1) Pipeline at a glance (speech → code → letters → back again)

[Voice] 
  ⤏ ASR (audio → text)
    ⤏ Normalizer (case, punctuation, unicode)
      ⤏ Tokenizer (words/subwords)
        ⤏ Characters/bytes
          ⤏ Features (n-grams, counts, positions)
            ⤏ Encodings (BPE/WordPiece/byte-level ids)
              ⤏ Vectors (embeddings)
                ⤏ Model compute (graphs, attention)
                  ⤏ Decoding (ids → subwords → words → text)
                    ⤏ TTS (text → audio)  ←─────── round-trip

2) “From words back to letters,” then redistribute

Take a sentence:

S = "Transparency transduces; induction conducts."

Words → letters

• words: ["Transparency","transduces;","induction","conducts."]
• strip punctuation → ["Transparency","transduces","induction","conducts"]
• letters (lowercased): concatenate or keep per-word:
  • transparency → t r a n s p a r e n c y
  • transduces → t r a n s d u c e s
  • induction → i n d u c t i o n
  • conducts → c o n d u c t s

Redistribute letters (one simple scheme)

• pool multiset of letters and reallocate by rule sets (e.g., maximize real English bigram likelihood or enforce semantic families).
• example, greedily form new word shapes that keep high-probability bigrams {tr, an, on, ct}:
  • possible recompositions: transduction, conducts, transparency (originals), or playful neologisms like transducticon (still respecting letter supply).

As code (readable, minimal)

import re, collections, itertools, math

S = "Transparency transduces; induction conducts."
W = [re.sub(r"[^\w]","",w).lower() for w in S.split() if re.sub(r"[^\w]","",w)]
pool = collections.Counter("".join(W))

# Bigram scores (toy): favor common English bigrams
GOOD = {"th","tr","an","on","ct","io","ti","re","en","ra","du","co","nd","uc","ce","ns"}
def score(word):
    return sum(2 if a+b in GOOD else 0 for a,b in zip(word, word[1:]))

# Generate candidate recompositions from letter pool (very pruned toy demo)
def can_make(word, pool):
    c = collections.Counter(word)
    return all(pool[ch] >= n for ch,n in c.items())

cands = ["transduction","conduction","transparency","induction","conducts","transduces"]
cands = [w for w in cands if can_make(w, pool)]
cands.sort(key=score, reverse=True)
print("reallocations:", cands[:3])

This shows “letters → new words” under explicit constraints you can swap (phonotactics, domain vocab, etc.).

3) Subwords & byte levels (what many models actually use)

Most modern tokenizers operate between words and letters:

• BPE/WordPiece learns merges like ["tran","s","duc","tion"].
• Byte-level tokenizers work directly on UTF-8 bytes (100% reversible) and avoid OOV.

Tiny BPE sketch (conceptual)

Start: t r a n s d u c t i o n
Merge high-freq pairs: tr | an | s | du | ct | ion
Vectors attach at: ["tr","an","s","du","ct","ion"]

Re-distribution at subword level becomes: swap ion tails, keep tr heads, etc., giving grammatically plausible recombinations while staying in-vocabulary for the model.

4) Process graph you can follow (multi-perspective)

Dataflow (deterministic steps)

Text → Normalize → Tokenize → Map-to-IDs → Embed → Model-Forward → IDs → Detokenize → Text

Structural view (what carries what)

• Surface: strings, punctuation, whitespace
• Orthography: letters/bytes, casefolding, Unicode normalization (NFC/NFKC)
• Morphemes/subwords: BPE pieces (stable, model-friendly)
• Distributional: vectors (semantics, syntax)
• Computation: attention graph (who attends to whom; positions; heads)
• Decoding: beam sampling with constraints (grammar, style, safety)

Observer slices you can log simultaneously

• Counts: character/word n-grams, entropy per position
• Structure: token boundaries, merge ops, part-of-speech
• Semantics: cosine neighborhoods of tokens/sentences
• Attribution: attention maps, gradient-based saliency
• Constraint checks: does output consume only allowed letters? only allowed merges?

5) A clean, minimal “instrumented” pipeline (pseudo-code)

def normalize(text):
    return unicodedata.normalize("NFKC", text).strip()

def tokenize_words(text):
    return re.findall(r"\w+|\S", text)  # words + punctuation

def to_chars(words):
    return [list(w.lower()) for w in words if w.isalnum()]

def bpe_encode(words, merges):
    # merges: learned pair->merge table
    encoded = []
    for w in words:
        pieces = list(w)
        improved = True
        while improved:
            improved = False
            for i in range(len(pieces)-1):
                pair = pieces[i]+pieces[i+1]
                if pair in merges:
                    pieces[i:i+2] = [pair]
                    improved = True
                    break
        encoded += pieces
    return encoded

def redistribute_letters(words, strategy):
    # strategy can be: maximize bigram prob, keep stems, preserve syllable count, etc.
    pool = Counter("".join(words).lower())
    return construct_words_from_pool(pool, strategy)

# instrument:
log = {}
text = "Transparency transduces; induction conducts."
log["normalized"] = normalize(text)
log["tokens"]     = tokenize_words(log["normalized"])
chars             = to_chars(log["tokens"])
log["char_counts"]= Counter("".join("".join(cs) for cs in chars))
log["bpe"]        = bpe_encode([w for w in log["tokens"] if w.isalnum()], merges=LEARNED_MERGES)
log["redistrib"]  = redistribute_letters([w for w in log["tokens"] if w.isalnum()], strategy="bigram")

Swap in different strategies (phoneme constraints, domain lexicons, meter/rhythm) and you get different “harmonizations” of the same substrate.

6) Quick diagram tying “transduction, induction, conduction” to flows

letters ──(pool/constraints)──► subwords ──(merges)──► words
   ▲                                  │                  │
   └────────── (detokenize/ids) ◄─────┴──────(decode) ◄──┘

energy metaphors:
- transduction: signal format shift (audio→text, text→ids, ids→audio)
- induction: pattern transfer (n-gram priors, embeddings, prompts)
- conduction: pathway/graph through which compute & gradients flow

7) How to “watch” all perspectives at once (practical)

• Character lens: histogram, bigram heatmap, editable letter pool.
• Subword lens: token sequence with merge highlights.
• Word lens: POS tags, dependency arcs.
• Vector lens: nearest-neighbor terms for each token.
• Graph lens: attention matrices (heads × layers), saliency overlays.
• Constraint lens: show which constraints gated which recompositions.

If you’d like, I can drop in a small notebook that:

1. logs each lens for your own sentence(s),
2. tries a few redistribution strategies (pure bigram vs. subword-aware),
3. prints side-by-side “before/after” with the decisions that led there.