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import whisper as openai_whisper
from transformers import AutoModelForCausalLM, AutoTokenizer
from TTS.api import TTS
import gradio as gr
import torch
import os
# 1. Speech-to-Text (STT) Implementation
def setup_stt():
model = openai_whisper.load_model("base") # Explicit OpenAI Whisper
return model
def transcribe_audio(model, audio_file):
result = model.transcribe(audio_file)
print("Transcription:", result['text'])
return result['text']
# 2. Natural Language Processing (NLP) Implementation
def setup_nlp():
model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
return tokenizer, model
def generate_response(tokenizer, model, input_text):
prompt = f"User: {input_text}\nAssistant:"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
response = model.generate(
input_ids,
max_length=150,
num_return_sequences=1,
temperature=0.7,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=2
)
return tokenizer.decode(response[0], skip_special_tokens=True)
# 3. Text-to-Speech (TTS) Implementation
def setup_tts():
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC")
return tts
def generate_speech(tts, text, file_path="output.wav"):
tts.tts_to_file(text, file_path=file_path)
return file_path
# 4. Voice AI System Class
class VoiceAISystem:
def __init__(self):
print("Initializing Voice AI System...")
print("Loading STT model...")
self.stt_model = setup_stt()
print("Loading NLP model...")
self.tokenizer, self.nlp_model = setup_nlp()
print("Loading TTS model...")
self.tts_model = setup_tts()
# GPU Optimization
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {self.device}")
self.nlp_model = self.nlp_model.to(self.device)
print("System initialization complete!")
def process_audio(self, audio_file):
try:
os.makedirs("tmp", exist_ok=True)
print("Transcribing audio...")
text = transcribe_audio(self.stt_model, audio_file)
print("Generating response...")
with torch.cuda.amp.autocast(enabled=torch.cuda.is_available()):
response = generate_response(self.tokenizer, self.nlp_model, text)
print("Converting response to speech...")
output_path = os.path.join("tmp", "response.wav")
audio_response = generate_speech(self.tts_model, response, output_path)
return audio_response, text, response
except Exception as e:
print(f"Error during processing: {str(e)}")
return None, f"Error: {str(e)}", "Error processing request"
# 5. Gradio UI Integration
def create_voice_ai_interface():
system = VoiceAISystem()
def chat(audio):
if audio is None:
return None, "No audio provided", "No response generated"
return system.process_audio(audio)
interface = gr.Interface(
fn=chat,
inputs=[
gr.Audio(
type="filepath",
label="Speak here"
)
],
outputs=[
gr.Audio(label="AI Response"),
gr.Textbox(label="Transcribed Text"),
gr.Textbox(label="AI Response Text")
],
title="Voice AI System",
description="Click to record your voice and interact with the AI"
)
return interface
# Launch the interface
if __name__ == "__main__":
iface = create_voice_ai_interface()
iface.launch(share=True)
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