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Hunzla
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Duplicate from Hunzla/whisperaudio
Browse files- .gitattributes +35 -0
- README.md +14 -0
- app.py +106 -0
- requirements.txt +3 -0
- tasks.json +17 -0
.gitattributes
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README.md
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---
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title: Whisperaudio
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emoji: 🚀
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colorFrom: green
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colorTo: red
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sdk: gradio
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sdk_version: 3.39.0
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app_file: app.py
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pinned: false
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license: openrail
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duplicated_from: Hunzla/whisperaudio
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from transformers import pipeline
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asr_pipe = pipeline("automatic-speech-recognition", model="Abdullah17/whisper-small-urdu")
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from difflib import SequenceMatcher
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import json
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import socket
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def get_local_ip():
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try:
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# Create a socket connection to a remote host (here, google.com)
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s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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s.connect(("8.8.8.8", 80))
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local_ip = s.getsockname()[0]
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s.close()
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return local_ip
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except Exception as e:
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print(f"Error getting local IP: {e}")
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return None
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with open("tasks.json", "r",encoding="utf-8") as json_file:
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urdu_data = json.load(json_file)
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# List of commands
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# commands = [
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# "نمائندے ایجنٹ نمائندہ",
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# " سم ایکٹیویٹ ",
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# " سم بلاک بند ",
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# "موبائل پیکیجز انٹرنیٹ پیکیج",
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# " چالان جمع چلان",
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# " گانا "
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# ]
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# replies = [
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# 1,2,
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# ]
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# Function to find the most similar command
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def find_most_similar_command(statement, command_list):
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best_match = None
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highest_similarity = 0
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i=0
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for sub_list in command_list:
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for command in sub_list:
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similarity = SequenceMatcher(None, statement, command).ratio()
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print(i,"similarity",similarity)
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if similarity > highest_similarity:
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highest_similarity = similarity
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best_match = command
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reply=i
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i+=1
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return best_match,reply
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transcript_only=["1","3","4"]
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col_names={'1':"name",'3':"address",'4':"order"}
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def send_data_to_db(menu_id,col_value,order_id):
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import requests
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col_name=col_names[menu_id]
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# API endpoint URL
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url = 'https://pizzahut.softinfix.tech/api/save_order?'+col_name+'='+col_value+"&order_id"+"="+order_id
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payload = {}
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headers = {}
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response = requests.request("GET", url, headers=headers, data=payload)
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# Print response
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print(response.status_code)
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print(response.text)
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def transcribe_the_command(audio,menu_id,order_id):
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local_ip = get_local_ip()
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if local_ip:
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print(f"Local IP Address: {local_ip}")
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else:
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print("Local IP could not be determined.")
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import soundfile as sf
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sample_rate, audio_data = audio
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file_name = "recorded_audio.wav"
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sf.write(file_name, audio_data, sample_rate)
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# Convert stereo to mono by averaging the two channels
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print(menu_id)
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transcript = asr_pipe(file_name)["text"]
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if menu_id in transcript_only:
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col_value=transcript
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send_data_to_db(menu_id,col_value,order_id)
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print("data uploaded successfully!")
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else:
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commands=urdu_data[menu_id]
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print(commands)
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most_similar_command,reply = find_most_similar_command(transcript, commands)
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print(f"Given Statement: {transcript}")
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print(f"Most Similar Command: {most_similar_command}\n")
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print(reply)
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return reply
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# get_text_from_voice("urdu.wav")
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import gradio as gr
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iface = gr.Interface(
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fn=transcribe_the_command,
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inputs=[gr.inputs.Audio(label="Recorded Audio",source="microphone"),gr.inputs.Textbox(label="menu_id"),gr.inputs.Textbox(label="order_id")],
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outputs="text",
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title="Whisper Small Urdu Command",
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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.",
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)
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iface.launch()
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requirements.txt
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gradio
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transformers[torch]
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soundfile
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tasks.json
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{
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"0": [
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["کیا آپ میری مدد کر سکتے ہیں","مجھے نمائندے سے بات کرنی ہے","مجھے مدد کی ضرورت ہے","مجھے ایجنٹ سے بات کرنی ہے۔"],
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["آرڈر کریں ","آرڈر پلیس کر دیں","رکھیں آرڈر","میں نے آرڈر کرنا ہے۔","مجھے آرڈر کرنا ہے"],
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[ "آرڈر کا اسٹیٹس پتا کرنا ہے","آرڈر کی حالت پتا کرنی ہے","آرڈر کی کنڈیشن کیا ہے","اوڈر کی تفیلات جانتی ہے","میں نے آرڈر کیا تھا"]
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],
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"2": [
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[ "لاہور","لہور","لاهور", "لہوڑ", "لاھور"],
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[ "کراچی", "کرچی", "کراچ","کرچ","کرچے"],
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[ "اسلام آباد", "اسلم آباد","اسلماباد","اسلامباد","اسلمباد","اسلامآبد"]
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],
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"3": [
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"تیسرا بیان اندراج ۱",
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"تیسرا بیان اندراج ۲",
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"تیسرا بیان اندراج ۳"
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]
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}
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