diff --git a/README.md b/README.md
index 5e78082..d71c254 100644
--- a/README.md
+++ b/README.md
@@ -83,6 +83,10 @@ conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=
 
 # to run ipynb
 conda install -n voicecraft ipykernel --no-deps --force-reinstall
+
+# below is only needed if you want to run gradio_app.py
+sudo apt-get install espeak # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
+sudo apt-get install libespeak-dev # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
 ```
 
 If you have encountered version issues when running things, checkout [environment.yml](./environment.yml) for exact matching.
diff --git a/gradio_app.py b/gradio_app.py
index 4321a11..08bca21 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,3 +1,7 @@
+import os
+# this is only needed locally
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 import gradio as gr
 import torch
 import torchaudio
@@ -6,7 +10,6 @@ from data.tokenizer import (
     TextTokenizer,
 )
 from models import voicecraft
-import os
 import io
 import numpy as np
 import random
@@ -64,7 +67,7 @@ class WhisperModel:
 class WhisperxModel:
     def __init__(self, model_name, align_model: WhisperxAlignModel):
         from whisperx import load_model
-        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True})
+        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True, "max_new_tokens": None, "clip_timestamps": None, "hallucination_silence_threshold": None})
         self.align_model = align_model
 
     def transcribe(self, audio_path):
@@ -75,9 +78,6 @@ class WhisperxModel:
 def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
     global transcribe_model, align_model, voicecraft_model
 
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
     if alignment_model_name is not None:
         align_model = WhisperxAlignModel()
 
@@ -443,7 +443,7 @@ with gr.Blocks() as app:
 
     with gr.Row():
         with gr.Column(scale=2):
-            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath")
+            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
             with gr.Group():
                 original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
                                                  info="Use whisper model to get the transcript. Fix and align it if necessary.")
@@ -496,22 +496,22 @@ with gr.Blocks() as app:
                 rerun_btn = gr.Button(value="Rerun")
 
     with gr.Row():
-        with gr.Accordion("VoiceCraft config", open=False):
-            seed = gr.Number(label="seed", value=-1, precision=0)
-            left_margin = gr.Number(label="left_margin", value=0.08)
-            right_margin = gr.Number(label="right_margin", value=0.08)
-            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
-            codec_sr = gr.Number(label="codec_sr", value=50)
-            top_k = gr.Number(label="top_k", value=0)
-            top_p = gr.Number(label="top_p", value=0.8)
-            temperature = gr.Number(label="temperature", value=1)
-            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3], value=3,
-                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1, -1 = disabled")
-            sample_batch_size = gr.Number(label="sample_batch_size", value=4, precision=0,
-                                          info="generate this many samples and choose the shortest one")
+        with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
+            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
+                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
+            sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
+                                          info="The higher the number, the faster the output will be. Under the hood, the model will generate this many samples and choose the shortest one")
+            seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
             kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
                                 info="set to 0 to use less VRAM, but with slower inference")
-            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
+            left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
+            right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
+            top_p = gr.Number(label="top_p", value=0.8, info="0.8 is a good value, 0.9 is also good")
+            temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
+            top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
+            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
+            codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
+            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
 
     
     audio_tensors = gr.State()