fb103ac5ae
Every registered string now lives in one table (codecs/registry.rs) with its kind, status, media class, registering SPEC section, edition and registration date. `wai registry export` writes registry/capabilities.json, its JSON Schema, the generated table in REGISTRY.md and vocab v2; --check fails on drift. vocab v1 stays stable. A candidate capability (a registered string whose payload format no revision has pinned) must not be emitted. The C packers, the browser writers and `wai wrap` refuse one, and `codecs::is_dispatchable` is the single dispatch and selection predicate (SPEC §4): candidates, companion-only capabilities, records and derivations are not dispatchable, and an unregistered string is decoded only where the sink advertises it. The codec glossary is linked to the registry through a reviewed table and held to its schema by a library test; `org` becomes `body`, from a closed allowlist. The SPEC gains §5 sections on kinds and candidates, a §8 edition rule, corrected citations and rewritten appendices. The quantum-ops vectors are regenerated with neutral device ids. CI declares the feature-gated test targets with required-features, checks the registry export, builds wai-web for wasm32, and runs two public-text checks (comparative prose, and private name patterns) with a self-test. The specification, extensions, research notes, READMEs, glossary and source comments are rewritten to name no company or product and to make no comparisons. Co-Authored-By: Claude Opus 5.5 <[email protected]>
93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
"""WAI source-side neural encoder for `wai.neural.mimi`.
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Mimi (arXiv 2410.00037) at 24 kHz, 12.5 Hz frame rate, 8 codebooks of 2048.
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Designed for real-time speech use. The token rate is 8 codebooks of 11 bits at
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12.5 Hz, about 1.1 kbps on the wire.
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Wire format (uniform with all wai.neural.<audio> capabilities):
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<III> q t n_samples (little-endian u32 × 3)
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q*t × u16 codes (row-major, LE)
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Usage:
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python tools/wai_mimi_encode.py <input.wav> -o <output.wai>
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"""
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from __future__ import annotations
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import argparse
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import json
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import struct
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import subprocess
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import sys
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import wave
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from pathlib import Path
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import numpy as np
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MODEL_ID = "kyutai/mimi" # allow-name: model-hub id the tool defaults to
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WAI_MAGIC = b"WAI1"
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SR = 24_000
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def encode(wav_path: str, out_path: str) -> None:
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with wave.open(wav_path, "rb") as w:
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sr_in = w.getframerate()
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raw_bytes = w.getnframes() * 2 * w.getnchannels()
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pcm = subprocess.run(
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["ffmpeg", "-v", "error", "-i", wav_path, "-ac", "1",
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"-ar", str(SR), "-f", "f32le", "-"],
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check=True, capture_output=True).stdout
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raw = np.frombuffer(pcm, np.float32)
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import torch
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from transformers import MimiModel, AutoFeatureExtractor
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model = MimiModel.from_pretrained(MODEL_ID).eval()
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proc = AutoFeatureExtractor.from_pretrained(MODEL_ID)
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inp = proc(raw_audio=raw, sampling_rate=SR, return_tensors="pt")
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with torch.no_grad():
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enc = model.encode(inp["input_values"], inp.get("padding_mask"))
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# MimiEncoderOutput.audio_codes: [batch=1, n_codebooks, T]
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codes = enc.audio_codes.squeeze(0).numpy().astype(np.uint16)
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q, t = codes.shape
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payload = struct.pack("<III", q, t, len(raw)) + codes.tobytes()
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manifest = {
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"wai": "1.0",
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"media": "audio",
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"intent": "replicate",
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"model_requirement": {
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"capability": "wai.neural.mimi",
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"fallback": "wai.audio.opus",
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},
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"conditioning": {"kind": "mimi_tokens"},
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"target": {"sr": SR, "n_samples": int(len(raw)),
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"codebooks": int(q), "frames": int(t)},
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}
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mb = json.dumps(manifest, separators=(",", ":")).encode()
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with open(out_path, "wb") as f:
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f.write(WAI_MAGIC)
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f.write(struct.pack("<I", len(mb))); f.write(mb)
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f.write(struct.pack("<I", len(payload))); f.write(payload)
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envelope_size = Path(out_path).stat().st_size
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dur = len(raw) / SR
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kbps = envelope_size * 8 / dur / 1000
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print(f"input audio: {dur:.2f} s mono @ {sr_in} Hz "
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f"({raw_bytes:>10,} B as int16 PCM)")
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print(f"Mimi tokens: {q} codebooks × {t} frames = {q*t} codes")
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print(f"WAI envelope: {envelope_size:>10,} B "
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f"({raw_bytes/envelope_size:6.1f}× compressed, "
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f"{kbps:5.1f} kbps)")
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print(f"model on the wire: 0 (Mimi weights stay at the sink)")
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print(f"capability declared:{manifest['model_requirement']['capability']:>30}")
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if __name__ == "__main__":
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p = argparse.ArgumentParser()
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p.add_argument("input")
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p.add_argument("-o", "--output", required=True)
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a = p.parse_args()
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encode(a.input, a.output)
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