148 lines
4.9 KiB
Python
148 lines
4.9 KiB
Python
"""
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generate_proper_noun_audio.py
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──────────────────────────────
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Read proper_nouns.txt, generate a short TTS audio clip for every entry
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using am_michael, and save a JSON manifest for the GUI.
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Outputs:
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output_proper_nouns/<slug>.wav – one wav per entry
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output_proper_nouns/manifest.json – { "Word" : "slug.wav", … }
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Already-generated files are skipped, so re-runs are fast.
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Run:
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.venv/bin/python generate_proper_noun_audio.py
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"""
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import json
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import re
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import sys
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import numpy as np
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import soundfile as sf
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import torch
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from pathlib import Path
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from kokoro import KPipeline
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PROPER_NOUNS_FILE = Path("proper_nouns.txt")
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DATA_DIR = Path("output_proper_nouns") # JSON files — tracked in git
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AUDIO_DIR = Path("proper_nouns_audio") # WAV files — not tracked
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MANIFEST_FILE = DATA_DIR / "manifest.json"
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VOICE = "am_michael"
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SAMPLE_RATE = 24000
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SPEED = 1.0
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# ── Parse proper_nouns.txt ─────────────────────────────────────────────────────
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def parse_entries(path: Path) -> list[tuple[str, str]]:
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"""Return list of (category, entry) pairs."""
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entries: list[tuple[str, str]] = []
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current_cat = "Uncategorised"
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header_re = re.compile(r"^[A-Z &]+\s+\(\d+\)$")
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for line in path.read_text(encoding="utf-8").splitlines():
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stripped = line.strip()
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if not stripped:
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continue
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if stripped.startswith("=") or stripped.startswith("─"):
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continue
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if header_re.match(stripped):
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# e.g. "PEOPLE & CHARACTERS (301)"
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current_cat = stripped.rsplit("(", 1)[0].strip().title()
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continue
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if stripped.startswith("TOTAL:"):
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continue
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if stripped.startswith("Review this") or stripped.startswith("Each entry"):
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continue
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if stripped.startswith("PROPER NOUNS"):
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continue
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# Regular entry — indented two spaces in the file
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if line.startswith(" "):
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entries.append((current_cat, stripped))
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return entries
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def slugify(text: str) -> str:
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"""Convert 'Hagoth-II foo' → 'hagoth_ii_foo'."""
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s = text.lower()
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s = re.sub(r"[^a-z0-9]+", "_", s)
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return s.strip("_")
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# ── TTS generation ─────────────────────────────────────────────────────────────
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def generate(pipeline: KPipeline, text: str, out_path: Path) -> bool:
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chunks = []
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# Speak the word in a short carrier phrase so the TTS pronounces it
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# naturally (isolated tokens sometimes get clipped prosody).
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spoken = text
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for _, _, chunk in pipeline(spoken, voice=VOICE, speed=SPEED):
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if hasattr(chunk, "numpy"):
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chunk = chunk.cpu().numpy()
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chunk = np.atleast_1d(chunk.squeeze())
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if chunk.size > 0:
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chunks.append(chunk)
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if chunks:
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audio = np.concatenate(chunks)
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sf.write(str(out_path), audio, SAMPLE_RATE)
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return True
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return False
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# ── Main ───────────────────────────────────────────────────────────────────────
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def main() -> None:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"Device: {device}")
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if device == "cuda":
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print(f"GPU: {torch.cuda.get_device_name(0)}")
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DATA_DIR.mkdir(exist_ok=True)
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AUDIO_DIR.mkdir(exist_ok=True)
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print(f"Parsing '{PROPER_NOUNS_FILE}' …")
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entries = parse_entries(PROPER_NOUNS_FILE)
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print(f" {len(entries)} entries found.\n")
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# Load existing manifest so we can skip already-done words
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if MANIFEST_FILE.exists():
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manifest: dict = json.loads(MANIFEST_FILE.read_text())
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else:
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manifest = {}
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print("Initialising Kokoro pipeline …")
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pipeline = KPipeline(lang_code="a")
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skipped = 0
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generated = 0
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failed = 0
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for i, (cat, entry) in enumerate(entries):
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slug = slugify(entry)
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wav_name = f"{slug}.wav"
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wav_path = AUDIO_DIR / wav_name
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if entry in manifest and wav_path.exists():
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skipped += 1
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continue
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sys.stdout.write(f"\r[{i+1}/{len(entries)}] {entry[:55]:<55}")
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sys.stdout.flush()
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ok = generate(pipeline, entry, wav_path)
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if ok:
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manifest[entry] = wav_name
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generated += 1
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else:
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print(f"\n ✗ Failed: {entry}")
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failed += 1
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print(f"\n\nDone. generated={generated} skipped={skipped} failed={failed}")
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MANIFEST_FILE.write_text(json.dumps(manifest, ensure_ascii=False, indent=2))
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print(f"Manifest saved → '{MANIFEST_FILE}'")
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if __name__ == "__main__":
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main()
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