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import torch
from flask import Flask, render_template, request, jsonify
import os
import re
from transformers import pipeline
from gtts import gTTS
from pydub import AudioSegment
from pydub.silence import detect_nonsilent
from waitress import serve
app = Flask(__name__)
# ✅ Load Whisper ASR Model Correctly
device = "cuda" if torch.cuda.is_available() else "cpu"
asr_model = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3", device=0 if device == "cuda" else -1)
# Function to generate voice 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")
# ✅ Ensure Proper Audio Format
def convert_to_wav(input_path, output_path):
try:
audio = AudioSegment.from_file(input_path)
audio = audio.set_frame_rate(16000).set_channels(1) # ✅ Convert to 16kHz, mono
audio.export(output_path, format="wav")
except Exception as e:
raise Exception(f"Audio conversion failed: {str(e)}")
# ✅ Check for Silence
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
@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.wav")
output_audio_path = os.path.join("static", "temp.wav")
audio_file.save(input_audio_path)
try:
# ✅ Convert audio to proper format
convert_to_wav(input_audio_path, output_audio_path)
# ✅ Check for silent audio
if is_silent_audio(output_audio_path):
return jsonify({"error": "No speech detected. Please try again."}), 400
# ✅ Transcribe Using Whisper ASR
result = asr_model(output_audio_path, generate_kwargs={"language": "en"})
transcribed_text = result["text"].strip().capitalize()
return jsonify({"text": transcribed_text})
except Exception as e:
return jsonify({"error": f"Speech recognition error: {str(e)}"}), 500
# ✅ Start Production Server
if __name__ == "__main__":
serve(app, host="0.0.0.0", port=7860)
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