model.py

import sys
import os

current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_dir)

from transformers import PreTrainedModel, PretrainedConfig, AutoConfig
import torch
import numpy as np
from f5_tts.infer.utils_infer import (
    infer_process,
    load_model,
    load_vocoder,
    preprocess_ref_audio_text,
)
from f5_tts.model import DiT
import soundfile as sf
import io
from pydub import AudioSegment, silence
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import os

class INF5Config(PretrainedConfig):
    model_type = "inf5"

    def __init__(self, ckpt_path: str = "checkpoints/model_best.pt", vocab_path: str = "checkpoints/vocab.txt",
                                                                  speed: float = 1.0, remove_sil: bool = True, **kwargs):
        super().__init__(**kwargs)
        self.ckpt_path = ckpt_path
        self.vocab_path = vocab_path
        self.speed = speed
        self.remove_sil = remove_sil

class INF5Model(PreTrainedModel):
    config_class = INF5Config
    _tied_weights_keys = []  # Fix for transformers 5.0.0 compatibility
    
    @property
    def all_tied_weights_keys(self):
        """Compatibility property for transformers 5.0.0"""
        return {}


    def __init__(self, config):
        super().__init__(config)
        # Determine target device for inference (GPU if available)
        self._target_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        # Disable torch.compile graph tracing to prevent ODE solver issues
        torch._dynamo.config.suppress_errors = True
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

        # Load vocoder - force on actual device to avoid meta tensor issues in transformers 5.0+
        with torch.device('cpu'):
            # Use eager backend to keep _orig_mod structure without actual compilation
            self.vocoder = torch.compile(load_vocoder(vocoder_name="vocos", is_local=False, device='cpu'), backend="eager")
                                     
        # Download and load model weights (load on CPU first for safe init,
        # model will be moved to target device in forward())
        safetensors_path = hf_hub_download(config.name_or_path, filename="model.safetensors")
        print(f"Loading model weights from {safetensors_path} (safetensors)...")
        state_dict = load_file(safetensors_path, device='cpu')

        # Download vocab.txt from HF Hub
        vocab_path = hf_hub_download(config.name_or_path, filename="checkpoints/vocab.txt")
                                                                     
        # Force model loading on CPU to avoid meta tensor issues
        with torch.device('cpu'):
            self.ema_model = load_model(
                    DiT,
                    dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4),
                                                                                     mel_spec_type="vocos",
                    vocab_file=vocab_path,
                    device='cpu'
                )

        # Load state dict into model BEFORE compiling
        # Separate ema_model and vocoder weights, strip _orig_mod. prefix
        ema_state_dict = {}
        vocoder_state_dict = {}
        
        for key, value in state_dict.items():
            # Process ema_model weights
            if key.startswith("ema_model._orig_mod."):
                new_key = key.replace("ema_model._orig_mod.", "")
                ema_state_dict[new_key] = value
            elif key.startswith("ema_model."):
                new_key = key.replace("ema_model.", "")
                ema_state_dict[new_key] = value
            # Process vocoder weights
            elif key.startswith("vocoder._orig_mod."):
                new_key = key.replace("vocoder._orig_mod.", "")
                vocoder_state_dict[new_key] = value
            elif key.startswith("vocoder."):
                new_key = key.replace("vocoder.", "")
                vocoder_state_dict[new_key] = value
        
        # Load ema_model weights
        missing_keys, unexpected_keys = self.ema_model.load_state_dict(
            ema_state_dict, strict=False)
        
        # Load vocoder weights if any (vocoder is already compiled, so use _orig_mod if needed)
        if vocoder_state_dict:
            try:
                # Try loading directly first
                self.vocoder.load_state_dict(vocoder_state_dict, strict=False)
            except:
                # If vocoder is compiled, access the underlying model
                if hasattr(self.vocoder, '_orig_mod'):
                    self.vocoder._orig_mod.load_state_dict(vocoder_state_dict, strict=False)
                                                                            
        # Use eager backend - disables actual compilation while keeping _orig_mod
        # structure for weight serialization. Full torch.compile with inductor
        # breaks the ODE solver in CFM.sample() causing jumbled/partial text output.
        self.ema_model = torch.compile(self.ema_model, backend="eager")
        print(f"Weight loading - Missing keys: {len(missing_keys)}, Unexpected keys: {len(unexpected_keys)}")
        if missing_keys:
            print(f"Missing keys sample: {missing_keys[:5]}")
        if unexpected_keys:
            print(f"Unexpected keys sample: {unexpected_keys[:5]}")

        # Flag for lazy buffer recomputation (see _recompute_buffers).
        # We cannot recompute here because transformers 5.0 materializes
        # meta tensors AFTER __init__ returns, overwriting our values.
        self._buffers_need_recompute = True

    def _recompute_buffers(self):
        """Recompute non-persistent buffers that were corrupted by
        transformers 5.0's meta device initialization.
        
        transformers 5.0 wraps __init__ in torch.device('meta') context,
        then materializes meta tensors with uninitialized (garbage) values.
        Non-persistent buffers (not in safetensors) never get correct values.
        This method must be called AFTER from_pretrained completes."""
        from f5_tts.model.modules import precompute_freqs_cis
        
        # Get the underlying model (unwrap torch.compile if needed)
        ema = self.ema_model._orig_mod if hasattr(self.ema_model, '_orig_mod') else self.ema_model
                                                                      
        # Determine current device of the buffers
        buf_device = ema.transformer.text_embed.freqs_cis.device if (
            hasattr(ema, 'transformer') and hasattr(ema.transformer, 'text_embed')
            and hasattr(ema.transformer.text_embed, 'freqs_cis')
        ) else torch.device('cpu')

        # Recompute text_embed.freqs_cis (positional embeddings for text)
        if hasattr(ema, 'transformer') and hasattr(ema.transformer, 'text_embed'):
            text_embed = ema.transformer.text_embed
            if hasattr(text_embed, 'extra_modeling') and text_embed.extra_modeling:
                text_dim = text_embed.text_embed.embedding_dim
                max_pos = text_embed.precompute_max_pos
                freqs_cis = precompute_freqs_cis(text_dim, max_pos).to(buf_device)
                # Check if recomputation needed (first value should be cos(0) = 1.0)
                if text_embed.freqs_cis.is_meta or abs(text_embed.freqs_cis[0, 0].item() - 1.0) > 0.01:
                    text_embed.freqs_cis.data.copy_(freqs_cis)
                    print(f"Recomputed freqs_cis: shape={freqs_cis.shape}, first_val={freqs_cis[0,0].item():.4f}")
                                                                    
        # Recompute mel_spec.dummy buffer
        if hasattr(ema, 'mel_spec') and hasattr(ema.mel_spec, 'dummy'):
            if ema.mel_spec.dummy.is_meta or ema.mel_spec.dummy.item() != 0:
                ema.mel_spec.dummy.data.fill_(0)
                print("Recomputed mel_spec.dummy to 0")
            
        # Recompute rotary_embed.inv_freq if needed
        if hasattr(ema, 'transformer') and hasattr(ema.transformer, 'rotary_embed'):
            rot = ema.transformer.rotary_embed
            if hasattr(rot, 'inv_freq'):
                dim = rot.inv_freq.shape[0] * 2
                if rot.inv_freq.is_meta or rot.inv_freq[0].abs() > 10:
                    theta = 10000.0
                    inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2).float().to(buf_device) / dim))
                    rot.inv_freq.data.copy_(inv_freq)
                    print(f"Recomputed rotary inv_freq: shape={inv_freq.shape}")
        
        self._buffers_need_recompute = False

    
    @property
    def device(self):
        """Get the target device of the model (GPU if available, else CPU)"""
        return getattr(self, '_target_device', torch.device('cpu'))
                                
    def forward(self, text: str, ref_audio_path: str, ref_text: str):
        """
        Generate speech given a reference audio & text input.
        
        Args:
            text (str): The text to be synthesized.
            ref_audio_path (str): Path to the reference audio file.
            ref_text (str): The reference text.
        Returns:
            np.array: Generated waveform.
        """

        # Lazy recomputation of non-persistent buffers corrupted by transformers 5.0
        if getattr(self, "_buffers_need_recompute", False):
            self._recompute_buffers()

        if not os.path.exists(ref_audio_path):
            raise FileNotFoundError(f"Reference audio file {ref_audio_path} not found.")
                                                                        
        # Load reference audio & text
        ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_path, ref_text)
        
        # Move models to target device (GPU if available) - only actually
        # transfers on first call; subsequent calls are no-ops
        self.ema_model.to(self.device)
        self.vocoder.to(self.device)
        
        # Perform inference
        audio, final_sample_rate, _ = infer_process(
            ref_audio,
            ref_text,
            text,
            self.ema_model,
            self.vocoder,
            mel_spec_type="vocos",
            speed=self.config.speed,
            device=self.device,
        )

        # Convert to pydub format and remove silence if needed
        buffer = io.BytesIO()
        sf.write(buffer, audio, samplerate=24000, format="WAV")
        buffer.seek(0)
        audio_segment = AudioSegment.from_file(buffer, format="wav")

        if self.config.remove_sil:
            non_silent_segs = silence.split_on_silence(
                audio_segment,
                min_silence_len=1000,
                silence_thresh=-50,
                keep_silence=500,
                seek_step=10,
            )
            non_silent_wave = sum(non_silent_segs, AudioSegment.silent(duration=0))
            audio_segment = non_silent_wave

        # Normalize loudness
        target_dBFS = -20.0
        change_in_dBFS = target_dBFS - audio_segment.dBFS
        audio_segment = audio_segment.apply_gain(change_in_dBFS)

        return np.array(audio_segment.get_array_of_samples())