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| import gradio as gr | |
| import torch | |
| import soundfile as sf | |
| from transformers import pipeline | |
| from groq import Groq | |
| from TTS.api import TTS | |
| import os | |
| # ---------------------------- | |
| # Load models | |
| # ---------------------------- | |
| # Whisper (Speech → Text) | |
| stt = pipeline( | |
| "automatic-speech-recognition", | |
| model="openai/whisper-small" | |
| ) | |
| # Groq Client | |
| client = Groq(api_key=os.environ["GROQ_API_KEY"]) | |
| # Text → Speech | |
| tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False) | |
| # ---------------------------- | |
| # Core function | |
| # ---------------------------- | |
| def voice_to_voice(audio): | |
| # Speech → Text | |
| text = stt(audio)["text"] | |
| # LLM Response | |
| completion = client.chat.completions.create( | |
| model="llama3-8b-8192", | |
| messages=[{"role": "user", "content": text}] | |
| ) | |
| reply = completion.choices[0].message.content | |
| # Text → Speech | |
| output_path = "response.wav" | |
| tts.tts_to_file(text=reply, file_path=output_path) | |
| return reply, output_path | |
| # ---------------------------- | |
| # UI | |
| # ---------------------------- | |
| ui = gr.Interface( | |
| fn=voice_to_voice, | |
| inputs=gr.Audio(type="filepath", label="🎤 Speak"), | |
| outputs=[ | |
| gr.Textbox(label="🧠 AI Response"), | |
| gr.Audio(label="🔊 Voice Reply") | |
| ], | |
| title="Voice to Voice AI (Groq + Hugging Face)", | |
| description="Speak → AI thinks → AI speaks back" | |
| ) | |
| ui.launch() | |