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import os, subprocess, sys
os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1"
os.environ["HF_HUB_DISABLE_HF_TRANSFER"] = "1"
os.environ["HF_HUB_ENABLE_XET"] = "0"
os.environ["NUMBA_CACHE_DIR"] = "/tmp/numba_cache"
os.makedirs("/tmp/numba_cache", exist_ok=True)
os.environ["NUMBA_DISABLE_JIT"] = "1"
from huggingface_hub import HfApi, HfFolder, upload_folder, snapshot_download
# 🔒 Eliminar hf_transfer si está presente
subprocess.run([sys.executable, "-m", "pip", "uninstall", "-y", "hf_transfer"])
# === Configuración ===
HF_MODEL_ID = "tu_usuario/xtts-v2-finetuned" # <--- cambia con tu repo en HF
HF_TOKEN = os.environ.get("HF_TOKEN") # Debe estar definido en tu Space/entorno
DATASET_PATH = "/home/user/app/dataset" # Ruta a tu dataset
OUTPUT_PATH = "/tmp/output_model"
BASE_MODEL = "coqui/XTTS-v2"
os.makedirs("/tmp/xtts_cache", exist_ok=True)
os.chmod("/tmp/xtts_cache", 0o777)
os.makedirs("/tmp/xtts_model", exist_ok=True)
os.chmod("/tmp/xtts_model", 0o777)
os.makedirs("/tmp/xtts_model/.huggingface", exist_ok=True)
os.chmod("/tmp/xtts_model/.huggingface", 0o777)
# Continúa con tu lógica, usando las nuevas rutas de manera consistent
# 🔧 Forzar descarga sin symlinks ni hf_transfer
model_dir = snapshot_download(
repo_id="coqui/XTTS-v2",
local_dir="/tmp/xtts_model", # descarga directa aquí
cache_dir="/tmp/hf_cache", # cache seguro en /tmp
#local_dir_use_symlinks=False, # 🔑 evita enlaces simbólicos
resume_download=True,
token=HF_TOKEN
)
print(f"✅ Modelo descargado en: {model_dir}")
CONFIG_PATH = "/tmp/xtts_model/config.json"
RESTORE_PATH = "/tmp/xtts_model/model.pth"
# === 2. Editar configuración para tu dataset VoxPopuli ===
print("=== Editando configuración para fine-tuning con VoxPopuli ===")
import json
with open(CONFIG_PATH, "r") as f:
config = json.load(f)
config["output_path"] = OUTPUT_PATH
config["datasets"] = [
{
"formatter": "voxpopuli",
"path": DATASET_PATH,
"meta_file_train": "metadata.json"
}
]
config["run_name"] = "xtts-finetune-voxpopuli"
config["lr"] = 1e-5 # más bajo para fine-tuning
with open(CONFIG_PATH, "w") as f:
json.dump(config, f, indent=2)
# === 3. Lanzar entrenamiento ===
print("=== Iniciando fine-tuning de XTTS-v2 ===")
import librosa
from librosa.core.spectrum import magphase
# Parchear dinámicamente
librosa.magphase = magphase
# subprocess.run([
# "python", "/home/user/TTS/TTS/bin/train_tts.py",
# "--config_path", CONFIG_PATH,
# "--restore_path", RESTORE_PATH
# ], check=True)
subprocess.run([
"python", "-m", "TTS.bin.train",
"--config_path", CONFIG_PATH,
"--restore_path", RESTORE_PATH
], check=True)
# === 4. Subir modelo resultante a HF ===
print("=== Subiendo modelo fine-tuneado a Hugging Face Hub ===")
api = HfApi()
HfFolder.save_token(HF_TOKEN)
upload_folder(
repo_id=HF_MODEL_ID,
repo_type="model",
folder_path=OUTPUT_PATH,
token=HF_TOKEN
)
print("✅ Fine-tuning completado y modelo subido a Hugging Face.")
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