app.py.bak

import os
import re
import json
import sys
import time
import threading
import traceback

import gradio as gr
import numpy as np
import soundfile as sf
import torch
import spaces
from huggingface_hub import login, snapshot_download

# --------- Environnement / stabilité ----------
os.environ.setdefault("FLA_CONV_BACKEND", "torch")   # éviter les kernels Triton
os.environ.setdefault("FLA_USE_FAST_OPS", "0")
os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
torch.backends.cuda.matmul.allow_tf32 = True
try:
    torch.set_float32_matmul_precision("high")
except Exception:
    pass

from pardi_speech import PardiSpeech, VelocityHeadSamplingParams  # présent dans ce repo

MODEL_REPO_ID = os.environ.get("MODEL_REPO_ID", "theodorr/pardi-speech-enfr-forbidden")
HF_TOKEN = os.environ.get("HF_TOKEN")

# --------- Cache global (préchargement au démarrage) ----------
_MODEL = {"pardi": None, "sr": 24000, "err": None, "logs": [], "thread": None}

def _log(msg: str):
    _MODEL["logs"].append(str(msg))
    # borne la taille
    if len(_MODEL["logs"]) > 2000:
        _MODEL["logs"] = _MODEL["logs"][-2000:]

def _env_diag() -> str:
    parts = []
    try:
        parts.append(f"torch={torch.__version__}")
        try:
            import triton  # type: ignore
            parts.append(f"triton={getattr(triton, '__version__', 'unknown')}")
        except Exception:
            parts.append("triton=not_importable")
        parts.append(f"cuda.is_available={torch.cuda.is_available()}")
        if torch.cuda.is_available():
            parts.append(f"cuda.version={torch.version.cuda}")
            try:
                free, total = torch.cuda.mem_get_info()
                parts.append(f"mem_free={free/1e9:.2f}GB/{total/1e9:.2f}GB")
            except Exception:
                pass
    except Exception as e:
        parts.append(f"env_diag_error={e}")
    return " | ".join(parts)

def _normalize_text(s: str, lang_hint: str = "fr") -> str:
    s = (s or "").strip()
    try:
        import re as _re
        from num2words import num2words
        def repl(m):
            try:
                return num2words(int(m.group()), lang=lang_hint)
            except Exception:
                return m.group()
        s = _re.sub(r"\d+", repl, s)
    except Exception:
        pass
    return s

def _to_mono_float32(arr: np.ndarray) -> np.ndarray:
    arr = np.asarray(arr)
    if arr.ndim == 2:
        arr = arr.mean(axis=1)
    return arr.astype(np.float32)

def _extract_repo_ids_from_config(config_path: str):
    repo_ids = set()
    preview = None
    try:
        with open(config_path, "r", encoding="utf-8") as f:
            cfg = json.load(f)
        pattern = re.compile(r"^[\w\-]+\/[\w\.\-]+$")  # org/name
        def rec(obj):
            if isinstance(obj, dict):
                for v in obj.values(): rec(v)
            elif isinstance(obj, list):
                for v in obj: rec(v)
            elif isinstance(obj, str):
                if pattern.match(obj): repo_ids.add(obj)
        rec(cfg)
        try:
            subset_keys = list(cfg)[:5] if isinstance(cfg, dict) else []
            preview = json.dumps({k: cfg[k] for k in subset_keys}, ensure_ascii=False)[:600]
        except Exception:
            pass
    except Exception:
        pass
    return sorted(repo_ids), preview

def _prefetch_and_load_cpu():
    """Exécuté dans un thread au démarrage du Space (hors worker GPU)."""
    try:
        _log("[prefetch] snapshot_download (main)...")
        local_dir = snapshot_download(
            repo_id=MODEL_REPO_ID,
            token=HF_TOKEN,
            local_dir=None,
            local_files_only=False,
        )
        _log(f"[prefetch] main done -> {local_dir}")

        cfg_path = os.path.join(local_dir, "config.json")
        nested, cfg_preview = _extract_repo_ids_from_config(cfg_path)
        if cfg_preview:
            _log(f"[config] preview: {cfg_preview}")
        for rid in nested:
            if rid == MODEL_REPO_ID:
                continue
            _log(f"[prefetch] nested repo: {rid} ...")
            snapshot_download(repo_id=rid, token=HF_TOKEN, local_dir=None, local_files_only=False)
            _log(f"[prefetch] nested repo: {rid} done")

        # Forcer offline pendant le vrai chargement
        old_off = os.environ.get("HF_HUB_OFFLINE")
        os.environ["HF_HUB_OFFLINE"] = "1"
        try:
            _log("[load] from_pretrained(map_location='cpu')...")
            m = PardiSpeech.from_pretrained(local_dir, map_location="cpu")
            m.eval()
            _MODEL["pardi"] = m
            _MODEL["sr"] = getattr(m, "sampling_rate", 24000)
            _log(f"[load] cpu OK (sr={_MODEL['sr']})")
        finally:
            if old_off is None:
                os.environ.pop("HF_HUB_OFFLINE", None)
            else:
                os.environ["HF_HUB_OFFLINE"] = old_off

    except BaseException as e:
        _MODEL["err"] = e
        _log(f"[EXC@preload] {type(e).__name__}: {e}")
        _log(traceback.format_exc())

# Lance le préchargement (hors GPU) dès l’import
if _MODEL["thread"] is None:
    _MODEL["thread"] = threading.Thread(target=_prefetch_and_load_cpu, daemon=True)
    _MODEL["thread"].start()

def _move_to_cuda_if_available(m, logs_acc):
    def L(msg): logs_acc.append(str(msg))
    if torch.cuda.is_available():
        L("[move] moving model to cuda...")
        try:
            m = m.to("cuda")  # type: ignore[attr-defined]
            L("[move] cuda OK")
        except Exception as e:
            L(f"[move] .to('cuda') failed: {e}. Keeping on CPU.")
    else:
        L("[move] cuda not available, keep CPU")
    return m

# --------- UI callback (GPU) ----------
@spaces.GPU(duration=200)
def synthesize(
    text: str,
    debug: bool,
    adv_sampling: bool,   # Velocity Head sampling
    ref_audio,
    ref_text: str,
    steps: int,
    cfg: float,
    cfg_ref: float,
    temperature: float,
    max_seq_len: int,
    seed: int,
    lang_hint: str,
):
    logs = []
    def LOG(msg: str):
        logs.append(str(msg))
        joined = "\n".join(logs + _MODEL["logs"][-50:])  # mêle quelques logs de préchargement
        if len(joined) > 12000:
            joined = joined[-12000:]
        return joined

    try:
        if HF_TOKEN:
            try:
                login(token=HF_TOKEN)
                yield None, LOG("✅ HF login ok")
            except Exception as e:
                yield None, LOG(f"⚠️ HF login failed: {e}")

        yield None, LOG("[env] " + _env_diag())
        torch.manual_seed(int(seed))
        os.environ.setdefault("CUDA_LAUNCH_BLOCKING", "1")

        # Si le modèle n’est pas encore prêt, on attend jusqu’à 180s max ici
        t0 = time.perf_counter()
        while _MODEL["pardi"] is None and _MODEL["err"] is None:
            elapsed = time.perf_counter() - t0
            yield None, LOG(f"[init] still loading on CPU… {elapsed:.1f}s")
            if elapsed > 180:
                # dump de la stack du thread de préchargement pour debug
                tid = _MODEL["thread"].ident if _MODEL["thread"] else None
                if tid is not None:
                    frame = sys._current_frames().get(tid)
                    if frame is not None:
                        stack_txt = "".join(traceback.format_stack(frame))
                        yield None, LOG("[stack-final]\n" + stack_txt)
                raise TimeoutError("Preload timeout (>180s)")
            time.sleep(2.0)

        if _MODEL["err"]:
            raise _MODEL["err"]

        pardi = _MODEL["pardi"]
        sr_out = _MODEL["sr"]

        # Déplacement vers CUDA si possible
        pardi = _move_to_cuda_if_available(pardi, logs)
        yield None, LOG(f"[init] model ready on {'cuda' if torch.cuda.is_available() else 'cpu'}, sr={sr_out}")

        # ---- Texte + prefix optionnel ----
        txt = _normalize_text(text or "", lang_hint=lang_hint)
        yield None, LOG(f"[text] {txt[:120]}{'...' if len(txt) > 120 else ''}")

        steps = int(min(max(1, int(steps)), 16))
        max_seq_len = int(min(max(50, int(max_seq_len)), 600))

        prefix = None
        if ref_audio is not None:
            yield None, LOG("[prefix] encoding reference audio...")
            if isinstance(ref_audio, str):
                wav, sr = sf.read(ref_audio)
            else:
                sr, wav = ref_audio
            wav = _to_mono_float32(wav)
            device = "cuda" if torch.cuda.is_available() else "cpu"
            wav_t = torch.from_numpy(wav).to(device).unsqueeze(0)
            with torch.inference_mode():
                prefix_tokens = pardi.patchvae.encode(wav_t)  # type: ignore[attr-defined]
            prefix = (ref_text or "", prefix_tokens[0])
            yield None, LOG("[prefix] done.")

        yield None, LOG(f"[run] has_prefix={prefix is not None}, steps={steps}, cfg={cfg}, cfg_ref={cfg_ref}, "
                        f"T={temperature}, max_seq_len={max_seq_len}, seed={seed}, adv_sampling={adv_sampling}")

        # ---- Chemin rapide (comme le notebook) ----
        with torch.inference_mode():
            if adv_sampling:
                try:
                    vparams = VelocityHeadSamplingParams(cfg_ref=float(cfg_ref), cfg=float(cfg), num_steps=int(steps))
                except TypeError:
                    vparams = VelocityHeadSamplingParams(cfg_ref=float(cfg_ref), cfg=float(cfg),
                                                         num_steps=int(steps), temperature=float(temperature))
                wavs, _ = pardi.text_to_speech([txt], prefix, max_seq_len=int(max_seq_len),
                                               velocity_head_sampling_params=vparams)
            else:
                wavs, _ = pardi.text_to_speech([txt], prefix, max_seq_len=int(max_seq_len))

        wav = wavs[0].detach().cpu().numpy().astype(np.float32)
        yield (sr_out, wav), LOG("[ok] done.")

    except Exception as e:
        tb = traceback.format_exc()
        yield None, LOG(f"[EXC] {type(e).__name__}: {e}\n{tb}")

# --------- UI ----------
def build_demo():
    with gr.Blocks(title="Lina-speech / pardi-speech Demo") as demo:
        gr.Markdown(
            "### Lina-speech (pardi-speech) – Démo TTS\n"
            "Génère de l'audio à partir de texte, avec ou sans prefix (audio de référence).\n"
            "Chemin rapide par défaut (comme le notebook)."
        )
        with gr.Row():
            text = gr.Textbox(label="Texte à synthétiser", lines=4, placeholder="Tape ton texte ici…")
        with gr.Accordion("Prefix (optionnel)", open=False):
            ref_audio = gr.Audio(sources=["upload", "microphone"], type="numpy", label="Audio de référence")
            ref_text = gr.Textbox(label="Texte du prefix (si connu)", placeholder="Transcription du prefix (optionnel)")
        with gr.Accordion("Options avancées", open=False):
            with gr.Row():
                steps = gr.Slider(1, 50, value=10, step=1, label="num_steps")
                cfg = gr.Slider(0.5, 3.0, value=1.4, step=0.05, label="CFG (guidance)")
                cfg_ref = gr.Slider(0.5, 3.0, value=1.0, step=0.05, label="CFG (réf.)")
            with gr.Row():
                temperature = gr.Slider(0.1, 2.0, value=1.0, step=0.05, label="Température")
                max_seq_len = gr.Slider(50, 1200, value=300, step=10, label="max_seq_len (tokens audio)")
                seed = gr.Number(value=0, precision=0, label="Seed")
                lang_hint = gr.Dropdown(choices=["fr", "en"], value="fr", label="Langue (normalisation)")
        with gr.Row():
            debug = gr.Checkbox(value=False, label="Mode debug")
            adv_sampling = gr.Checkbox(value=False, label="Sampling avancé (Velocity Head)")

        btn = gr.Button("Synthétiser")
        out_audio = gr.Audio(label="Sortie audio", type="numpy")
        logs_box = gr.Textbox(label="Logs (live)", lines=28)

        demo.queue(default_concurrency_limit=1, max_size=32)
        btn.click(
            fn=synthesize,
            inputs=[text, debug, adv_sampling, ref_audio, ref_text, steps, cfg, cfg_ref, temperature, max_seq_len, seed, lang_hint],
            outputs=[out_audio, logs_box],
            api_name="synthesize",
        )
    return demo

if __name__ == "__main__":
    build_demo().launch(ssr_mode=False)
# retrigger 2025-10-30T15:17:49+01:00
# retrigger 2025-10-30T16:37:47+01:00