omni: W1 audio align smoke — synthetic dataset + 50-step script

End-to-end smoke proving the audio path:
  wav -> WhisperEncoder (frozen) -> Projector -> prepend to text embeddings
      -> tiny d6 GPT (random init) -> CE loss on text only

Pass criterion is a plain "loss drops by at least 0.5". On a 4090 the run
finishes in ~1 s and goes 5.55 -> 0.17 over 50 steps, so the threshold has
plenty of headroom against false positives.

Two design calls worth keeping in mind:

1. Synthetic sine clips, not LibriSpeech. W1 is forward-path proof, not
   alignment quality, and a deterministic offline dataset means no network
   on the smoke path. data/audio_smoke/manifest.jsonl is the only thing
   committed; wavs are regenerated by audio_smoke_data.py and gitignored.
   W2 swaps in real LibriSpeech.

2. Standalone byte-level tokenizer (UTF-8 bytes + a single BOS, vocab=257).
   Avoids depending on a trained nanochat BPE — the d6 GPT is random
   anyway, so vocab choice doesn't matter for "does the gradient flow"
   smoke. W2 onwards uses the real BPE on a real base.

Caveat documented in doc/todo.md: because the LM is also random and being
trained, the loss-down here mostly reflects the LM memorising 5 short
strings, not Whisper-Projector alignment. That's fine for proving
plumbing; W2 freezes the LM so projector-only gradient is the only path
to lower loss.

scripts/audio_smoke_data.py

@@ -0,0 +1,78 @@
+"""
+Generate the W1 audio smoke dataset: a handful of 5s sine-wave clips paired
+with deterministic transcripts.
+
+Why synthetic instead of real speech: W1 only proves the forward path
+(WhisperEncoder -> Projector -> GPT prepend) and that the projector's gradient
+flows into a decreasing loss on a tiny fixed set. Real speech adds a network
+dependency to a step that should be reproducible offline. W2 swaps in
+LibriSpeech.
+
+Audio files land under data/audio_smoke/wavs/ (gitignored). The manifest
+data/audio_smoke/manifest.jsonl is the only artifact committed.
+
+Usage:
+    python -m scripts.audio_smoke_data
+"""
+
+import argparse
+import json
+import wave
+from pathlib import Path
+
+import numpy as np
+
+
+SAMPLES = [
+    (220.0, "low tone"),
+    (330.0, "mid low tone"),
+    (440.0, "middle tone"),
+    (660.0, "mid high tone"),
+    (880.0, "high tone"),
+]
+SR = 16000
+DURATION_S = 5.0
+
+
+def synth_sine(freq_hz, duration_s=DURATION_S, sr=SR):
+    """Sine + 2nd harmonic + a sliver of noise so Whisper sees non-degenerate
+    frames (a pure tone collapses to a near-constant log-mel)."""
+    t = np.arange(int(sr * duration_s)) / sr
+    x = 0.5 * np.sin(2 * np.pi * freq_hz * t) + 0.25 * np.sin(2 * np.pi * 2 * freq_hz * t)
+    rng = np.random.default_rng(int(freq_hz))
+    x = x + 0.01 * rng.standard_normal(len(x))
+    return x.astype(np.float32)
+
+
+def write_wav_pcm16(path, audio, sr=SR):
+    """Write mono PCM16 WAV using the stdlib (no scipy/soundfile dependency)."""
+    pcm = np.clip(audio, -1.0, 1.0)
+    pcm = (pcm * 32767.0).astype(np.int16)
+    with wave.open(str(path), "wb") as w:
+        w.setnchannels(1)
+        w.setsampwidth(2)
+        w.setframerate(sr)
+        w.writeframes(pcm.tobytes())
+
+
+def generate_synthetic(data_dir):
+    data_dir = Path(data_dir)
+    wav_dir = data_dir / "wavs"
+    wav_dir.mkdir(parents=True, exist_ok=True)
+    manifest_path = data_dir / "manifest.jsonl"
+    with open(manifest_path, "w") as f:
+        for freq, text in SAMPLES:
+            name = f"sine_{int(freq):04d}.wav"
+            wav_path = wav_dir / name
+            if not wav_path.exists():
+                write_wav_pcm16(wav_path, synth_sine(freq))
+            f.write(json.dumps({"wav": f"wavs/{name}", "text": text, "sr": SR}) + "\n")
+    print(f"Wrote {len(SAMPLES)} samples to {data_dir}")
+    return manifest_path
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-dir", default="data/audio_smoke")
+    args = parser.parse_args()
+    generate_synthetic(args.data_dir)