diff --git a/README.md b/README.md
index 6438eee..74713fa 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ To clone or edit an unseen voice, VoiceCraft needs only a few seconds of referen
 - [x] Model weights (both 330M and 830M, the former seems to be just as good)
 - [ ] Write colab notebooks for better hands-on experience
 - [ ] HuggingFace Spaces demo
-- [ ] Better guidance on training
+- [ ] Better guidance on training/finetuning
 
 ## How to run TTS inference 
 There are two ways: 
@@ -28,6 +28,8 @@ There are two ways:
 
 When you are inside the docker image or you have installed all dependencies, Checkout [`inference_tts.ipynb`](./inference_tts.ipynb).
 
+If you want to do model development such as training/finetuning, I recommend following [envrionment setup](#environment-setup) and [training](#training).
+
 ## QuickStart
 :star: To try out TTS inference with VoiceCraft, the best way is using docker. Thank [@ubergarm](https://github.com/ubergarm) and [@jayc88](https://github.com/jay-c88) for making this happen. 
 
@@ -66,13 +68,13 @@ echo GOOD LUCK
 conda create -n voicecraft python=3.9.16
 conda activate voicecraft
 
-pip install torch==2.0.1 # this assumes your system is compatible with CUDA 11.7, otherwise checkout https://pytorch.org/get-started/previous-versions/#v201
-apt-get install ffmpeg # if you don't already have ffmpeg installed
 pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft
+pip install xformers==0.0.22
+pip install torchaudio==2.0.2 torch==2.0.1 # this assumes your system is compatible with CUDA 11.7, otherwise checkout https://pytorch.org/get-started/previous-versions/#v201
+apt-get install ffmpeg # if you don't already have ffmpeg installed
 apt-get install espeak-ng # backend for the phonemizer installed below
 pip install tensorboard==2.16.2
 pip install phonemizer==3.2.1
-pip install torchaudio==2.0.2
 pip install datasets==2.16.0
 pip install torchmetrics==0.11.1
 # install MFA for getting forced-alignment, this could take a few minutes
@@ -80,7 +82,7 @@ conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=
 # conda install pocl # above gives an warning for installing pocl, not sure if really need this
 
 # to run ipynb
-conda install -n voicecraft ipykernel --update-deps --force-reinstall
+conda install -n voicecraft ipykernel --no-deps --force-reinstall
 ```
 
 If you have encountered version issues when running things, checkout [environment.yml](./environment.yml) for exact matching.
@@ -129,7 +131,7 @@ bash e830M.sh
 
 
 ## License
-The codebase is under CC BY-NC-SA 4.0 ([LICENSE-CODE](./LICENSE-CODE)), and the model weights are under Coqui Public Model License 1.0.0 ([LICENSE-MODEL](./LICENSE-MODEL)). Note that we use some of the code from other repository that are under different licenses: `./models/codebooks_patterns.py` is under MIT license; `./models/modules`, `./steps/optim.py`, `data/tokenizer.py` are under Apache License, Version 2.0; the phonemizer we used is under GNU 3.0 License. For drop-in replacement of the phonemizer (i.e. text to IPA phoneme mapping), try [g2p](https://github.com/roedoejet/g2p) (MIT License) or [OpenPhonemizer](https://github.com/NeuralVox/OpenPhonemizer) (BSD-3-Clause Clear), although these are not tested.
+The codebase is under CC BY-NC-SA 4.0 ([LICENSE-CODE](./LICENSE-CODE)), and the model weights are under Coqui Public Model License 1.0.0 ([LICENSE-MODEL](./LICENSE-MODEL)). Note that we use some of the code from other repository that are under different licenses: `./models/codebooks_patterns.py` is under MIT license; `./models/modules`, `./steps/optim.py`, `data/tokenizer.py` are under Apache License, Version 2.0; the phonemizer we used is under GNU 3.0 License.
 
 <!-- How to use g2p to convert english text into IPA phoneme sequence
 first install it with `pip install g2p`
diff --git a/inference_speech_editing.ipynb b/inference_speech_editing.ipynb
index 84ebc5b..95efa12 100644
--- a/inference_speech_editing.ipynb
+++ b/inference_speech_editing.ipynb
@@ -30,13 +30,15 @@
     "# import libs\n",
     "import torch\n",
     "import torchaudio\n",
+    "import numpy as np\n",
+    "import random\n",
     "\n",
     "from data.tokenizer import (\n",
     "    AudioTokenizer,\n",
     "    TextTokenizer,\n",
     ")\n",
     "\n",
-    "from models import voicecraft\n"
+    "from models import voicecraft"
    ]
   },
   {
@@ -72,6 +74,15 @@
     "silence_tokens = [1388,1898,131] # if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1\n",
     "stop_repetition = -1 # -1 means do not adjust prob of silence tokens. if there are long silence or unnaturally strecthed words, increase sample_batch_size to 2, 3 or even 4\n",
     "# what this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest\n",
+    "def seed_everything(seed):\n",
+    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
+    "    random.seed(seed)\n",
+    "    np.random.seed(seed)\n",
+    "    torch.manual_seed(seed)\n",
+    "    torch.cuda.manual_seed(seed)\n",
+    "    torch.backends.cudnn.benchmark = False\n",
+    "    torch.backends.cudnn.deterministic = True\n",
+    "seed_everything(seed)\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
     "\n",
     "# point to the original file or record the file\n",
diff --git a/inference_tts.ipynb b/inference_tts.ipynb
index 43deb0c..8da5402 100644
--- a/inference_tts.ipynb
+++ b/inference_tts.ipynb
@@ -122,11 +122,13 @@
     "\n",
     "import torch\n",
     "import torchaudio\n",
+    "import numpy as np\n",
+    "import random\n",
     "\n",
     "from data.tokenizer import (\n",
     "    AudioTokenizer,\n",
     "    TextTokenizer,\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -241,6 +243,16 @@
     "sample_batch_size = 4 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
     "seed = 1 # change seed if you are still unhappy with the result\n",
     "\n",
+    "def seed_everything(seed):\n",
+    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
+    "    random.seed(seed)\n",
+    "    np.random.seed(seed)\n",
+    "    torch.manual_seed(seed)\n",
+    "    torch.cuda.manual_seed(seed)\n",
+    "    torch.backends.cudnn.benchmark = False\n",
+    "    torch.backends.cudnn.deterministic = True\n",
+    "seed_everything(seed)\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, \"sample_batch_size\": sample_batch_size}\n",
     "from inference_tts_scale import inference_one_sample\n",
     "concated_audio, gen_audio = inference_one_sample(model, ckpt[\"config\"], phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)\n",
@@ -280,7 +292,7 @@
   "kernelspec": {
    "display_name": "voicecraft",
    "language": "python",
-   "name": "voicecraft"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -292,7 +304,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,