"""eval_joint.py — stock vs JOINT (LLM-LoRA + full head, fullv3) on the identical 9-dialogue set. Baked fixes: demo-exact generate, sf tuple unpacks, save_audio write path, safe drift aggregation, Whisper return_timestamps=True, adapter download from model repo via list_repo_files with shutil.copyfile (HF cache paths are RELATIVE symlinks — os.replace leaves dangling links), load_lora_assets (imported standalone from vibevoice-community main with sys.modules registration) with component asserts. Env: ARMS (default "stock,joint"). Pushes to ACloudCenter/vibevoice-eval-jointv1. Prints EVAL DONE. """ import importlib.util, json, os, shutil, sys, time sys.path.insert(0, "/VibeVoice-classic") import numpy as np import soundfile as sf import torch import jiwer from huggingface_hub import hf_hub_download, HfApi from resemblyzer import VoiceEncoder, preprocess_wav from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor _spec = importlib.util.spec_from_file_location( "vibevoice.modular.lora_loading", "/vv-main/vibevoice/modular/lora_loading.py") assert _spec is not None and _spec.loader is not None, "lora_loading.py not found at /vv-main" lora_loading = importlib.util.module_from_spec(_spec) sys.modules[_spec.name] = lora_loading # dataclass machinery needs the module registered _spec.loader.exec_module(lora_loading) load_lora_assets = lora_loading.load_lora_assets MODEL = "microsoft/VibeVoice-1.5B" ADAPTER_REPO = "ACloudCenter/vibevoice-lora-joint" ADAPTER_SUB = "fullv3/lora" OUT_REPO = "ACloudCenter/vibevoice-eval-jointv1" SR = 24000 device = "cuda" ARMS = [a.strip() for a in os.environ.get("ARMS", "stock,joint").split(",")] SHORTS = [ "Speaker 1: Did you see the meteor shower last night?\nSpeaker 2: I missed it! Was it worth staying up for?\nSpeaker 1: Absolutely. The sky lit up green for a full minute.\nSpeaker 2: Okay, now I'm actually upset I slept through it.", "Speaker 1: The bread machine finally arrived.\nSpeaker 2: So you're a baker now?\nSpeaker 1: I'm a disaster with a bread machine. The first loaf came out like a brick.\nSpeaker 2: Send me a photo. Immediately.", "Speaker 1: My flight lands at six, so don't wait for dinner.\nSpeaker 2: I'll keep something warm anyway.\nSpeaker 1: You really don't have to.\nSpeaker 2: I know. That's why I'm doing it.", ] MEDIUMS = [ "Speaker 1: I've been reading about how old lighthouse keepers kept journals.\nSpeaker 2: What did they write about?\nSpeaker 1: Mostly weather, ships passing, and how lonely it was.\nSpeaker 2: That sounds peaceful and terrible at the same time.\nSpeaker 1: One keeper wrote that the light was his only conversation for months.\nSpeaker 2: Honestly, that might fix my screen time problem.\nSpeaker 1: Or break you completely. There was no off switch on the ocean.", "Speaker 1: The city is redoing the bike lane on Fifth Street.\nSpeaker 2: Finally. That stretch is dangerous.\nSpeaker 1: They're adding a physical barrier between cars and cyclists.\nSpeaker 2: About time. I've almost been clipped twice this month.\nSpeaker 1: Construction starts Monday, so expect detours.\nSpeaker 2: Worth it if it means I stop white-knuckling my handlebars.", "Speaker 1: My grandmother used to make bread every Sunday morning.\nSpeaker 2: Sourdough?\nSpeaker 1: A dense rye. She said the recipe came from her grandmother.\nSpeaker 2: Four generations of the same loaf.\nSpeaker 1: I still have her mixing bowl. It's older than the house.\nSpeaker 2: That bowl has earned a museum display.", ] LONGS = [ "Speaker 1: Let's talk about sleep. Everyone says eight hours, but nobody agrees on anything else.\nSpeaker 2: Right, and half the advice contradicts the other half.\nSpeaker 1: Some people swear by cold rooms. Others say the temperature barely matters.\nSpeaker 2: The one thing that's consistent is the schedule. Same bedtime, same wake time.\nSpeaker 1: Even on weekends, which is where everyone cheats.\nSpeaker 2: That's exactly where I cheat.\nSpeaker 1: The researchers say your body doesn't know it's Saturday.\nSpeaker 2: My body absolutely knows it's Saturday.\nSpeaker 1: Apparently not, biologically speaking.\nSpeaker 2: Fine, I'll set an alarm for Sunday too. This conversation is costing me my weekend.\nSpeaker 1: Sleep hygiene comes with sacrifices.\nSpeaker 2: Don't lecture me at nine on a Sunday morning.", "Speaker 1: The bakery on the corner closed last week.\nSpeaker 2: No! That place had the best almond croissants in the city.\nSpeaker 1: Rent tripled. The owner said she couldn't make the numbers work anymore.\nSpeaker 2: Where does a neighborhood bakery even go from there?\nSpeaker 1: She's moving production to a shared kitchen across the river and doing delivery only.\nSpeaker 2: So no more counter, no more coffee, just a website?\nSpeaker 1: Just a website. She seemed okay about it, which surprised me.\nSpeaker 2: Thirty years behind that counter and she seemed okay?\nSpeaker 1: Maybe she's more practical than sentimental.\nSpeaker 2: I'm going to miss walking past that smell every morning.\nSpeaker 1: Delivery still smells. It just doesn't linger.", "Speaker 1: I finally fixed the shed door.\nSpeaker 2: The one that's been hanging crooked since spring?\nSpeaker 1: Since spring. The hinges were stripped, not the frame, which saved me a full rebuild.\nSpeaker 2: How long did that take?\nSpeaker 1: Four hours, two of which were finding screws that actually fit.\nSpeaker 2: The hardware store run is always the real project.\nSpeaker 1: Two trips. One for the wrong screws, one for the right ones.\nSpeaker 2: Classic. Did you paint it?\nSpeaker 1: Not yet. The wood needs to dry out first, apparently.\nSpeaker 2: So the project continues.\nSpeaker 1: The project continues. But the door closes now, which is more than March could say.", ] def download_adapter(dest="/adapter"): api = HfApi() files = [f for f in api.list_repo_files(ADAPTER_REPO) if f.startswith(ADAPTER_SUB + "/")] assert len(files) >= 4, f"adapter files not found: {files}" for f in files: local = hf_hub_download(ADAPTER_REPO, f) name = f[len(ADAPTER_SUB) + 1:] op = os.path.join(dest, name) os.makedirs(os.path.dirname(op), exist_ok=True) shutil.copyfile(local, op) # NOT os.replace: cache paths are relative symlinks listing = sorted(os.listdir(dest)) print(f"ADAPTER OK: {listing}", flush=True) # follow-the-link verification: os.listdir can show dangling symlinks for must in ("adapter_config.json", "adapter_model.safetensors", "diffusion_head_full.bin"): assert os.path.isfile(os.path.join(dest, must)), f"dangling or missing: {must}" return dest def load_model(arm): model = VibeVoiceForConditionalGenerationInference.from_pretrained( MODEL, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, attn_implementation="sdpa").to(device).eval() if arm == "joint": report = load_lora_assets(model, "/adapter") loaded = [n for n, ok in (("language_model", report.language_model), ("diffusion_head_lora", report.diffusion_head_lora), ("diffusion_head_full", report.diffusion_head_full), ("acoustic_connector", report.acoustic_connector), ("semantic_connector", report.semantic_connector)) if ok] print(f"JOINT LOADED: {loaded}", flush=True) assert "language_model" in loaded and "diffusion_head_full" in loaded, loaded return model def gt_text(script): return " ".join(line.split(": ", 1)[1].strip() for line in script.strip().splitlines()) def main(): api = HfApi() download_adapter() scripts = ([("short", i, t) for i, t in enumerate(SHORTS)] + [("medium", i, t) for i, t in enumerate(MEDIUMS)] + [("long", i, t) for i, t in enumerate(LONGS)]) os.makedirs("/ref", exist_ok=True) ref_wavs = {} for spk, name in ((1, "en-Carter_man.wav"), (2, "en-Maya_woman.wav")): wav, sr = sf.read(f"/VibeVoice-classic/demo/voices/{name}") assert sr == SR ref_wavs[spk] = wav.astype(np.float32) sf.write(f"/ref/s{spk}.wav", ref_wavs[spk], SR) ref_paths = {spk: f"/ref/s{spk}.wav" for spk in (1, 2)} proc = VibeVoiceProcessor.from_pretrained(MODEL) from transformers import pipeline transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3", device=device, torch_dtype=torch.float16) encoder = VoiceEncoder(device) ref_wav = np.concatenate([ref_wavs[1], np.zeros(SR // 4, dtype=np.float32), ref_wavs[2]]) emb_ref = encoder.embed_utterance(preprocess_wav(ref_wav, SR)) results = [] os.makedirs("/out", exist_ok=True) for arm in ARMS: print(f"=== ARM {arm} ===", flush=True) model = load_model(arm) model.set_ddpm_inference_steps(num_steps=10) for tier, idx, script in scripts: t0 = time.time() with torch.no_grad(): inputs = proc(text=[script], voice_samples=[[ref_paths[1], ref_paths[2]]], padding=True, return_tensors="pt", return_attention_mask=True) out = model.generate( **inputs, max_new_tokens=None, cfg_scale=1.3, tokenizer=proc.tokenizer, generation_config={"do_sample": False}) gen_s = time.time() - t0 fn = f"{arm}_{tier}_{idx}.wav" proc.save_audio(out.speech_outputs[0], output_path=f"/out/{fn}") wav = sf.read(f"/out/{fn}", dtype="float32")[0] wav = np.asarray(wav, dtype=np.float32) audio_s = len(wav) / SR rtf = gen_s / max(audio_s, 1e-6) hyp = transcriber(f"/out/{fn}", generate_kwargs={"language": "english"}, return_timestamps=True)["text"].strip() gt = gt_text(script) wer = float(jiwer.wer(gt, hyp)) emb_gen = encoder.embed_utterance(preprocess_wav(f"/out/{fn}")) sim = float(np.dot(emb_gen, emb_ref) / (np.linalg.norm(emb_gen) * np.linalg.norm(emb_ref))) drift = None if tier == "long" and audio_s > 60: e1 = encoder.embed_utterance(preprocess_wav(wav[: 30 * SR], SR)) e2 = encoder.embed_utterance(preprocess_wav(wav[-30 * SR:], SR)) n = np.linalg.norm(e1) * np.linalg.norm(e2) drift = float(np.dot(e1, e2) / n) if n > 0 else None results.append({"arm": arm, "tier": tier, "idx": idx, "audio_s": round(audio_s, 1), "rtf": round(rtf, 3), "wer": round(wer, 3), "sim": round(sim, 3), "drift": round(drift, 3) if drift is not None else None}) api.upload_file(path_or_fileobj=f"/out/{fn}", path_in_repo=fn, repo_id=OUT_REPO) print(f"{arm} {tier}{idx}: rtf {rtf:.3f} wer {wer:.3f} sim {sim:.3f} " f"dur {audio_s:.1f}s drift {drift}", flush=True) del model torch.cuda.empty_cache() import statistics as stat def agg(arm, key): vals = [r[key] for r in results if r["arm"] == arm and r[key] is not None] return round(stat.mean(vals), 3) if vals else None summary = {arm: {"wer": agg(arm, "wer"), "sim": agg(arm, "sim"), "rtf": agg(arm, "rtf"), "drift": agg(arm, "drift")} for arm in ARMS} blob = {"summary": summary, "results": results} with open("/out/results.json", "w") as f: json.dump(blob, f, indent=1) api.upload_file(path_or_fileobj="/out/results.json", path_in_repo="results.json", repo_id=OUT_REPO) print("SUMMARY:", json.dumps(summary), flush=True) print("EVAL DONE", flush=True) if __name__ == "__main__": main()