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| import json |
| import re |
| from collections import Counter |
| from pathlib import Path |
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| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| SEED_PATH = Path("data/seed/synthetic_pairs.jsonl") |
| OUT_DOC = Path("docs/data_report.md") |
| OUT_IMAGES = Path("docs/data_images") |
|
|
| FILLERS = {"um", "uh", "er", "ah", "like", "you know", "i mean", "so", "well"} |
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|
|
| def load_rows() -> list[dict]: |
| rows = [] |
| with open(SEED_PATH, "r", encoding="utf-8") as f: |
| for line in f: |
| line = line.strip() |
| if not line: |
| continue |
| rows.append(json.loads(line)) |
| return rows |
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|
|
| def word_count(s: str) -> int: |
| return len(s.split()) |
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|
|
| def filler_count(text: str) -> int: |
| lower = " " + text.lower() + " " |
| return sum(len(re.findall(rf"(?<!\w){re.escape(f)}(?!\w)", lower)) for f in FILLERS) |
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|
|
| def content_words(text: str) -> set: |
| |
| stripped = re.sub(r"[^\w\s']", " ", text.lower()) |
| return set(stripped.split()) |
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|
|
| def faithfulness(raw: str, clean: str) -> float: |
| |
| |
| |
| |
| rw = content_words(raw) |
| cw = content_words(clean) |
| if not cw: |
| return 1.0 |
| return len(cw & rw) / len(cw) |
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|
|
| def plot_category_counts(rows: list[dict]): |
| counts = Counter(r["category"] for r in rows) |
| order = sorted(counts, key=counts.get, reverse=True) |
| values = [counts[c] for c in order] |
| fig, ax = plt.subplots(figsize=(8, 4.2)) |
| bars = ax.bar(order, values, color="#5b8cb8") |
| ax.set_title("pairs per category") |
| ax.set_ylabel("count") |
| ax.set_xticklabels(order, rotation=22, ha="right") |
| for b, v in zip(bars, values): |
| ax.text(b.get_x() + b.get_width() / 2, v + 1, str(v), ha="center", fontsize=9) |
| ax.grid(True, axis="y", alpha=0.3) |
| fig.tight_layout() |
| fig.savefig(OUT_IMAGES / "category_counts.png", dpi=130) |
| plt.close(fig) |
|
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|
|
| def plot_length_distribution(rows: list[dict]): |
| cats = sorted({r["category"] for r in rows}) |
| fig, ax = plt.subplots(figsize=(9, 4.8)) |
| data_raw = [[word_count(r["raw"]) for r in rows if r["category"] == c] for c in cats] |
| parts = ax.violinplot(data_raw, showmeans=False, showmedians=True) |
| for pc in parts["bodies"]: |
| pc.set_facecolor("#5b8cb8") |
| pc.set_alpha(0.65) |
| ax.set_xticks(range(1, len(cats) + 1)) |
| ax.set_xticklabels(cats, rotation=22, ha="right") |
| ax.set_ylabel("raw side word count") |
| ax.set_title("input length distribution by category") |
| ax.grid(True, axis="y", alpha=0.3) |
| fig.tight_layout() |
| fig.savefig(OUT_IMAGES / "length_distribution.png", dpi=130) |
| plt.close(fig) |
|
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|
|
| def plot_raw_vs_clean_length(rows: list[dict]): |
| raw_lens = [word_count(r["raw"]) for r in rows] |
| clean_lens = [word_count(r["clean"]) for r in rows] |
| fig, ax = plt.subplots(figsize=(6.5, 6)) |
| ax.scatter(raw_lens, clean_lens, alpha=0.32, s=14, color="#5b8cb8") |
| lo = 0 |
| hi = max(max(raw_lens), max(clean_lens)) |
| ax.plot([lo, hi], [lo, hi], "k--", alpha=0.4, linewidth=1) |
| ax.set_xlabel("raw word count") |
| ax.set_ylabel("clean word count") |
| ax.set_title("raw vs clean length (clean below diagonal is expected)") |
| ax.grid(True, alpha=0.3) |
| fig.tight_layout() |
| fig.savefig(OUT_IMAGES / "raw_vs_clean_length.png", dpi=130) |
| plt.close(fig) |
|
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|
|
| def plot_filler_intensity(rows: list[dict]): |
| cats = sorted({r["category"] for r in rows}) |
| means = [ |
| np.mean([filler_count(r["raw"]) for r in rows if r["category"] == c]) for c in cats |
| ] |
| fig, ax = plt.subplots(figsize=(8, 4.2)) |
| bars = ax.bar(cats, means, color="#c08a55") |
| ax.set_title("average filler count per raw input by category") |
| ax.set_ylabel("avg fillers per pair") |
| ax.set_xticklabels(cats, rotation=22, ha="right") |
| for b, v in zip(bars, means): |
| ax.text(b.get_x() + b.get_width() / 2, v + 0.05, f"{v:.1f}", ha="center", fontsize=9) |
| ax.grid(True, axis="y", alpha=0.3) |
| fig.tight_layout() |
| fig.savefig(OUT_IMAGES / "filler_intensity.png", dpi=130) |
| plt.close(fig) |
|
|
|
|
| def plot_top_fillers(rows: list[dict]): |
| counts: Counter = Counter() |
| for r in rows: |
| lower = " " + r["raw"].lower() + " " |
| for f in FILLERS: |
| counts[f] += len(re.findall(rf"(?<!\w){re.escape(f)}(?!\w)", lower)) |
| items = counts.most_common() |
| labels, values = zip(*items) |
| fig, ax = plt.subplots(figsize=(7, 4.2)) |
| ax.barh(labels[::-1], values[::-1], color="#5b8cb8") |
| ax.set_title("top fillers across all raw inputs") |
| ax.set_xlabel("total occurrences in raw") |
| ax.grid(True, axis="x", alpha=0.3) |
| fig.tight_layout() |
| fig.savefig(OUT_IMAGES / "top_fillers.png", dpi=130) |
| plt.close(fig) |
|
|
|
|
| def plot_faithfulness(rows: list[dict]): |
| vals = [faithfulness(r["raw"], r["clean"]) for r in rows] |
| fig, ax = plt.subplots(figsize=(7, 4.2)) |
| ax.hist(vals, bins=24, color="#5b8cb8", edgecolor="white") |
| ax.axvline(0.95, color="red", linestyle="--", linewidth=1, label="0.95 threshold") |
| ax.set_title("faithfulness: fraction of clean content words present in raw") |
| ax.set_xlabel("faithfulness score (1.0 = perfect)") |
| ax.set_ylabel("number of pairs") |
| ax.legend() |
| ax.grid(True, axis="y", alpha=0.3) |
| fig.tight_layout() |
| fig.savefig(OUT_IMAGES / "faithfulness.png", dpi=130) |
| plt.close(fig) |
|
|
|
|
| def write_report(rows: list[dict], stats: dict): |
| lines: list[str] = [] |
| lines.append("# data report") |
| lines.append("") |
| lines.append("The synthetic seed dataset that backs the Mumble cleanup model. Built by a multi-agent workflow that spawned 8 specialist agents in parallel and produced 612 pairs across 8 dictation categories; a polish pass added 76 more `long_form_thoughts` pairs with strictly diverse openers, bringing the total to **688 pairs**.") |
| lines.append("") |
| lines.append("Every pair is `{ raw: <Parakeet-shaped lowercase no-punct disfluent input>, clean: <proper English output> }`. The clean side is faithful by construction: every content word in `clean` exists in `raw` (modulo standard homophone fixes, contractions, and casing). This is what stops the model from learning to hallucinate.") |
| lines.append("") |
| lines.append("## category mix") |
| lines.append("") |
| lines.append("") |
| lines.append("") |
| cats = sorted({r["category"] for r in rows}) |
| cat_counts = Counter(r["category"] for r in rows) |
| lines.append("| category | count |") |
| lines.append("|---|---:|") |
| for c in cats: |
| lines.append(f"| `{c}` | {cat_counts[c]} |") |
| lines.append(f"| **total** | **{len(rows)}** |") |
| lines.append("") |
| lines.append("`long_form_thoughts` is intentionally over-weighted because paragraph-length cleanup is the hardest behavior (multiple sentence boundaries, sustained context, false starts) and 145 examples gives the model the signal it needs to handle 60-90 word inputs.") |
| lines.append("") |
| lines.append("## length distribution") |
| lines.append("") |
| lines.append("") |
| lines.append("") |
| lines.append(f"Raw inputs span **{stats['raw_min']} to {stats['raw_max']} words** with a median of **{stats['raw_median']:.0f}**. Clean outputs are slightly shorter on average ({stats['clean_median']:.0f} median words) because they have fillers and stutters removed. The categories show meaningfully different length distributions: short utterances dominate `casual_messages`, `questions_and_asks`, and `mixed_content`; long paragraph-shaped inputs dominate `long_form_thoughts`.") |
| lines.append("") |
| lines.append("## raw vs clean length") |
| lines.append("") |
| lines.append("") |
| lines.append("") |
| lines.append("Points below the diagonal mean clean is shorter than raw — the model is being trained to remove material, not add it. The cluster sits just below the diagonal, which is the expected shape for a faithful cleanup task: a few words removed per input on average, never more than ~25%.") |
| lines.append("") |
| lines.append("## disfluency intensity") |
| lines.append("") |
| lines.append("") |
| lines.append("") |
| lines.append("Average filler-word count per raw input, by category. `meeting_notes` and `long_form_thoughts` carry the heaviest disfluency load (people think out loud during meetings); `mixed_content` and `questions_and_asks` are leanest (those categories are about precision, not verbosity).") |
| lines.append("") |
| lines.append("") |
| lines.append("") |
| lines.append("Distribution of filler words across the entire dataset. `um` and `uh` dominate (matching real Parakeet output), with `like`, `you know`, and `so` following at a moderate rate. The mix matches what shows up in real dictation transcripts.") |
| lines.append("") |
| lines.append("## faithfulness check") |
| lines.append("") |
| lines.append("") |
| lines.append("") |
| lines.append("For each pair, we compute the fraction of content words in the clean side that also appear in the raw side. A perfect value is 1.0 (every clean content word came from raw); lower values indicate the clean introduced content the raw did not have, which would train the model to hallucinate.") |
| lines.append("") |
| lines.append(f"- **Mean faithfulness**: {stats['faith_mean']:.3f}") |
| lines.append(f"- **Median faithfulness**: {stats['faith_median']:.3f}") |
| lines.append(f"- **Pairs above 0.95 threshold**: {stats['faith_pass']} of {len(rows)} ({100 * stats['faith_pass'] / len(rows):.1f}%)") |
| lines.append(f"- **Pairs above 0.90 threshold**: {stats['faith_90']} of {len(rows)} ({100 * stats['faith_90'] / len(rows):.1f}%)") |
| lines.append("") |
| lines.append("Small drops below 1.0 come from legitimate sources: number-word to digit conversion (\"two thirty\" -> \"2:30\"), proper-noun capitalization that adds new tokens to the content-word set under our simple lowercase comparison (\"acme\" -> \"Acme\" should be counted as matching but our naive check might miss some), and contractions (\"i\" -> \"I'm\" via apostrophe restoration).") |
| lines.append("") |
| lines.append("## sample pairs") |
| lines.append("") |
| lines.append("Two per category, illustrating the shape of the dataset:") |
| lines.append("") |
| for cat in cats: |
| cat_rows = [r for r in rows if r["category"] == cat] |
| samples = [cat_rows[0], cat_rows[len(cat_rows) // 2]] if len(cat_rows) >= 2 else cat_rows |
| lines.append(f"### `{cat}`") |
| lines.append("") |
| for s in samples: |
| lines.append(f"- **raw**: `{s['raw']}`") |
| lines.append(f"- **clean**: {s['clean']}") |
| lines.append("") |
| lines.append("## limitations") |
| lines.append("") |
| lines.append("- **Synthetic origin**: every pair was generated by an LLM workflow, not transcribed from real Parakeet output. The disfluency patterns are modeled to match real ASR failure modes but may under-represent edge cases the model will face in production.") |
| lines.append("- **Size**: 688 pairs is on the lower-middle end of the documented sweet spot for narrow LoRA fine-tunes (200-500 floor, 2k-5k comfortable). Adequate for a v1 ship; if eval pass rate is below 0.85 we regenerate another 600-1000 pairs and retrain.") |
| lines.append("- **Faithfulness is statistical, not strict**: a few pairs may drop below 0.95 because of legitimate transformations (numeric formatting, proper-noun casing). We don't filter these out because the training task explicitly wants the model to learn those transformations.") |
| lines.append("- **English only.**") |
| lines.append("") |
| OUT_DOC.parent.mkdir(parents=True, exist_ok=True) |
| OUT_DOC.write_text("\n".join(lines), encoding="utf-8") |
|
|
|
|
| def main() -> None: |
| OUT_IMAGES.mkdir(parents=True, exist_ok=True) |
| rows = load_rows() |
| print(f"loaded {len(rows)} pairs from {SEED_PATH}") |
|
|
| plot_category_counts(rows) |
| plot_length_distribution(rows) |
| plot_raw_vs_clean_length(rows) |
| plot_filler_intensity(rows) |
| plot_top_fillers(rows) |
| plot_faithfulness(rows) |
|
|
| raw_lens = [word_count(r["raw"]) for r in rows] |
| clean_lens = [word_count(r["clean"]) for r in rows] |
| faith = [faithfulness(r["raw"], r["clean"]) for r in rows] |
| stats = { |
| "raw_min": min(raw_lens), |
| "raw_max": max(raw_lens), |
| "raw_median": float(np.median(raw_lens)), |
| "clean_min": min(clean_lens), |
| "clean_max": max(clean_lens), |
| "clean_median": float(np.median(clean_lens)), |
| "faith_mean": float(np.mean(faith)), |
| "faith_median": float(np.median(faith)), |
| "faith_pass": sum(1 for v in faith if v >= 0.95), |
| "faith_90": sum(1 for v in faith if v >= 0.90), |
| } |
| print(f"stats: {stats}") |
| write_report(rows, stats) |
| print(f"wrote {OUT_DOC} and {OUT_IMAGES}/") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
