diff --git a/.gitignore b/.gitignore
index 17dbc9b..b88bfef 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,7 @@ thumbs.db
 *.mp3
 *.pth
 *.th
+*.json
 
 *durip*
 *rtx*
diff --git a/inference_speech_editing.ipynb b/inference_speech_editing.ipynb
index 022e3ba..a0b5cd5 100644
--- a/inference_speech_editing.ipynb
+++ b/inference_speech_editing.ipynb
@@ -32,6 +32,7 @@
     "import torchaudio\n",
     "import numpy as np\n",
     "import random\n",
+    "from argparse import Namespace\n",
     "\n",
     "from data.tokenizer import (\n",
     "    AudioTokenizer,\n",
@@ -84,6 +85,34 @@
     "    torch.backends.cudnn.deterministic = True\n",
     "seed_everything(seed)\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "# load model, tokenizer, and other necessary files\n",
+    "voicecraft_name=\"giga330M.pth\" # or gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth\n",
+    "\n",
+    "# the new way of loading the model, with huggingface, recommended\n",
+    "from models import voicecraft\n",
+    "model = voicecraft.VoiceCraft.from_pretrained(f\"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}\")\n",
+    "phn2num = model.args.phn2num\n",
+    "config = vars(model.args)\n",
+    "model.to(device)\n",
+    "\n",
+    "# # the old way of loading the model\n",
+    "# from models import voicecraft\n",
+    "# filepath = hf_hub_download(repo_id=\"pyp1/VoiceCraft\", filename=voicecraft_name, repo_type=\"model\")\n",
+    "# ckpt = torch.load(filepath, map_location=\"cpu\")\n",
+    "# model = voicecraft.VoiceCraft(ckpt[\"config\"])\n",
+    "# model.load_state_dict(ckpt[\"model\"])\n",
+    "# config = vars(model.args)\n",
+    "# phn2num = ckpt[\"phn2num\"]\n",
+    "# model.to(device)\n",
+    "# model.eval()\n",
+    "\n",
+    "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n",
+    "if not os.path.exists(encodec_fn):\n",
+    "    os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th\")\n",
+    "    os.system(f\"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th\")\n",
+    "audio_tokenizer = AudioTokenizer(signature=encodec_fn) # will also put the neural codec model on gpu\n",
+    "\n",
+    "text_tokenizer = TextTokenizer(backend=\"espeak\")\n",
     "\n",
     "# point to the original file or record the file\n",
     "# write down the transcript for the file, or run whisper to get the transcript (and you can modify it if it's not accurate), save it as a .txt file\n",
@@ -199,40 +228,13 @@
     "mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]\n",
     "mask_interval = torch.LongTensor(mask_interval) # [M,2], M==1 for now\n",
     "\n",
-    "# load model, tokenizer, and other necessary files\n",
-    "voicecraft_name=\"giga330M.pth\" # or gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth\n",
     "\n",
-    "# the new way of loading the model, with huggingface, recommended\n",
-    "from models import voicecraft\n",
-    "model = voicecraft.VoiceCraft.from_pretrained(f\"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}\")\n",
-    "phn2num = model.args.phn2num\n",
-    "config = vars(model.args)\n",
-    "model.to(device)\n",
-    "\n",
-    "# # the old way of loading the model\n",
-    "# from models import voicecraft\n",
-    "# filepath = hf_hub_download(repo_id=\"pyp1/VoiceCraft\", filename=voicecraft_name, repo_type=\"model\")\n",
-    "# ckpt = torch.load(filepath, map_location=\"cpu\")\n",
-    "# model = voicecraft.VoiceCraft(ckpt[\"config\"])\n",
-    "# model.load_state_dict(ckpt[\"model\"])\n",
-    "# config = vars(model.args)\n",
-    "# phn2num = ckpt[\"phn2num\"]\n",
-    "# model.to(device)\n",
-    "# model.eval()\n",
-    "\n",
-    "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n",
-    "if not os.path.exists(encodec_fn):\n",
-    "    os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th\")\n",
-    "    os.system(f\"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th\")\n",
-    "audio_tokenizer = AudioTokenizer(signature=encodec_fn) # will also put the neural codec model on gpu\n",
-    "\n",
-    "text_tokenizer = TextTokenizer(backend=\"espeak\")\n",
     "\n",
     "# run the model to get the output\n",
     "from inference_speech_editing_scale import inference_one_sample\n",
     "\n",
     "decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition, 'kvcache': kvcache, \"codec_audio_sr\": codec_audio_sr, \"codec_sr\": codec_sr, \"silence_tokens\": silence_tokens}\n",
-    "orig_audio, new_audio = inference_one_sample(model, ckpt[\"config\"], phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, mask_interval, device, decode_config)\n",
+    "orig_audio, new_audio = inference_one_sample(model, Namespace(**config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, mask_interval, device, decode_config)\n",
     "        \n",
     "# save segments for comparison\n",
     "orig_audio, new_audio = orig_audio[0].cpu(), new_audio[0].cpu()\n",