Upload finetune_vibevoice_lora105.py

VibeVoice-finetuning/src/finetune_vibevoice_lora105.py

@@ -0,0 +1,1044 @@
+# train_vibevoice_lora.py
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import logging
+import os
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from datasets import load_dataset, DatasetDict, VerificationMode
+
+from transformers import (
+    HfArgumentParser,
+    Trainer,
+    set_seed,
+    TrainerCallback,
+)
+from transformers import TrainingArguments as HfTrainingArguments
+
+from peft import LoraConfig, get_peft_model, TaskType
+
+from vibevoice.modular.modeling_vibevoice import VibeVoiceForConditionalGeneration
+from vibevoice.modular.configuration_vibevoice import VibeVoiceConfig
+from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor
+
+from data_vibevoice import VibeVoiceDataset, VibeVoiceCollator
+
+logger = logging.getLogger(__name__)
+
+# ================== SAMPLE CALLBACK UTILS ==================
+
+import copy
+import torch
+from transformers import TrainerCallback
+
+class EmaCallback(TrainerCallback):
+    def __init__(self, attr_path="model.prediction_head", decay=0.999, device="cuda"):
+        """
+        attr_path: where the head lives under self.model (Trainer wraps your VibeVoiceForConditionalGeneration)
+        decay:     EMA decay (0.999 ~ stable, 0.9999 ~ very smooth, slower to adapt)
+        """
+        self.attr_path = attr_path
+        self.decay = float(decay)
+        self.device = torch.device(device)
+        self.shadow = None
+        self._orig = None  # store non-EMA weights when we swap
+
+    def _get_module(self, model):
+        # Resolve dotted path like "model.prediction_head"
+        mod = model
+        for name in self.attr_path.split('.'):
+            mod = getattr(mod, name)
+        return mod
+
+    def on_train_begin(self, args, state, control, model=None, **kwargs):
+        head = self._get_module(model)
+        self.shadow = {k: p.detach().to(self.device).clone()
+                       for k, p in head.state_dict().items()}
+
+    def on_step_end(self, args, state, control, model=None, **kwargs):
+        if self.shadow is None: return
+        head = self._get_module(model)
+        with torch.no_grad():
+            for k, v in head.state_dict().items():
+                self.shadow[k].mul_(self.decay).add_(v.detach().to(self.device), alpha=(1.0 - self.decay))
+
+    # ---- Swap helpers ----
+    def _swap_in_ema(self, model):
+        head = self._get_module(model)
+        self._orig = copy.deepcopy(head.state_dict())
+        head.load_state_dict(self.shadow, strict=False)
+
+    def _swap_back(self, model):
+        if self._orig is None: return
+        head = self._get_module(model)
+        head.load_state_dict(self._orig, strict=False)
+        self._orig = None
+
+    def on_evaluate(self, args, state, control, model=None, **kwargs):
+        # use EMA during eval
+        self._swap_in_ema(model)
+
+    def on_evaluate_end(self, args, state, control, model=None, **kwargs):
+        self._swap_back(model)
+
+    def on_save(self, args, state, control, model=None, **kwargs):
+        # temporarily swap to EMA, let Trainer save, then swap back
+        self._swap_in_ema(model)
+
+    def on_save_end(self, args, state, control, model=None, **kwargs):
+        self._swap_back(model)
+
+    def on_train_end(self, args, state, control, model=None, **kwargs):
+        # final checkpoint: persist EMA
+        self._swap_in_ema(model)
+
+
+@dataclass
+class ModelArguments:
+    model_name_or_path: Optional[str] = field(
+        default=None, metadata={"help": "Path to VibeVoice base model with config.json"}
+    )
+    processor_name_or_path: Optional[str] = field(
+        default=None, metadata={"help": "Path to processor dir (preprocessor_config.json). Defaults to model path."}
+    )
+    cache_dir: Optional[str] = field(default=None)
+    freeze_acoustic_tokenizer: bool = field(default=True)
+    freeze_semantic_tokenizer: bool = field(default=True)
+    lora_r: int = field(default=8)
+    lora_alpha: int = field(default=32)
+    lora_dropout: float = field(default=0.05)
+    lora_target_modules: str = field(
+        default="q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj",
+        metadata={"help": "Comma-separated list of target module names in the LLM blocks"},
+    )
+    lora_wrap_diffusion_head: bool = field(default=False, metadata={"help": "Wrap diffusion head with PEFT LoRA"})
+    train_diffusion_head: bool = field(default=False, metadata={"help": "Train diffusion prediction head (full fine-tune)"})
+    train_connectors: bool = field(default=False, metadata={"help": "Train acoustic/semantic connectors (full fine-tune)"})
+    layers_to_freeze: Optional[str] = field(
+        default=None, 
+        metadata={"help": "Comma-separated indices of diffusion head layers to freeze (e.g., '0,1,5,7,8')."}
+    )
+
+@dataclass
+class DataArguments:
+    dataset_name: Optional[str] = field(default=None, metadata={"help": "HF dataset name or 'json' with --train_jsonl for local files"})
+    dataset_config_name: Optional[str] = field(default=None)
+    train_split_name: str = field(default="train")
+    eval_split_name: Optional[str] = field(default="validation")
+    text_column_name: str = field(default="text")
+    audio_column_name: str = field(default="audio")
+    voice_prompts_column_name: Optional[str] = field(default="voice_prompts")
+    eval_split_size: float = field(default=0.0)
+    ignore_verifications: bool = field(default=False)
+    max_length: Optional[int] = field(default=None)
+    train_jsonl: Optional[str] = field(default=None, metadata={"help": "Path to local train JSONL with {text, audio, [voice_prompts]}"})
+    validation_jsonl: Optional[str] = field(default=None, metadata={"help": "Optional path to local validation JSONL"})
+    voice_prompt_drop_rate: float = field(
+        default=0.0,
+        metadata={"help": "Probability to drop conditioning voice prompt during training (0.0 keep always, 1.0 drop always)."},
+    )
+
+@dataclass
+class CustomTrainingArguments(HfTrainingArguments):
+    ddpm_batch_mul: int = field(default=1)
+    ce_loss_weight: float = field(default=1.0)
+    diffusion_loss_weight: float = field(default=1.0)
+    debug_ce_details: bool = field(default=False)
+    debug_ce_topk: int = field(default=5)
+    debug_ce_max_examples: int = field(default=1)
+    debug_ce_every_n_steps: int = field(default=200)
+    gradient_clipping: bool = field(
+        default=False,
+        metadata={"help": "Enable gradient clipping using max_grad_norm (set via --max_grad_norm, default 1.0). When False, disables clipping by forcing max_grad_norm=0.0."},
+    )
+    debug_save: bool = field(
+        default=False,
+        metadata={"help": "If set, saves model components BEFORE training starts, into output_dir/debug_initial."},
+    )
+
+def build_lora_config(args: ModelArguments) -> LoraConfig:
+    target_modules = [s.strip() for s in args.lora_target_modules.split(",") if s.strip()]
+    return LoraConfig(
+        r=args.lora_r,
+        lora_alpha=args.lora_alpha,
+        lora_dropout=args.lora_dropout,
+        bias="none",
+        task_type=TaskType.CAUSAL_LM,
+        target_modules=target_modules,
+    )
+
+def build_head_lora_config(args: ModelArguments) -> LoraConfig:
+    target_modules = ["noisy_images_proj","cond_proj","gate_proj","up_proj","down_proj","linear"]
+    return LoraConfig(
+        r=args.lora_r,
+        lora_alpha=args.lora_alpha,
+        lora_dropout=args.lora_dropout,
+        bias="none",
+        task_type=TaskType.FEATURE_EXTRACTION,
+        target_modules=target_modules,
+    )
+
+def mask_for_ce(labels: torch.Tensor, attention_mask: torch.Tensor, acoustic_input_mask: torch.Tensor, pad_id: int = -100) -> torch.Tensor:
+    shifted = labels[:, 1:].contiguous()
+    base_mask = attention_mask[:, 1:].contiguous().eq(1) if (attention_mask is not None and attention_mask.numel() > 0) else torch.ones_like(shifted, dtype=torch.bool)
+    label_is_acoustic = acoustic_input_mask[:, 1:].contiguous()
+    final_mask = base_mask & (~label_is_acoustic)
+    out = shifted.clone()
+    out[~final_mask] = pad_id
+    return out
+
+def _patch_acoustic_encode_for_legacy_indexing(model_obj, logger_):
+    try:
+        acoustic = getattr(getattr(model_obj, "model", model_obj), "acoustic_tokenizer", None)
+        if acoustic is None or not hasattr(acoustic, "encode"):
+            logger_.warning("No acoustic_tokenizer.encode() found to patch.")
+            return
+        base_encode = acoustic.encode
+        def encode_wrapped(*args, **kwargs):
+            out = base_encode(*args, **kwargs)
+            try:
+                _ = out[0][0]
+                return out
+            except Exception:
+                pass
+            if isinstance(out, dict):
+                for k in ("frames", "codes", "tokens", "latents", "hidden_states"):
+                    if k in out:
+                        return [[out[k]]]
+                if len(out) > 0:
+                    return [[next(iter(out.values()))]]
+            for attr in ("frames", "codes", "tokens", "latents", "hidden_states"):
+                if hasattr(out, attr):
+                    return [[getattr(out, attr)]]
+            try:
+                if isinstance(out, torch.Tensor):
+                    return [[out]]
+            except Exception:
+                pass
+            return [[out]]
+        acoustic.encode = encode_wrapped
+        logger_.info("Patched acoustic_tokenizer.encode() to return [[...]] for legacy indexing.")
+    except Exception as e:
+        logger_.warning(f"Failed to patch acoustic_tokenizer.encode(): {e}")
+
+def main() -> None:
+    parser = HfArgumentParser((ModelArguments, DataArguments, CustomTrainingArguments))
+    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
+    )
+    logger.info("Training/evaluation parameters %s", training_args)
+    set_seed(training_args.seed)
+
+    # Configure gradient clipping
+    if not getattr(training_args, "gradient_clipping", False):
+        if hasattr(training_args, "max_grad_norm"):
+            training_args.max_grad_norm = 0.0
+            logger.info("Gradient clipping disabled (set max_grad_norm=0.0). Use --gradient_clipping to enable.")
+    else:
+        if (not hasattr(training_args, "max_grad_norm")) or training_args.max_grad_norm is None or training_args.max_grad_norm <= 0:
+            training_args.max_grad_norm = 1.0
+        logger.info(f"Gradient clipping enabled: max_grad_norm={training_args.max_grad_norm}")
+
+    # Load processor
+    processor_path = model_args.processor_name_or_path or model_args.model_name_or_path
+    if processor_path is None:
+        raise ValueError("--model_name_or_path (or --processor_name_or_path) must be provided")
+    processor: VibeVoiceProcessor = VibeVoiceProcessor.from_pretrained(processor_path)
+
+    # Required special tokens
+    tok = processor.tokenizer
+    for required in ["speech_start_id", "speech_diffusion_id", "speech_end_id"]:
+        if not hasattr(tok, required) or getattr(tok, required) is None:
+            raise RuntimeError(f"Tokenizer missing required special id: {required}")
+
+    # Load model
+    if model_args.model_name_or_path is None:
+        raise ValueError("--model_name_or_path is required to load VibeVoice base model")
+    dtype = torch.float32
+    if training_args.bf16:
+        dtype = torch.bfloat16
+    elif getattr(training_args, "fp16", False):
+        dtype = torch.float16
+    model = VibeVoiceForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        torch_dtype=dtype, device_map={"": 0},
+    )
+    _patch_acoustic_encode_for_legacy_indexing(model, logger)
+    processor.semantic_tokenizer = getattr(model.model, "semantic_tokenizer", None)
+
+    # Diagnostics: LM head tie
+    try:
+        in_emb_mod = model.get_input_embeddings()
+        out_emb_mod = model.get_output_embeddings()
+        in_w = getattr(in_emb_mod, "weight", None)
+        out_w = getattr(out_emb_mod, "weight", None)
+        shared_ptr = bool(in_w is not None and out_w is not None and in_w.data_ptr() == out_w.data_ptr())
+        values_equal = False
+        if in_w is not None and out_w is not None and in_w.shape == out_w.shape:
+            try:
+                values_equal = bool(torch.allclose(in_w, out_w))
+            except Exception:
+                values_equal = False
+        try:
+            tie_cfg = getattr(getattr(model.config, "decoder_config", model.config), "tie_word_embeddings", None)
+        except Exception:
+            tie_cfg = getattr(model.config, "tie_word_embeddings", None)
+        logger.info(f"LM head diagnostics -> shared_params={shared_ptr}, values_equal={values_equal}, tie_word_embeddings={tie_cfg}")
+        if out_w is not None:
+            logger.info(f"LM head requires_grad before freeze: {bool(out_w.requires_grad)}")
+    except Exception as e:
+        logger.warning(f"LM head tie diagnostics failed: {e}")
+
+    # Hard-tie LM head
+    try:
+        emb_module = model.get_input_embeddings()
+        head_module = model.get_output_embeddings()
+        if hasattr(emb_module, "weight") and hasattr(head_module, "weight"):
+            if emb_module.weight.shape == head_module.weight.shape and emb_module.weight.data_ptr() != head_module.weight.data_ptr():
+                with torch.no_grad():
+                    head_module.weight = emb_module.weight
+                logger.info("Force-tied LM head weight to input embeddings (pointer share).")
+    except Exception as e:
+        logger.warning(f"Force-tie of LM head failed: {e}")
+
+    # Validate special IDs (info logs only)
+    try:
+        special_names = ["speech_start_id", "speech_diffusion_id", "speech_end_id"]
+        try:
+            vocab_size = int(getattr(model.config.decoder_config, "vocab_size", 0))
+        except Exception:
+            vocab_size = 0
+        in_emb_mod = model.get_input_embeddings()
+        out_emb_mod = model.get_output_embeddings()
+        in_w = getattr(in_emb_mod, "weight", None)
+        out_w = getattr(out_emb_mod, "weight", None)
+        for name in special_names:
+            val = getattr(tok, name, None)
+            exists = (val is not None)
+            in_range = (exists and isinstance(val, int) and 0 <= val < vocab_size)
+            equal_row = None
+            if in_range and in_w is not None and out_w is not None and in_w.shape == out_w.shape and in_w.size(0) > val:
+                try:
+                    equal_row = bool(torch.allclose(in_w[val], out_w[val]))
+                except Exception:
+                    equal_row = False
+            decoded_str = None
+            if exists and isinstance(val, int):
+                try:
+                    decoded_str = tok.decode([val])
+                except Exception:
+                    try:
+                        decoded_str = tok.convert_ids_to_tokens(val)
+                    except Exception:
+                        decoded_str = "<decode_failed>"
+            logger.info(f"Special token check -> {name}={val}, decoded='{decoded_str}', exists={exists}, in_vocab_range={in_range}, emb_vs_head_row_equal={equal_row}")
+    except Exception as e:
+        logger.warning(f"Special token ID/row validation failed: {e}")
+
+    # Quick tokenizer diagnostics (optional)
+    try:
+        logger.info("=== TOKENIZER DIAGNOSTICS ===")
+        logger.info(f"Tokenizer class: {type(tok).__name__}")
+        logger.info(f"Tokenizer vocab_size: {tok.vocab_size}")
+        # tiny CE smoke test
+        with torch.no_grad():
+            simple_text = "The cat sat on the mat."
+            simple_ids = torch.tensor([tok.encode(simple_text, add_special_tokens=True)], device=model.device)
+            simple_mask = torch.ones_like(simple_ids)
+            x = model.get_input_embeddings()(simple_ids)
+            outputs = model.model(inputs_embeds=x, attention_mask=simple_mask, return_dict=True)
+            logits = model.lm_head(outputs.last_hidden_state)
+            shift_logits = logits[:, :-1, :].contiguous()
+            shift_labels = simple_ids[:, 1:].contiguous()
+            ce_loss = F.cross_entropy(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1), reduction='mean')
+            logger.info(f"Simple text CE loss: {ce_loss.item():.4f}")
+    except Exception as e:
+        logger.warning(f"Tokenizer diagnostics failed: {e}")
+
+    # Disable cache during training
+    if hasattr(model.config, "use_cache") and training_args.do_train:
+        model.config.use_cache = False
+
+    # Freeze tokenizers
+    if model_args.freeze_acoustic_tokenizer and hasattr(model.model, "acoustic_tokenizer"):
+        for p in model.model.acoustic_tokenizer.parameters():
+            p.requires_grad = False
+    if model_args.freeze_semantic_tokenizer and hasattr(model.model, "semantic_tokenizer"):
+        for p in model.model.semantic_tokenizer.parameters():
+            p.requires_grad = False
+
+    # LoRA wrap LLM (optional)
+    lora_cfg = build_lora_config(model_args)
+    tm_lower = [s.strip().lower() for s in model_args.lora_target_modules.split(",") if s.strip()]
+    skip_lm_lora = (len(tm_lower) == 0) or all(t in ("none", "off", "disable", "disabled") for t in tm_lower)
+    if not skip_lm_lora:
+        model.model.language_model = get_peft_model(model.model.language_model, lora_cfg)
+    else:
+        logger.info("Skipping LLM LoRA wrapping (lora_target_modules indicates none).")
+
+    try:
+        model.tie_weights()
+    except Exception:
+        pass
+
+    # Freeze all then enable trainable subsets
+    for _, p in model.named_parameters():
+        p.requires_grad = False
+
+    try:
+        for n, p in model.model.language_model.named_parameters():
+            if "lora_A" in n or "lora_B" in n:
+                p.requires_grad = True
+    except Exception:
+        logger.warning("Could not re-enable LoRA params on language_model.")
+
+    # Diffusion head LoRA wrapping (optional)
+    if getattr(model_args, "lora_wrap_diffusion_head", False) and hasattr(model.model, "prediction_head"):
+        class _HeadForwardShim(nn.Module):
+            def __init__(self, base: nn.Module): super().__init__(); self.base = base
+            def forward(self, *args, **kwargs):
+                if len(args) >= 3:
+                    noisy_images, timesteps, condition = args[:3]
+                else:
+                    noisy_images = kwargs.get("noisy_images")
+                    timesteps = kwargs.get("timesteps")
+                    condition = kwargs.get("condition")
+                return self.base(noisy_images, timesteps, condition)
+        try:
+            shim = _HeadForwardShim(model.model.prediction_head)
+            model.model.prediction_head = get_peft_model(shim, build_head_lora_config(model_args))
+            for n, p in model.model.prediction_head.named_parameters():
+                if "lora_A" in n or "lora_B" in n:
+                    p.requires_grad = True
+        except Exception as e:
+            logger.warning(f"Could not LoRA-wrap diffusion head: {e}")
+
+    # Train full diffusion head (optional)
+    if getattr(model_args, "train_diffusion_head", False) and hasattr(model.model, "prediction_head"):
+        for p in model.model.prediction_head.parameters():
+            p.requires_grad = True
+
+    # Freeze diffusion head layers (optional)
+    if model_args.layers_to_freeze is not None and hasattr(model.model, "prediction_head"):
+        head_params = list(model.model.prediction_head.named_parameters())
+        try:
+            indices_to_freeze = {int(x.strip()) for x in model_args.layers_to_freeze.split(',') if x.strip()}
+            frozen_count = 0
+            for i, (name, param) in enumerate(head_params):
+                if i in indices_to_freeze:
+                    param.requires_grad = False
+                    frozen_count += 1
+                    logger.info(f"Froze layer [{i}]: {name}")
+            logger.info(f"Successfully froze {frozen_count} parameter groups in the diffusion head.")
+        except Exception as e:
+            logger.error(f"Could not parse --layers_to_freeze: {e}")
+            raise
+    
+    # Connectors
+    if getattr(model_args, "train_connectors", False):
+        if hasattr(model.model, "acoustic_connector"):
+            for p in model.model.acoustic_connector.parameters():
+                p.requires_grad = True
+        if hasattr(model.model, "semantic_connector"):
+            for p in model.model.semantic_connector.parameters():
+                p.requires_grad = True
+    else:
+        if hasattr(model.model, "acoustic_connector"):
+            for p in model.model.acoustic_connector.parameters():
+                p.requires_grad = False
+        if hasattr(model.model, "semantic_connector"):
+            for p in model.model.semantic_connector.parameters():
+                p.requires_grad = False
+
+    # Freeze embedding + head
+    try:
+        emb = model.get_input_embeddings()
+        if hasattr(emb, "weight"):
+            emb.weight.requires_grad_(False)
+        head = model.get_output_embeddings()
+        if head is not None and hasattr(head, "weight"):
+            head.weight.requires_grad_(False)
+    except Exception:
+        pass
+
+    # Diagnostics
+    def _sum_params(named_iter):
+        return sum(p.numel() for _, p in named_iter if p.requires_grad)
+    try:
+        lm_lora = _sum_params(model.model.language_model.named_parameters()) if hasattr(model.model, "language_model") else 0
+        pred_head_train = _sum_params(model.model.prediction_head.named_parameters()) if hasattr(model.model, "prediction_head") else 0
+        ac_conn_train = _sum_params(model.model.acoustic_connector.named_parameters()) if hasattr(model.model, "acoustic_connector") else 0
+        se_conn_train = _sum_params(model.model.semantic_connector.named_parameters()) if hasattr(model.model, "semantic_connector") else 0
+        total_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        logger.info(f"Trainable by block -> LLM-LoRA: {lm_lora:,} | diff_head: {pred_head_train:,} | ac_conn: {ac_conn_train:,} | se_conn: {se_conn_train:,}")
+        logger.info("TOTAL trainable: %s", f"{total_trainable:,}")
+    except Exception:
+        pass
+
+    # Preprocessed data classes
+    class PreprocessedBatchDataset:
+        def __init__(self, preprocessed_file: str):
+            self.data = torch.load(preprocessed_file, map_location='cpu')
+            logger.info(f"Loaded {len(self.data)} preprocessed batches from {preprocessed_file}")
+        
+        def __len__(self):
+            return len(self.data)
+        
+        def __getitem__(self, idx):
+            batch = self.data[idx]
+            result = {}
+            for k, v in batch.items():
+                if isinstance(v, torch.Tensor):
+                    result[k] = v
+                else:
+                    result[k] = v
+            return result
+
+    class PreprocessedBatchSubset:
+        def __init__(self, dataset: 'PreprocessedBatchDataset', indices: List[int]):
+            self.dataset = dataset
+            self.indices = indices
+        
+        def __len__(self):
+            return len(self.indices)
+        
+        def __getitem__(self, idx):
+            actual_idx = self.indices[idx]
+            return self.dataset[actual_idx]
+
+    class PreprocessedBatchCollator:
+        def __call__(self, batch: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+            if not batch:
+                return {}
+            result = {}
+            for key in batch[0].keys():
+                tensors = [b[key] for b in batch if b[key] is not None]
+                if tensors and isinstance(tensors[0], torch.Tensor):
+                    result[key] = torch.cat(tensors, dim=0)
+                else:
+                    result[key] = tensors[0] if tensors else None
+            return result
+
+    # Datasets
+    preprocessed_dir = os.path.join(training_args.output_dir, "preprocessed")
+    preprocessed_file = os.path.join(preprocessed_dir, "preprocessed_batches.pt")
+    
+    if os.path.exists(preprocessed_file):
+        logger.info(f"Loading preprocessed data from {preprocessed_file}")
+        preprocessed_data = PreprocessedBatchDataset(preprocessed_file)
+        
+        train_dataset = preprocessed_data
+        eval_dataset = None
+        
+        if training_args.do_eval and data_args.eval_split_size and data_args.eval_split_size > 0 and len(preprocessed_data) > 1:
+            num_eval = max(1, int(len(preprocessed_data) * data_args.eval_split_size))
+            num_train = len(preprocessed_data) - num_eval
+            indices = list(range(len(preprocessed_data)))
+            import random
+            random.Random(training_args.seed).shuffle(indices)
+            train_indices = indices[:num_train]
+            eval_indices = indices[num_train:]
+            train_dataset = PreprocessedBatchSubset(preprocessed_data, train_indices)
+            eval_dataset = PreprocessedBatchSubset(preprocessed_data, eval_indices)
+    else:
+        logger.info(f"Preprocessed data not found at {preprocessed_file}, loading from raw JSONL/HF datasets")
+        verification_mode = VerificationMode.NO_CHECKS if data_args.ignore_verifications else VerificationMode.BASIC_CHECKS
+        if data_args.train_jsonl is not None:
+            data_files: Dict[str, str] = {"train": data_args.train_jsonl}
+            if data_args.validation_jsonl is not None:
+                data_files["validation"] = data_args.validation_jsonl
+            raw = load_dataset("json", data_files=data_files, verification_mode=verification_mode, cache_dir=model_args.cache_dir)
+        else:
+            if data_args.dataset_name is None:
+                raise ValueError("Provide --dataset_name (HF datasets) or use --train_jsonl/--validation_jsonl for local files.")
+            raw = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                verification_mode=verification_mode,
+                cache_dir=model_args.cache_dir,
+            )
+        train_ds = raw[data_args.train_split_name]
+        eval_ds = None
+        if training_args.do_eval:
+            if data_args.eval_split_name and data_args.eval_split_name in raw:
+                eval_ds = raw[data_args.eval_split_name]
+            elif data_args.eval_split_size and data_args.eval_split_size > 0 and len(train_ds) > 1:
+                split = train_ds.train_test_split(test_size=data_args.eval_split_size, seed=training_args.seed)
+                train_ds, eval_ds = split["train"], split["test"]
+
+        train_dataset = VibeVoiceDataset(
+            train_ds,
+            text_column=data_args.text_column_name,
+            audio_column=data_args.audio_column_name,
+            voice_prompts_column=data_args.voice_prompts_column_name,
+        )
+        eval_dataset = None
+        if eval_ds is not None:
+            eval_dataset = VibeVoiceDataset(
+                eval_ds,
+                text_column=data_args.text_column_name,
+                audio_column=data_args.audio_column_name,
+                voice_prompts_column=data_args.voice_prompts_column_name,
+            )
+
+    # Ratios/dims from processor+model
+    speech_compress_ratio = getattr(processor, "speech_tok_compress_ratio", 3200)
+    semantic_dim = getattr(model.config, "semantic_vae_dim", None)
+    if semantic_dim is None:
+        try:
+            semantic_dim = int(getattr(model.config.semantic_tokenizer_config, "vae_dim", 128))
+        except Exception:
+            semantic_dim = 128
+
+    compute_semantics_flag = hasattr(processor, "semantic_tokenizer") and processor.semantic_tokenizer is not None
+
+    if os.path.exists(preprocessed_file):
+        data_collator = PreprocessedBatchCollator()
+    else:
+        data_collator = VibeVoiceCollator(
+            processor=processor,
+            max_length=data_args.max_length,
+            speech_compress_ratio=speech_compress_ratio,
+            semantic_vae_dim=semantic_dim,
+            compute_semantics=compute_semantics_flag,
+            debug_checks=False,
+            voice_prompt_drop_rate=data_args.voice_prompt_drop_rate,
+        )
+
+    class LoRADebugCallback(TrainerCallback):
+        def __init__(self, log_every_n_steps: int = 50):
+            self.log_every_n_steps = max(1, int(log_every_n_steps))
+            self.prev_param_norms: Dict[str, float] = {}
+            self.lora_param_names: List[str] = []
+
+        def on_train_begin(self, args, state, control, model=None, **kwargs):
+            try:
+                if model is None:
+                    return
+                named: Dict[str, torch.nn.Parameter] = dict(model.named_parameters())
+                self.lora_param_names = [n for n in named.keys() if ("lora_A" in n or "lora_B" in n)]
+                for n in self.lora_param_names:
+                    p = named[n]
+                    self.prev_param_norms[n] = float(p.data.norm().item())
+                total = len(self.lora_param_names)
+                req_grad = sum(1 for n in self.lora_param_names if named[n].requires_grad)
+                num_A = sum(1 for n in self.lora_param_names if "lora_A" in n)
+                num_B = sum(1 for n in self.lora_param_names if "lora_B" in n)
+                zero_B = sum(1 for n in self.lora_param_names if ("lora_B" in n and float(named[n].data.norm().item()) == 0.0))
+                logger.info(f"LoRA debug: found {total} LoRA params (A={num_A}, B={num_B}); trainable={req_grad}. Initial lora_B_zero={zero_B}.")
+                if total == 0:
+                    logger.warning("LoRA debug: No LoRA parameters found. Check lora_target_modules.")
+                if req_grad != total:
+                    logger.warning("LoRA debug: Some LoRA params are frozen. They should be trainable.")
+            except Exception as e:
+                logger.warning(f"LoRA debug (on_train_begin) failed: {e}")
+
+        def on_step_end(self, args, state, control, model=None, **kwargs):
+            try:
+                if model is None or len(self.lora_param_names) == 0:
+                    return
+                step = int(getattr(state, "global_step", 0) or 0)
+                if step % self.log_every_n_steps != 0 and step != 1:
+                    return
+                named: Dict[str, torch.nn.Parameter] = dict(model.named_parameters())
+                changed_A = 0
+                changed_B = 0
+                zero_B = 0
+                eps = 1e-12
+                for n in self.lora_param_names:
+                    p = named.get(n, None)
+                    if p is None:
+                        continue
+                    prev = self.prev_param_norms.get(n, 0.0)
+                    curr = float(p.data.norm().item())
+                    if "lora_A" in n and abs(curr - prev) > eps:
+                        changed_A += 1
+                    if "lora_B" in n:
+                        if abs(curr - prev) > eps:
+                            changed_B += 1
+                        if curr == 0.0:
+                            zero_B += 1
+                    self.prev_param_norms[n] = curr
+                total_A = sum(1 for n in self.lora_param_names if "lora_A" in n)
+                total_B = sum(1 for n in self.lora_param_names if "lora_B" in n)
+                logger.info(f"LoRA debug step {step}: changed A {changed_A}/{total_A}, changed B {changed_B}/{total_B}, lora_B_zero_now={zero_B}.")
+            except Exception as e:
+                logger.warning(f"LoRA debug (on_step_end) failed: {e}")
+
+    class VibeVoiceTrainer(Trainer):
+        def compute_loss(self, model: VibeVoiceForConditionalGeneration, inputs: Dict[str, Any], return_outputs=False, num_items_in_batch: Optional[int] = None):
+            labels = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask")
+            acoustic_input_mask = inputs.get("acoustic_input_mask")
+
+            # Ensure semantic tensors exist and have correct dtype/device
+            sem = inputs.get("speech_semantic_tensors", None)
+            try:
+                target_dtype = next(model.model.semantic_connector.parameters()).dtype
+            except Exception:
+                target_dtype = model.get_input_embeddings().weight.dtype
+
+            if sem is None:
+                sm = inputs.get("speech_masks")
+                if sm is not None:
+                    zeros = torch.zeros(
+                        sm.size(0), sm.size(1),
+                        getattr(model.config, "semantic_vae_dim", 128),
+                        dtype=target_dtype,
+                        device=sm.device,
+                    )
+                    inputs["speech_semantic_tensors"] = zeros
+            else:
+                if isinstance(sem, torch.Tensor):
+                    inputs["speech_semantic_tensors"] = sem.to(dtype=target_dtype)
+
+            outputs = model(
+                input_ids=inputs.get("input_ids"),
+                attention_mask=attention_mask,
+                speech_tensors=inputs.get("speech_tensors"),
+                speech_masks=inputs.get("speech_masks"),
+                speech_semantic_tensors=inputs.get("speech_semantic_tensors"),
+                acoustic_input_mask=acoustic_input_mask,
+                acoustic_loss_mask=inputs.get("acoustic_loss_mask"),
+                speeches_loss_input=inputs.get("speeches_loss_input"),
+                ddpm_batch_mul=training_args.ddpm_batch_mul,
+            )
+
+            # Invariants: token/latent selection equality across views (warn, don't assert)
+            try:
+                al_mask = inputs.get("acoustic_loss_mask")
+                sp_masks = inputs.get("speech_masks")
+                sp_loss_sel = inputs.get("speeches_loss_input")
+                num_tok_total = int(acoustic_input_mask.sum().item()) if acoustic_input_mask is not None else 0
+                num_tok_loss = int(al_mask.sum().item()) if al_mask is not None else 0
+                num_lat_total = int(sp_masks.sum().item()) if sp_masks is not None else 0
+                num_lat_loss = int(((sp_loss_sel & sp_masks).sum().item())) if (sp_loss_sel is not None and sp_masks is not None) else 0
+                self.log({
+                    "debug/num_tok_total": float(num_tok_total),
+                    "debug/num_tok_loss": float(num_tok_loss),
+                    "debug/num_lat_total": float(num_lat_total),
+                    "debug/num_lat_loss": float(num_lat_loss),
+                })
+                if sp_loss_sel is not None and sp_masks is not None and al_mask is not None:
+                    if num_tok_loss != num_lat_loss:
+                        logger.warning(f"Loss selection mismatch: acoustic_loss_mask={num_tok_loss} vs speeches_loss_input={num_lat_loss}")
+            except Exception:
+                pass
+
+            # CE Loss
+            logits = outputs.logits
+            ce_labels = mask_for_ce(labels, attention_mask, acoustic_input_mask, pad_id=-100)
+            shift_logits = logits[:, :-1, :].contiguous()
+            loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
+            ce_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), ce_labels.view(-1))
+
+            # Optional CE diagnostics
+            try:
+                self._debug_ce(shift_logits, ce_labels, attention_mask, acoustic_input_mask)
+            except Exception as e:
+                logger.warning(f"Failed invoking CE debug: {e}")
+
+            # Diffusion loss
+            diffusion_loss = outputs.diffusion_loss if outputs.diffusion_loss is not None else torch.tensor(0.0, device=ce_loss.device)
+            total = training_args.ce_loss_weight * ce_loss + training_args.diffusion_loss_weight * diffusion_loss
+
+            # Logs
+            try:
+                prefix = "train" if model.training else "eval"
+                self.log({
+                    f"{prefix}/ce_loss": ce_loss.detach().item(),
+                    f"{prefix}/diffusion_loss": diffusion_loss.detach().item() if isinstance(diffusion_loss, torch.Tensor) else float(diffusion_loss),
+                })
+                if hasattr(self, "optimizer") and self.optimizer is not None and len(self.optimizer.param_groups) > 0:
+                    lr_val = self.optimizer.param_groups[0].get("lr", None)
+                    if lr_val is not None:
+                        self.log({"train/learning_rate_real": float(lr_val)})
+            except Exception:
+                pass
+
+            return (total, outputs) if return_outputs else total
+
+        def _debug_ce(self, shift_logits: torch.Tensor, ce_labels: torch.Tensor, attention_mask: Optional[torch.Tensor], acoustic_input_mask: Optional[torch.Tensor]):
+            try:
+                if not getattr(training_args, "debug_ce_details", False):
+                    return
+                step = int(getattr(self.state, "global_step", 0) or 0)
+                every_n = max(1, int(getattr(training_args, "debug_ce_every_n_steps", 200) or 200))
+                if not (step <= 1 or (step % every_n == 0)):
+                    return
+
+                with torch.no_grad():
+                    vocab = shift_logits.size(-1)
+                    per_token_loss = F.cross_entropy(
+                        shift_logits.view(-1, vocab),
+                        ce_labels.view(-1),
+                        reduction="none",
+                        ignore_index=-100,
+                    ).view_as(ce_labels)
+
+                    valid_mask = ce_labels.ne(-100)
+                    num_valid = int(valid_mask.sum().item())
+                    avg_loss = float((per_token_loss[valid_mask].mean().item())) if num_valid > 0 else float("nan")
+
+                    per_ex_avgs = []
+                    max_examples = max(1, int(getattr(training_args, "debug_ce_max_examples", 1) or 1))
+                    B = ce_labels.size(0)
+                    for b in range(min(B, max_examples)):
+                        vb = valid_mask[b]
+                        if int(vb.sum().item()) > 0:
+                            per_ex_avgs.append(float(per_token_loss[b][vb].mean().item()))
+                        else:
+                            per_ex_avgs.append(float("nan"))
+                    logger.info(f"CE debug: tokens_in_loss={num_valid}, avg_loss={avg_loss:.4f}, per_example_avgs={[round(x,4) if x==x else None for x in per_ex_avgs]}")
+            except Exception as e:
+                logger.warning(f"CE detailed debug failed: {e}")
+
+        # --------- CRITICAL SAVE OVERRIDES: also dump FULL head/connectors for inference ---------
+  
+
+        def _save(self, output_dir: Optional[str] = None, state_dict=None) -> None:
+            try:
+                target_dir = output_dir or self.args.output_dir
+                lora_out = os.path.join(target_dir, "lora")
+                os.makedirs(lora_out, exist_ok=True)
+    
+                # --- LLM PEFT adapters (if LoRA-wrapped) ---
+                language_model = getattr(self.model.model, "language_model", None)
+                if hasattr(language_model, "save_pretrained"):
+                    language_model.save_pretrained(lora_out)
+    
+                # --- Diffusion head PEFT adapters (if LoRA-wrapped) ---
+                pred_head = getattr(self.model.model, "prediction_head", None)
+                if hasattr(pred_head, "save_pretrained"):
+                    ph_dir = os.path.join(lora_out, "diffusion_head")
+                    os.makedirs(ph_dir, exist_ok=True)
+                    pred_head.save_pretrained(ph_dir)
+    
+                # --- ALWAYS save FULL diffusion head state_dict for fallback ---
+                if pred_head is not None and hasattr(pred_head, "state_dict"):
+                    sd = pred_head.state_dict()
+                    torch.save(sd, os.path.join(lora_out, "diffusion_head_full.bin"))
+                    ph_dir = os.path.join(lora_out, "diffusion_head")
+                    os.makedirs(ph_dir, exist_ok=True)
+                    torch.save(sd, os.path.join(ph_dir, "diffusion_head_full.bin"))
+    
+                # --- Connectors (plain state_dicts) ---
+                ac = getattr(self.model.model, "acoustic_connector", None)
+                if ac is not None:
+                    ac_dir = os.path.join(lora_out, "acoustic_connector")
+                    os.makedirs(ac_dir, exist_ok=True)
+                    torch.save(ac.state_dict(), os.path.join(ac_dir, "pytorch_model.bin"))
+    
+                se = getattr(self.model.model, "semantic_connector", None)
+                if se is not None:
+                    se_dir = os.path.join(lora_out, "semantic_connector")
+                    os.makedirs(se_dir, exist_ok=True)
+                    torch.save(se.state_dict(), os.path.join(se_dir, "pytorch_model.bin"))
+    
+            except Exception as e:
+                logger.warning(f"Failed to save LoRA assets: {e}")
+
+
+    # ------------- Build the Trainer -------------
+
+    # Resolve which adapters to apply in samples
+
+    ema_cb = EmaCallback(attr_path="model.prediction_head", decay=0.999, device="cuda")
+
+    # --- CRITICAL FIX: CAST TRAINABLE PARAMS TO FP32 ---
+    # This prevents 'ValueError: Attempting to unscale FP16 gradients'
+    if getattr(training_args, 'fp16', False) or getattr(training_args, 'bf16', False):
+        print('>>> INFO: Enforcing float32 for trainable parameters (LoRA/Head) to fix GradScaler.')
+        for name, param in model.named_parameters():
+            if param.requires_grad:
+                param.data = param.data.to(torch.float32)
+    # ---------------------------------------------------
+
+    trainer = VibeVoiceTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        data_collator=data_collator,
+        callbacks=[ema_cb, LoRADebugCallback(log_every_n_steps=(int(getattr(training_args, "logging_steps", 50) or 50)))],
+    )
+
+    # Optional debug pre-training save
+    if getattr(training_args, "debug_save", False):
+        try:
+            debug_dir = os.path.join(training_args.output_dir, "debug_initial")
+            lora_out = os.path.join(debug_dir, "lora")
+            os.makedirs(lora_out, exist_ok=True)
+            logger.info(f"[debug_save] Saving initial (pre-training) model components to: {debug_dir}")
+            # language model adapters / base
+            try:
+                if hasattr(model.model.language_model, "save_pretrained"):
+                    model.model.language_model.save_pretrained(lora_out)
+            except Exception as e_lm:
+                logger.warning(f"[debug_save] Failed to save language_model: {e_lm}")
+            # diffusion head
+            try:
+                if hasattr(model.model, "prediction_head") and hasattr(model.model.prediction_head, "save_pretrained"):
+                    model.model.prediction_head.save_pretrained(os.path.join(lora_out, "diffusion_head"))
+            except Exception as e_head:
+                logger.warning(f"[debug_save] Failed to save prediction_head: {e_head}")
+            # NEW: full diffusion head state_dict as fallback
+            try:
+                ph = getattr(model.model, "prediction_head", None)
+                if ph is not None and hasattr(ph, "state_dict"):
+                    sd = ph.state_dict()
+                    torch.save(sd, os.path.join(lora_out, "diffusion_head_full.bin"))
+                    os.makedirs(os.path.join(lora_out, "diffusion_head"), exist_ok=True)
+                    torch.save(sd, os.path.join(lora_out, "diffusion_head", "diffusion_head_full.bin"))
+            except Exception as e:
+                logger.warning(f"[debug_save] Failed to save FULL diffusion head: {e}")
+            # connectors
+            try:
+                ac_conn = getattr(model.model, "acoustic_connector", None)
+                if ac_conn is not None:
+                    ac_dir = os.path.join(lora_out, "acoustic_connector")
+                    os.makedirs(ac_dir, exist_ok=True)
+                    torch.save(ac_conn.state_dict(), os.path.join(ac_dir, "pytorch_model.bin"))
+            except Exception as e_ac:
+                logger.warning(f"[debug_save] Failed to save acoustic_connector: {e_ac}")
+            try:
+                se_conn = getattr(model.model, "semantic_connector", None)
+                if se_conn is not None:
+                    se_dir = os.path.join(lora_out, "semantic_connector")
+                    os.makedirs(se_dir, exist_ok=True)
+                    torch.save(se_conn.state_dict(), os.path.join(se_dir, "pytorch_model.bin"))
+            except Exception as e_se:
+                logger.warning(f"[debug_save] Failed to save semantic_connector: {e_se}")
+        except Exception as e:
+            logger.warning(f"[debug_save] Unexpected failure saving initial components: {e}")
+
+    if getattr(training_args, "gradient_checkpointing", False):
+        try:
+            model.gradient_checkpointing_enable()
+        except Exception:
+            logger.warning("Failed to enable gradient checkpointing on the model.")
+
+    # =========================================================================
+    # Load Custom Weights from Checkpoint before resuming training
+    # =========================================================================
+    if training_args.do_train and training_args.resume_from_checkpoint:
+        checkpoint_path = None
+        if isinstance(training_args.resume_from_checkpoint, bool) and training_args.resume_from_checkpoint:
+            from transformers.trainer_utils import get_last_checkpoint
+            checkpoint_path = get_last_checkpoint(training_args.output_dir)
+        else:
+            checkpoint_path = training_args.resume_from_checkpoint
+
+        if checkpoint_path is not None and os.path.exists(checkpoint_path):
+            lora_dir = os.path.join(checkpoint_path, "lora")
+            if os.path.exists(lora_dir):
+                logger.info(f"*** Resuming custom weights (LoRA, Connectors, Head) from {lora_dir} ***")
+                
+                # 1. Load LLM LoRA
+                if hasattr(model.model, "language_model"):
+                    try:
+                        from peft import load_peft_weights, set_peft_model_state_dict
+                        adapters_weights = load_peft_weights(lora_dir)
+                        set_peft_model_state_dict(model.model.language_model, adapters_weights)
+                        logger.info("Successfully loaded LLM LoRA weights.")
+                    except Exception as e:
+                        logger.warning(f"Could not load LLM LoRA weights: {e}")
+
+                # 2. Load Diffusion Head
+                ph_full_path = os.path.join(lora_dir, "diffusion_head_full.bin")
+                if os.path.exists(ph_full_path) and hasattr(model.model, "prediction_head"):
+                    try:
+                        model.model.prediction_head.load_state_dict(torch.load(ph_full_path, map_location="cpu"), strict=False)
+                        logger.info("Successfully loaded Diffusion Head weights.")
+                    except Exception as e:
+                        logger.warning(f"Failed to load Diffusion Head weights: {e}")
+
+                # 3. Load Acoustic Connector
+                ac_path = os.path.join(lora_dir, "acoustic_connector", "pytorch_model.bin")
+                if os.path.exists(ac_path) and hasattr(model.model, "acoustic_connector"):
+                    try:
+                        model.model.acoustic_connector.load_state_dict(torch.load(ac_path, map_location="cpu"))
+                        logger.info("Successfully loaded Acoustic Connector weights.")
+                    except Exception as e:
+                        logger.warning(f"Failed to load Acoustic Connector weights: {e}")
+
+                # 4. Load Semantic Connector
+                se_path = os.path.join(lora_dir, "semantic_connector", "pytorch_model.bin")
+                if os.path.exists(se_path) and hasattr(model.model, "semantic_connector"):
+                    try:
+                        model.model.semantic_connector.load_state_dict(torch.load(se_path, map_location="cpu"))
+                        logger.info("Successfully loaded Semantic Connector weights.")
+                    except Exception as e:
+                        logger.warning(f"Failed to load Semantic Connector weights: {e}")
+            else:
+                logger.warning(f"No custom 'lora' directory found inside checkpoint: {checkpoint_path}")
+    # =========================================================================
+
+    if training_args.do_train:
+        # ----- THE FIX: SET resume_from_checkpoint=False HERE -----
+        # The weights are ALREADY loaded via the custom block above.
+        # Setting this to False forces Trainer to start counting steps/epochs from 0
+        # for your new dataset, preventing it from immediately exiting.
+        trainer.train(resume_from_checkpoint=False)
+    
+        lora_out = os.path.join(training_args.output_dir, "lora")
+        os.makedirs(lora_out, exist_ok=True)
+    
+        # LLM PEFT (if any)
+        lm = getattr(model.model, "language_model", None)
+        if hasattr(lm, "save_pretrained"):
+            lm.save_pretrained(lora_out)
+    
+        # Diffusion head PEFT (if any)
+        ph = getattr(model.model, "prediction_head", None)
+        if hasattr(ph, "save_pretrained"):
+            ph_dir = os.path.join(lora_out, "diffusion_head")
+            os.makedirs(ph_dir, exist_ok=True)
+            ph.save_pretrained(ph_dir)
+    
+        # ALWAYS: full diffusion head state_dict fallback
+        try:
+            if ph is not None and hasattr(ph, "state_dict"):
+                sd = ph.state_dict()
+                torch.save(sd, os.path.join(lora_out, "diffusion_head_full.bin"))
+                ph_dir = os.path.join(lora_out, "diffusion_head")
+                os.makedirs(ph_dir, exist_ok=True)
+                torch.save(sd, os.path.join(ph_dir, "diffusion_head_full.bin"))
+        except Exception as e:
+            logger.warning(f"Failed to save FULL diffusion head at end: {e}")
+    
+        # Connectors (if trained)
+        try:
+            ac = getattr(model.model, "acoustic_connector", None)
+            if ac is not None:
+                ac_dir = os.path.join(lora_out, "acoustic_connector")
+                os.makedirs(ac_dir, exist_ok=True)
+                torch.save(ac.state_dict(), os.path.join(ac_dir, "pytorch_model.bin"))
+        except Exception as e:
+            logger.warning(f"Failed to save acoustic_connector: {e}")
+    
+        try:
+            se = getattr(model.model, "semantic_connector", None)
+            if se is not None:
+                se_dir = os.path.join(lora_out, "semantic_connector")
+                os.makedirs(se_dir, exist_ok=True)
+                torch.save(se.state_dict(), os.path.join(se_dir, "pytorch_model.bin"))
+        except Exception as e:
+            logger.warning(f"Failed to save semantic_connector: {e}")
+
+    if training_args.do_eval and eval_dataset is not None:
+        trainer.evaluate()
+
+
+if __name__ == "__main__":
+    main()