{
 "cells": [
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   "id": "7ea23f53-dd65-4af8-9b7c-87992827b0d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (25.0)\n",
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      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=10.11.0->typer>=0.23.1->typer-slim->transformers) (2.19.2)\n",
      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer>=0.23.1->typer-slim->transformers) (0.1.2)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86deb0f6-2901-43ba-9fcf-422925d50622",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d74128c4-2857-4464-b31a-6d645d6852ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e7146aa021346958ffdfe4a3663d4da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e81dde0420a419883eb436e20f380ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching 141 files:   0%|          | 0/141 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloaded to: /workspace/.cache/huggingface/hub/models--Maisha0055--menstrual_bangla_tts/snapshots/2de454379d284de3669897330bfab8f4c725674b\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "29f15193-18d3-4919-86b7-2ab8d3e17080",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "voxcpm_model_path: /workspace/.cache/huggingface/hub/models--Maisha0055--menstrual_bangla_tts/snapshots/2de454379d284de3669897330bfab8f4c725674b, zipenhancer_model_path: None, enable_denoiser: False\n",
      "/usr/local/lib/python3.12/dist-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
      "  WeightNorm.apply(module, name, dim)\n",
      "Running on device: cuda, dtype: bfloat16\n",
      "Loading model from pytorch_model.bin: /workspace/.cache/huggingface/hub/models--Maisha0055--menstrual_bangla_tts/snapshots/2de454379d284de3669897330bfab8f4c725674b/pytorch_model.bin\n",
      "Loading LoRA weights from: /workspace/.cache/huggingface/hub/models--Maisha0055--menstrual_bangla_tts/snapshots/2de454379d284de3669897330bfab8f4c725674b/lora\n",
      "Loaded 272 LoRA parameters, skipped 0\n",
      "Warm up VoxCPMModel...\n",
      "100%|██████████| 10/10 [00:01<00:00,  6.27it/s]\n",
      "  9%|▉         | 25/280 [00:01<00:12, 20.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation successful.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "!pip install torch soundfile transformers huggingface_hub\n",
    "from huggingface_hub import snapshot_download\n",
    "from pathlib import Path\n",
    "import importlib.util\n",
    "import soundfile as sf\n",
    "\n",
    "\n",
    "from huggingface_hub import login\n",
    "login(\"TOKEN\")\n",
    "repo_id = \"Maisha0055/menstrual_bangla_tts\"\n",
    "local_dir = snapshot_download(repo_id=repo_id)\n",
    "print(\"Downloaded to:\", local_dir)\n",
    "\n",
    "ROOT = Path(local_dir)\n",
    "\n",
    "sys.path.insert(0, str(ROOT))\n",
    "sys.path.insert(0, str(ROOT / \"VoxCPM\"))\n",
    "\n",
    "from load_model import load_menstrual_bangla_tts\n",
    "\n",
    "model = load_menstrual_bangla_tts(str(ROOT))\n",
    "\n",
    "audio = model.generate(\n",
    "    text=\"এটি একটি পরীক্ষা\",\n",
    "    cfg_value=2.0,\n",
    "    inference_timesteps=10,\n",
    ")\n",
    "\n",
    "sf.write(\"output.wav\", audio, model.tts_model.sample_rate)\n",
    "\n",
    "print(\"Generation successful.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0698283-2315-4dee-b54b-3ba0d80adcae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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