Standalone joint2 eval: SUB=epoch2/checkpoint-900/lora baked, copyfile+isfile asserts, VERSION marker v2.0

eval_joint2.py

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+"""eval_joint2.py v2.0 — closed-loop eval of the POLISHED joint model (epoch2/checkpoint-900/lora).
+Standalone: no runtime patching; adapter path baked to ground-truth layout (verified via repo ls).
+Baked fixes: shutil.copyfile download (HF cache = relative symlinks), isfile asserts after copy,
+Whisper return_timestamps, safe drift aggregation, demo-exact generate.
+Env: ARMS (default "joint2"). Pushes to ACloudCenter/vibevoice-eval-jointv1. Prints EVAL DONE.
+"""
+VERSION = "eval_joint2.py v2.0 (SUB=epoch2/checkpoint-900/lora)"
+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
+_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 = "epoch2/checkpoint-900/lora"
+OUT_REPO = "ACloudCenter/vibevoice-eval-jointv1"
+SR = 24000
+device = "cuda"
+ARMS = [a.strip() for a in os.environ.get("ARMS", "joint2").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 under {ADAPTER_SUB}: {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)  # HF cache paths are relative symlinks — copy content, never move
+        assert os.path.isfile(op) and os.path.getsize(op) > 0, f"copy failed for {name}"
+    listing = sorted(os.listdir(dest))
+    print(f"ADAPTER OK: {listing}", flush=True)
+    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 == "joint2":
+        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"JOINT2 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()
+    print(VERSION, flush=True)
+    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_joint2.json", "w") as f:
+        json.dump(blob, f, indent=1)
+    api.upload_file(path_or_fileobj="/out/results_joint2.json", path_in_repo="results_joint2.json", repo_id=OUT_REPO)
+    print("SUMMARY:", json.dumps(summary), flush=True)
+    print("EVAL DONE", flush=True)
+
+
+if __name__ == "__main__":
+    main()