app.py

import os
os.system("""pip install nemo_toolkit['all']""")

import nemo.collections.asr as nemo_asr
from transformers import pipeline
import numpy as np
import gradio as gr
import librosa
from scipy.io.wavfile import write

def respond(message, chat_history):
  bot_message = message
  chat_history.append((message, bot_message))
  return "", chat_history

def transcribe(audio):
  sr, y = audio
  audio_name = "resampled_audio.wav"
  resampled_audio = librosa.resample(y=y.astype("float"), orig_sr=sr, target_sr=16000)
  write(audio_name, 16000, resampled_audio)
  result = asr_model.transcribe([f"./{audio_name}"])
  return result[0]

asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="nvidia/parakeet-ctc-0.6b")

with gr.Blocks() as demo:
  with gr.Column():
    gr.Markdown(
      """
      # HKU Canteen VA
      """)
    va = gr.Chatbot(container=False)

    with gr.Row(): # text input
      text_input = gr.Textbox(placeholder="Ask me anything...", container=False, scale=1)
      submit_btn = gr.Button("Submit", scale=0)

    with gr.Row():  # audio input
      recording = gr.Microphone(show_download_button=False, container=False)

    with gr.Row(): # button toolbar
      clear = gr.ClearButton([text_input, va])

  text_input.submit(respond, [text_input, va], [text_input, va], queue=False)
  submit_btn.click(respond, [text_input, va], [text_input, va], queue=False)
  # recording.stop_recording(transcribe, [recording], [text_input]).then(respond,s [text_input, va], [text_input, va], queue=False)
  recording.stop_recording(transcribe, [recording], [text_input])

if __name__ == "__main__":
    demo.launch()