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5c2beba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | #!/usr/bin/env python3
"""Batch-synthesize the Egyptian eval set with s2-pro (+optional LoRA ckpt).
Loads the text2semantic model + codec ONCE, then synthesizes every sentence in
--sentences (jsonl: {id, text}) using a fixed reference voice, writing
<outdir>/<id>.wav plus timing.jsonl with generation stats (RTF etc).
Usage:
python synth_eval.py --outdir /opt/work/eval/baseline
python synth_eval.py --outdir /opt/work/eval/step_2000 \
--lora-ckpt results/s2pro_egy_lora/checkpoints/step_000002000.ckpt \
--lora-config r_32_egy
"""
import argparse
import json
import time
from pathlib import Path
import numpy as np
import soundfile as sf
import torch
from loguru import logger
from fish_speech.models.text2semantic.inference import (
encode_audio,
generate_long,
init_model,
load_codec_model,
)
CKPT = Path("/opt/work/checkpoints/s2-pro")
def load_lora(model, ckpt_path: str, lora_config_name: str, lora_filter: str = "all"):
from hydra import compose, initialize_config_dir
from hydra.utils import instantiate
from fish_speech.models.text2semantic.lora import setup_lora
cfg_dir = str(
Path("/opt/work/fish-speech/fish_speech/configs/lora").resolve()
)
with initialize_config_dir(version_base="1.3", config_dir=cfg_dir):
lora_cfg = instantiate(compose(config_name=lora_config_name))
setup_lora(model, lora_cfg)
sd = torch.load(ckpt_path, map_location="cpu", weights_only=False)
if "state_dict" in sd:
sd = sd["state_dict"]
sd = {k.removeprefix("model."): v for k, v in sd.items()}
if lora_filter == "slow":
sd = {k: v for k, v in sd.items() if ".fast_" not in k and not k.startswith("fast_")}
logger.info(f"ablation slow-only: {len(sd)} tensors kept")
elif lora_filter == "fast":
sd = {k: v for k, v in sd.items() if ".fast_" in k or k.startswith("fast_")}
logger.info(f"ablation fast-only: {len(sd)} tensors kept")
err = model.load_state_dict(sd, strict=False)
n_lora = sum(1 for k in sd if "lora" in k)
assert n_lora > 0, "no lora keys in checkpoint!"
logger.info(f"Loaded {n_lora} LoRA tensors; missing={len(err.missing_keys)} (expected: base weights)")
if err.unexpected_keys:
logger.warning(f"Unexpected keys: {err.unexpected_keys[:5]}")
return model
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--sentences", default="/opt/work/scripts/eval_sentences.jsonl")
ap.add_argument("--outdir", required=True)
ap.add_argument("--lora-ckpt", default=None)
ap.add_argument("--lora-config", default="r_32_egy")
ap.add_argument("--ref-audio", default="/opt/work/eval/ref_voice.wav")
ap.add_argument("--ref-text-file", default="/opt/work/eval/ref_voice.txt")
ap.add_argument("--temperature", type=float, default=0.8)
ap.add_argument("--top-p", type=float, default=0.8)
ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--skip-existing", action="store_true",
help="only synthesize ids whose wav is missing; merge timing")
ap.add_argument("--lora-filter", choices=["all", "slow", "fast"], default="all",
help="ablation: load only slow-transformer or only fast-transformer LoRA weights")
ap.add_argument("--limit", type=int, default=0, help="synth only first N sentences")
args = ap.parse_args()
outdir = Path(args.outdir)
outdir.mkdir(parents=True, exist_ok=True)
device = "cuda"
precision = torch.bfloat16
sentences = [
json.loads(l)
for l in Path(args.sentences).read_text(encoding="utf-8").splitlines()
if l.strip()
]
old_timing = {}
tj = outdir / "timing.jsonl"
if args.skip_existing and tj.exists():
for l in tj.read_text(encoding="utf-8").splitlines():
if l.strip():
r = json.loads(l)
old_timing[r["id"]] = r
before = len(sentences)
sentences = [s for s in sentences
if not (outdir / f"{s['id']}.wav").exists()]
logger.info(f"skip-existing: {before - len(sentences)} kept, {len(sentences)} to synth")
if not sentences:
logger.info("nothing to do")
return
if args.limit:
sentences = sentences[: args.limit]
logger.info("Loading text2semantic model...")
model, decode_one_token = init_model(CKPT, device, precision, compile=False)
if args.lora_ckpt:
model = load_lora(model, args.lora_ckpt, args.lora_config, args.lora_filter)
model = model.to(device=device, dtype=precision).eval()
with torch.device(device):
model.setup_caches(
max_batch_size=1,
max_seq_len=model.config.max_seq_len,
dtype=next(model.parameters()).dtype,
)
logger.info("Loading codec...")
codec = load_codec_model(CKPT / "codec.pth", device, precision)
ref_text = Path(args.ref_text_file).read_text(encoding="utf-8").strip()
ref_tokens = encode_audio(args.ref_audio, codec, device).cpu()
results = []
for s in sentences:
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
t0 = time.time()
gen = generate_long(
model=model,
device=device,
decode_one_token=decode_one_token,
text=s["text"],
num_samples=1,
max_new_tokens=0,
top_p=args.top_p,
top_k=30,
temperature=args.temperature,
compile=False,
iterative_prompt=True,
chunk_length=300,
prompt_text=[ref_text],
prompt_tokens=[ref_tokens],
)
codes = []
for r in gen:
if r.action == "sample":
codes.append(r.codes)
gen_s = time.time() - t0
if not codes:
logger.error(f"{s['id']}: NO CODES GENERATED")
results.append({"id": s["id"], "error": "no_codes"})
continue
merged = torch.cat(codes, dim=1).to(device)
with torch.no_grad():
fake = codec.from_indices(merged.unsqueeze(0))
wav = fake[0, 0].float().cpu().numpy()
dur = len(wav) / codec.sample_rate
sf.write(outdir / f"{s['id']}.wav", wav, codec.sample_rate)
rtf = gen_s / max(dur, 1e-6)
results.append(
{"id": s["id"], "text": s["text"], "gen_s": round(gen_s, 2),
"dur_s": round(dur, 2), "rtf": round(rtf, 2)}
)
logger.info(f"{s['id']}: {dur:.1f}s audio in {gen_s:.1f}s (RTF {rtf:.2f})")
merged = {**old_timing, **{r["id"]: r for r in results}}
with open(outdir / "timing.jsonl", "w", encoding="utf-8") as f:
for r in merged.values():
f.write(json.dumps(r, ensure_ascii=False) + "\n")
ok = [r for r in results if "rtf" in r]
if ok:
logger.info(
f"DONE {len(ok)}/{len(results)} ok; mean RTF "
f"{sum(r['rtf'] for r in ok)/len(ok):.2f}"
)
if __name__ == "__main__":
main()
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