Create app.py

app.py

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+!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()