Create app.py

app.py

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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.")