| """Run the trained Coherence Dial LoRA across (sentence, voice, level, seed) tuples. |
| |
| For each (sentence, voice, level, seed): |
| - prompt = "{sentence} | {stem} {level_word}" (the LoRA's expected input format) |
| - call F5-TTS + LoRA inference |
| - write sweep/{voice}_lv{level}_s{seed}_{idx}.wav |
| |
| Default sweep: 10 hold-out sentences x N voices x 5 levels x 3 seeds. |
| """ |
|
|
| import argparse |
| import json |
| import sys |
| from pathlib import Path |
|
|
| |
| |
| sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) |
|
|
| LEVEL_WORDS = ["zero", "one", "two", "three", "four", "five", "six", "seven"] |
| DEFAULT_HOLDOUT_SENTENCES = [ |
| "the river was wide and calm in the morning light", |
| "she opened the old book and began to read aloud", |
| "a quiet wind moved through the empty stone courtyard", |
| "the children laughed and ran across the wet grass", |
| "he wrote her a letter that he never sent", |
| "the train arrived late and emptied into the rain", |
| "small lights flickered in windows along the harbor", |
| "no one knew what the dog had seen in the trees", |
| "she remembered a song her grandmother used to hum", |
| "the city slept under a thin veil of new snow", |
| ] |
|
|
|
|
| def main(): |
| p = argparse.ArgumentParser() |
| p.add_argument("--lora", required=True, help="path to LoRA adapter dir or .safetensors") |
| p.add_argument("--voices", required=True, |
| help="comma-separated voice specs: v1:data/voices/v1.wav:data/voices/v1.txt,...") |
| p.add_argument("--out", default="sweep") |
| p.add_argument("--levels", type=int, default=5) |
| p.add_argument("--seeds", default="42,123,7") |
| p.add_argument("--sentences", help="optional path; defaults to a built-in 10-sentence holdout") |
| p.add_argument("--max-sentences", type=int, default=10) |
| p.add_argument("--stem", default="tongues") |
| p.add_argument("--model", default="F5TTS_v1_Base") |
| args = p.parse_args() |
|
|
| out = Path(args.out); out.mkdir(parents=True, exist_ok=True) |
| sentences = (Path(args.sentences).read_text().splitlines() |
| if args.sentences else DEFAULT_HOLDOUT_SENTENCES) |
| sentences = [s.strip() for s in sentences if s.strip()][: args.max_sentences] |
|
|
| voices = [] |
| for v in args.voices.split(","): |
| parts = v.split(":") |
| vid, wav = parts[0], parts[1] |
| ref_text_path = parts[2] if len(parts) > 2 else None |
| ref_text = Path(ref_text_path).read_text().strip() if ref_text_path and Path(ref_text_path).exists() else "" |
| voices.append({"id": vid, "wav": wav, "ref_text": ref_text}) |
|
|
| seeds = [int(s) for s in args.seeds.split(",") if s.strip()] |
|
|
| try: |
| import patches |
| except ImportError as e: |
| print(f"could not import patches/ from {Path(__file__).resolve().parent.parent}: {e}", file=sys.stderr); sys.exit(1) |
| try: |
| from f5_tts.api import F5TTS |
| except ImportError as e: |
| print(f"could not import f5_tts: {e}", file=sys.stderr); sys.exit(1) |
| tts = F5TTS(model=args.model) |
| tts.load_lora(args.lora) |
|
|
| rows = [] |
| total = len(sentences) * len(voices) * args.levels * len(seeds) |
| print(f"sweeping {total} clips", file=sys.stderr) |
| i = 0 |
| for si, sentence in enumerate(sentences): |
| for voice in voices: |
| for lv in range(args.levels): |
| for sd in seeds: |
| name = f"{voice['id']}_lv{lv}_s{sd}_s{si}" |
| wav_p = out / f"{name}.wav" |
| |
| |
| try: |
| tts.set_dial(lv) |
| tts.infer(ref_file=voice["wav"], ref_text=voice["ref_text"], |
| gen_text=sentence, file_wave=str(wav_p), seed=sd) |
| except Exception as e: |
| print(f" fail {name}: {e}", file=sys.stderr); continue |
| rows.append({"name": name, "wav": str(wav_p), "sentence": sentence, |
| "voice": voice["id"], "level": lv, "seed": sd}) |
| i += 1 |
| if i % 10 == 0: |
| print(f" {i}/{total}", file=sys.stderr) |
| (out / "sweep_manifest.json").write_text(json.dumps(rows, indent=2)) |
| print(f"\nwrote {len(rows)} clips + {out}/sweep_manifest.json") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
