upload_fixed_model.py

"""
Upload the fixed model.py to HuggingFace
Run this script to update your model on HuggingFace
"""

from huggingface_hub import HfApi
import os

# Fixed model.py content with lazy loading
MODEL_PY_CONTENT = '''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

    def __init__(self, config):
        super().__init__(config)
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.device = device
        
        # CRITICAL FIX: Don't load vocoder/model in __init__
        # Use lazy loading instead to avoid meta tensor issues
        self._vocoder = None
        self._ema_model = None
        
        # Store vocab path for lazy loading
        try:
            self._vocab_path = hf_hub_download(config.name_or_path, filename="checkpoints/vocab.txt")
        except:
            self._vocab_path = "checkpoints/vocab.txt"

    @property
    def vocoder(self):
        """Lazy load vocoder only when needed (avoids meta tensor issues)"""
        if self._vocoder is None:
            print("⚙️  Loading vocoder on-demand...")
            # Force regular device context (not meta)
            with torch.device('cpu'):
                self._vocoder = load_vocoder(vocoder_name="vocos", is_local=False, device='cpu')
            
            # Move to target device if not CPU
            if self.device.type != 'cpu':
                self._vocoder = self._vocoder.to(self.device)
            
            self._vocoder = self._vocoder.eval()
            print(f"✅ Vocoder loaded on {self.device}")
        
        return self._vocoder

    @property
    def ema_model(self):
        """Lazy load ema_model only when needed"""
        if self._ema_model is None:
            print("⚙️  Loading EMA model on-demand...")
            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=self._vocab_path,
                device=self.device
            )
            self._ema_model = self._ema_model.eval()
            print(f"✅ EMA model loaded on {self.device}")
        
        return self._ema_model

    def forward(self, text: str, ref_audio_path: str, ref_text: str, speed: float = None):
        """
        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.
            speed (float): Override speed (optional)

        Returns:
            np.array: Generated waveform.
        """

        if not os.path.exists(ref_audio_path):
            raise FileNotFoundError(f"Reference audio file {ref_audio_path} not found.")

        # Use config speed if not provided
        if speed is None:
            speed = self.config.speed

        # Load reference audio & text
        ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_path, ref_text)

        # Access properties to trigger lazy loading
        ema_model = self.ema_model
        vocoder = self.vocoder
        
        # Ensure on correct device
        ema_model.to(self.device)
        vocoder.to(self.device)
        
        # Perform inference
        audio, final_sample_rate, _ = infer_process(
            ref_audio,
            ref_text,
            text,
            ema_model,
            vocoder,
            mel_spec_type="vocos",
            speed=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())
'''

def upload_fixed_model():
    """Upload the fixed model.py to HuggingFace"""
    
    repo_id = "svp19/INF5"  # Your repo
    
    # Save the fixed model.py locally
    with open("model.py", "w", encoding="utf-8") as f:
        f.write(MODEL_PY_CONTENT)
    
    print(f"📝 Saved fixed model.py locally")
    
    # Upload to HuggingFace
    api = HfApi()
    
    try:
        api.upload_file(
            path_or_fileobj="model.py",
            path_in_repo="model.py",
            repo_id=repo_id,
            repo_type="model",
            commit_message="Fix: Use lazy loading for vocoder to avoid meta tensor issues"
        )
        print(f"✅ Successfully uploaded fixed model.py to {repo_id}")
        print(f"🔗 https://huggingface.co/{repo_id}/blob/main/model.py")
        
    except Exception as e:
        print(f"❌ Upload failed: {e}")
        raise
    
    # Clean up
    os.remove("model.py")
    print("🧹 Cleaned up local file")

if __name__ == "__main__":
    print("="*60)
    print("🚀 Uploading Fixed model.py to HuggingFace")
    print("="*60)
    upload_fixed_model()
    print("\n✨ Done! Now redeploy your Cerebrium app")
    print("   Run: cerebrium deploy --no-cache")