app.py

from transformers import pipeline
asr_pipe = pipeline("automatic-speech-recognition", model="ihanif/whisper-medium-urdu")
from difflib import SequenceMatcher

# List of commands
commands = [
    "کمپیوٹر، کھیل کھیلو",
    "میوزک چلاؤ موسیقی",
    "روشنی کم کریں",
    "آج کی تاریخ کیا ہے؟",
    "مجھے ایک لطیفہ سنائیں۔",
    
]
replies = [
    "کیا آپ کھیل دیکھنا چاہتے ہیں؟",
    "کیا آپ موسیقی سننا چاہتے ہیں؟",
    "کیا آپ روشنی کم کرنا چاہتے ہیں؟",
    "کیا آپ تاریخ جاننا چاہتے ہیں؟",
    "کیا آپ لطیفہ سننا چاہتے ہیں؟"
    
]
# Function to find the most similar command
def find_most_similar_command(statement, command_list):
    best_match = None
    highest_similarity = 0
    i=0
    for command in command_list:
        similarity = SequenceMatcher(None, statement, command).ratio()
        print(similarity)
        if similarity > highest_similarity:
            highest_similarity = similarity
            best_match = command
            reply=replies[i]
            print(reply)
            i+=1

    return best_match,reply
def transcribe_the_command(audio):
      import soundfile as sf
      sample_rate, audio_data = audio
      file_name = "recorded_audio.wav"
      sf.write(file_name, audio_data, sample_rate)
    # Convert stereo to mono by averaging the two channels
      print(file_name)

      transcript = asr_pipe(file_name)["text"]
      most_similar_command,reply = find_most_similar_command(transcript, commands)
      print(f"Given Statement: {transcript}")
      print(f"Most Similar Command: {most_similar_command}\n")
      print(reply)
    
      return reply
# get_text_from_voice("urdu.wav")
import gradio as gr


iface = gr.Interface(
    fn=transcribe_the_command,
    inputs=gr.inputs.Audio(label="Recorded Audio",source="microphone"),
    outputs="text",
    title="Whisper Small Hindi",
    description="Realtime demo for Hindi speech recognition using a fine-tuned Whisper small model.",
)

iface.launch()