app.py

import nemo.collections.asr as nemo_asr
import gradio as gr
import pandas as pd 
from pydub import AudioSegment 

asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_rw_conformer_ctc_large")
df = pd.read_csv("amasaku_data.tsv",sep='\t')

amasaku_mapping = {str(key).lower():str(val).lower() for key,val in zip(df.iloc[:,0],df.iloc[:,1])}

def transcribe(file):

    #if not audio:
    #    return {state_var: state, transcription_var: state} 
    
    #print("filename: ",file)
    try:
        audio = AudioSegment.from_file(file).set_frame_rate(16000).set_channels(1)
        new_file_name = file.split(".")[0]+".wav"
        audio.export(new_file_name,format)
    except Exception as e:
        print(e)
    transcription= asr_model.transcribe([new_file_name])
    transcription = transcription[0].lower().split()
    transcribed_with_amasaku = []
    for word in transcription:
        transcribed_with_amasaku.append(amasaku_mapping.get(word,word))
    transcribed_with_amasaku = " ".join(transcribed_with_amasaku)
    return  transcribed_with_amasaku.capitalize()


with gr.Blocks() as demo:
   # state_var = gr.State("")

    with gr.Row():
        with gr.Column():
            uploaded_audio = gr.Audio(label="Upload Audio File", type="filepath")

        with gr.Column():
            transcription = gr.Textbox(type="text", label="Transcription")
    with gr.Row():
      transcribe_button = gr.Button("Transcribe")

    transcribe_button.click(
        transcribe,
        [uploaded_audio],
        transcription
    )

  
demo.launch()