whisperaudio / app.py
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from transformers import pipeline
asr_pipe = pipeline("automatic-speech-recognition", model="Abdullah17/whisper-small-urdu")
from difflib import SequenceMatcher
# List of commands
commands = [
"نمائندے ایجنٹ نمائندہ",
" سم ایکٹیویٹ ",
" سم بلاک بند ",
"موبائل پیکیجز انٹرنیٹ پیکیج",
" چالان جمع ",
" گانا سنانا"
]
# replies = [
# 1,2,
# ]
# 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=i
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 Urdu Command",
description="Realtime demo for Urdu speech recognition using a fine-tuned Whisper small model and outputting the estimated command on the basis of speech transcript.",
)
iface.launch()