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"""
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") |