Update app.py

app.py

@@ -1,55 +1,3 @@
-pip install -U git+https://github.com/PrithivirajDamodaran/Gramformer.git
-pip install gradio -q
-## Install dependencies
-pip install wget
-apt-get install sox libsndfile1 ffmpeg
-pip install text-unidecode
-pip install matplotlib>=3.3.2
-
-## Install NeMo
-BRANCH = 'r1.13.0'
-python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
-
-## Grab the config we'll use in this example
-mkdir configs
-wget -P configs/ https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/asr/conf/config.yaml
-
-python -m spacy download en_core_web_md
-python -m spacy link en_core_web_md en
-
 import gradio as gr
-import time
-from nemo.collections.asr.models import ASRModel
-import torch
-if torch.cuda.is_available():
-    device = torch.device(f'cuda:0')
-asr_model = ASRModel.from_pretrained(model_name='stt_en_citrinet_1024')
-
-from gramformer import Gramformer
-import torch
-
-def set_seed(seed):
-  torch.manual_seed(seed)
-  if torch.cuda.is_available():
-    torch.cuda.manual_seed_all(seed)
-
-set_seed(1212)
-
-gf = Gramformer(models = 1, use_gpu=False) # 1=corrector, 2=detector
-
-def transcribe(audio):
-  """Speech to text using Nvidia Nemo"""
-  text = asr_model.transcribe(paths2audio_files=[audio])[0]
-  correct = list(gf.correct(text, max_candidates = 1))[0]
-  return text, correct
 
-# we need input, output and interface components for gradio
-gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.components.Audio(type="filepath"),
-    ],
-    outputs=[
-        "textbox",
-        "textbox"
-    ]).launch()
+gr.Interface.load("models/nvidia/stt_en_citrinet_1024_gamma_0_25").launch()