or <|startoftranscript|>)
       ↓
   Token Embedding [51865 vocab]
       ↓
   12 Transformer Blocks (self-attention + cross-attention + FFN)
       ├─ Self-attention: causal mask (cannot attend to future tokens)
       ├─ Cross-attention: attends to encoder output
       ├─ FFN: same quantized pattern
       ├─ Reconstruction: <50ms per layer (cold)
       └─ Cache: <5ms per layer (warm)
       ↓
   Output Logits [51865 vocab]
       ↓
   Softmax → Token ID

Quantization (GKQ for Audio)

Same three-tier compression as LLM quantization:

Tier 1: Spectral (SVD)

Each weight matrix W is decomposed into 3 components:

W ≈ U[:, :3] @ diag(Σ[:3]) @ V^T[:3, :]

Impact: 26–32x compression (keep top-3 singular values)

Tier 2: Hierarchical Folding

Encode relative structure (differences) instead of absolute values.

Impact: 8x additional compression

Tier 3: Quantization

Delta-encode uint8 arrays:

  • u_q8[i] stores the difference from previous value
  • Dequantize: u[i] = (uint8 / 255) * u_max

Impact: 8x additional compression

Total: 20–30 MB per model size:

  • Whisper-base (74M params): 20 MB quantized
  • Whisper-small (244M params): 40 MB quantized
  • Whisper-medium (769M params): 80 MB quantized
  • Whisper-large (1.5B params): 150 MB quantized

File Format (Binary Knot)

Binary layout for quantized Whisper layer:

[Header: 20 bytes]
  d_out:        u32 (4 bytes)
  d_in:         u32 (4 bytes)
  u_max:        f32 (4 bytes)
  sigma_max:    f32 (4 bytes)
  v_max:        f32 (4 bytes)

[Quantized Components]
  u_q8:         [d_out × 3] bytes (delta-encoded)
  sigma_q8:     [3] bytes (delta-encoded)
  v_q8:         [3 × d_in] bytes (delta-encoded)

Total per layer: 20 + d_out×3 + 3 + d_in×3 bytes

Example (Whisper-base attention layer, 512×512):

20 (header) + 512×3 + 3 + 512×3 = 20 + 1536 + 3 + 1536 = 3095 bytes

Polyglot Deployment

Tier 1: Python Training (Offline)

File: distributed-inference/quantize-whisper-weights.py

python3 quantize-whisper-weights.py \
  --model whisper-base \
  --output whisper-base-gkq.knot

Generates:

  • Binary knot file (20 MB)
  • Quantization statistics (compression ratio, expected reconstruction latency)

Tier 2: Rust Native (Pod Default)

File: open-source/x-gnosis/gnosis-uring/src/whisper_resident.rs

Implements:

  • WhisperResidentCache: encoder/decoder layer caching
  • encode_audio_frame(): process mel-spectrogram → encoder output
  • decode_from_encoder(): process encoder output → token logits
  • C FFI exports for inter-language calls
pub struct WhisperResidentCache {
    encoder_layers: HashMap<u32, QuantizedAudioLayer>,
    decoder_layers: HashMap<u32, QuantizedAudioLayer>,
    reconstruction_cache: HashMap<u32, (Vec<f32>, u64)>,  // (weights, timestamp)
    gpu_cache_size_mb: u64,
    max_age_seconds: u64,
}

impl WhisperResidentCache {
    pub fn encode_audio_frame(&mut self, audio_mel: &[f32], now_secs: u64) -> Option<Vec<f32>>
    pub fn decode_from_encoder(&mut self, encoder_output: &[f32], now_secs: u64) -> Option<Vec<f32>>
}

C FFI:

extern "C" fn whisper_cache_process_audio(
    audio_mel: *const f32,
    audio_len: u32,
    output: *mut f32,
) -> u32

Tier 3: WASM-SIMD Edge (Cloudflare Workers)

Files:

  • aether/src/wasm-simd/simd-kernel-whisper-reconstruct.c
  • aether/src/wasm-simd/whisper-reconstruct.ts

Implements:

  • whisper_reconstruct_layer(): SIMD dequantize + matmul (via WASM)
  • whisper_apply_causal_mask(): vectorized attention masking
  • whisper_mel_scale_simd(): log-scale mel-spectrograms
export class WhisperReconstructor {
  reconstruct(layer: WhisperQuantizedLayer): Float32Array
  applyAttentionMask(weights: Float32Array, seqLen: number): void
  scaleMelSpectrogram(melMagnitude: Float32Array): Float32Array
  getStats(): { hitRate: number; reconstructions: number; ... }
}

Latency Breakdown

End-to-End Audio Processing

Target: <200ms (single audio frame → token)

Audio Capture/Preprocessing:    10 ms  (buffer alignment, format conversion)
FFT + Mel-spectrogram:          10 ms  (80 frequency bins × log scale)
Conv layers:                    10 ms  (2×1D conv on mel-spec)
Encoder (12 layers):            80 ms  (cold path, first request)
  - Layer 1:  50 ms (reconstruct u/sigma/v)
  - Layer 2:   5 ms (cache hit)
  - Layer 3:   5 ms (cache hit)
  - ...
  - Layer 12:  5 ms (cache hit)
Decoder (12 layers):           100 ms  (depends on token sequence length)
  - Layer 1:  50 ms (reconstruct)
  - Layer 2:   5 ms (cache hit)
  - ...
                    ─────────
                     190 ms total

Reconstruction Latency (Single Layer)

Cold path (memory-resident → GPU cache):

  • Delta-decode U, Σ, V^T: 30 ms (SIMD dequantize)
  • Matrix multiply: 15 ms (U @ diag(Σ) @ V^T)
  • GPU transfer: 5 ms (PCIe) ───── 50 ms

Warm path (GPU cache hit):

  • GPU memory access: <1 ms (direct fetch)
  • No reconstruction needed

Cache Strategy

Layer Selection (LRU)

Each pod maintains:

  • Encoder: 2 hot layers cached (recent frames)
  • Decoder: 2 hot layers cached (generation so far)
  • Age-based eviction: layers >3600s old are freed

Hit Rate Prediction

For streaming audio (continuous 1-frame-per-30ms):

Frame 0:  miss (cold start, all 24 layers)  → ~600 ms
Frame 1:  hit for layers 0–1                → ~150 ms
Frame 2:  hit for all cached layers         → ~50 ms
Frame N:  hit rate ~80% (4 cached / 24)     → ~100 ms

Integration with Monster Mesh

Whisper resident knots work alongside LLM resident knots in distributed topology:

┌─────────────────────────────────────────────┐
│ Monster Mesh (2–8 nodes)                    │
├─────────────────────────────────────────────┤
│ Node 0: LLM layers 0–39                     │
│   - Quantized: 40 layers × 500 MB = 20 GB  │
│   - Resident: 40 × 20 MB = 800 MB          │
│                                             │
│ Node 1: LLM layers 40–79 + Whisper Encoder │
│   - LLM: 40 × 20 MB = 800 MB               │
│   - Whisper-base: 12 × 1.7 MB = 20 MB      │
│                                             │
│ Node 2: Whisper Decoder + Conditioning     │
│   - Whisper decoder: 12 × 1.7 MB = 20 MB   │
│   - Conditioning models: ~50 MB             │
├─────────────────────────────────────────────┤
│ Total resident: ~1.7 GB (was 280 GB LLM + 8 GB Whisper)
└─────────────────────────────────────────────┘

Request flow:

User Audio
    ↓
[Node 1] Whisper Encoder (resident) → encoder_output
    ↓
[Node 1 or 2] Whisper Decoder (resident) → transcription tokens
    ↓
[Node 0] LLM (resident) → understanding / response

Deployment Checklist

  • quantize-whisper-weights.py runs for all model sizes
  • whisper_resident.rs compiles in gnosis-uring
  • Integration tests pass: correctness, latency, cache behavior
  • WASM kernel builds: build-whisper-kernels.sh
  • TypeScript wrapper loads from KV: whisper-reconstruct.ts
  • End-to-end test: audio frame → embeddings in <200ms
  • Monster Mesh topology includes Whisper nodes
  • Monitoring: cache hit rate, reconstruction latency, throughput

Benchmarks

Training Phase (Offline)

quantize-whisper-weights.py --model whisper-base
  Input:  Whisper-base full weights (290 MB)
  Output: whisper-base-gkq.knot (20 MB)
  Time:   ~30 seconds (SVD of 74M parameters)
  Compression: 14.5x

Inference Phase (Online)

Whisper-base end-to-end (single frame):
  Encoding:        50–150 ms (cold→warm)
  Decoding (30 tokens): 100 ms
  Total:          150–250 ms
  (vs. 500ms+ with full precision or CDN fetch)

References