Update app.py

app.py

@@ -1,6 +1,112 @@
 from flask import Flask, render_template, request, jsonify
 import os
+import torchafrom flask import Flask, render_template, request, jsonify
+import os
 import torch
+import re
+import ffmpeg  # Ensure FFmpeg is installed
+from transformers import pipeline
+from gtts import gTTS
+from pydub import AudioSegment
+from pydub.silence import detect_nonsilent
+from waitress import serve
+import whisper  # Improved Whisper ASR Model
+
+app = Flask(__name__)
+
+# Load Whisper Model for Highly Accurate Speech-to-Text
+device = "cuda" if torch.cuda.is_available() else "cpu"
+asr_model = whisper.load_model("large-v3", device=device)
+
+# Function to generate audio prompts
+def generate_audio_prompt(text, filename):
+    tts = gTTS(text=text, lang="en")
+    tts.save(os.path.join("static", filename))
+
+# Generate required voice prompts
+prompts = {
+    "welcome": "Welcome to Biryani Hub.",
+    "ask_name": "Tell me your name.",
+    "ask_email": "Please provide your email address.",
+    "thank_you": "Thank you for registration."
+}
+
+for key, text in prompts.items():
+    generate_audio_prompt(text, f"{key}.mp3")
+
+# Symbol mapping for better recognition
+SYMBOL_MAPPING = {
+    "at the rate": "@",
+    "at": "@",
+    "dot": ".",
+    "underscore": "_",
+    "hash": "#",
+    "plus": "+",
+    "dash": "-",
+    "comma": ",",
+    "space": " "
+}
+
+# Function to convert audio to WAV format (Fixes FFmpeg issues)
+def convert_to_wav(input_path, output_path):
+    try:
+        audio = AudioSegment.from_file(input_path)
+        audio.export(output_path, format="wav")
+    except Exception as e:
+        raise Exception(f"Audio conversion failed: {str(e)}")
+
+# Function to clean transcribed text (Removes unnecessary words)
+def clean_transcription(text):
+    text = text.lower().strip()
+    ignore_phrases = ["my name is", "this is", "i am", "it's", "name"]
+    for phrase in ignore_phrases:
+        text = text.replace(phrase, "").strip()
+    
+    for word, symbol in SYMBOL_MAPPING.items():
+        text = text.replace(word, symbol)
+    
+    return text.capitalize()
+
+# Function to check if the audio contains actual speech
+def is_silent_audio(audio_path):
+    audio = AudioSegment.from_wav(audio_path)
+    nonsilent_parts = detect_nonsilent(audio, min_silence_len=500, silence_thresh=audio.dBFS-16)
+    return len(nonsilent_parts) == 0  # Returns True if silence detected
+
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+@app.route("/transcribe", methods=["POST"])
+def transcribe():
+    if "audio" not in request.files:
+        return jsonify({"error": "No audio file provided"}), 400
+
+    audio_file = request.files["audio"]
+    input_audio_path = os.path.join("static", "temp_input")
+    output_audio_path = os.path.join("static", "temp.wav")
+    audio_file.save(input_audio_path)
+
+    try:
+        # Convert audio to WAV format
+        convert_to_wav(input_audio_path, output_audio_path)
+
+        # Check if the audio contains valid speech
+        if is_silent_audio(output_audio_path):
+            return jsonify({"error": "No speech detected. Please try again."}), 400
+        
+        # Transcribe using Whisper
+        result = asr_model.transcribe(output_audio_path, language="en")
+        transcribed_text = clean_transcription(result["text"])
+        
+        return jsonify({"text": transcribed_text})
+    except Exception as e:
+        return jsonify({"error": f"Speech recognition error: {str(e)}"}), 500
+
+# Use Waitress for Production Server
+if __name__ == "__main__":
+    serve(app, host="0.0.0.0", port=7860)
+
 import re
 import ffmpeg  # Ensures FFmpeg is installed
 from transformers import pipeline