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import streamlit as st
import whisper
from transformers import AutoModelForCausalLM, AutoTokenizer
from gtts import gTTS
import os
# Hugging Face Token (if using a private model)
HF_AUTH_TOKEN = "" # Replace with your token if needed; leave empty for public models
# Load Whisper Model
@st.cache_resource
def load_whisper_model():
return whisper.load_model("base")
# Load Llama-2 Model
@st.cache_resource
def load_llama_model():
model_name = "meta-llama/Llama-2-7b-chat-hf" # Official Llama-2 model from Meta
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=HF_AUTH_TOKEN)
model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=HF_AUTH_TOKEN, torch_dtype="auto")
return tokenizer, model
# Initialize models
whisper_model = load_whisper_model()
llama_tokenizer, llama_model = load_llama_model()
# Streamlit App
def main():
st.title("Audio Query App with Llama-2 and Whisper")
# File upload
uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "m4a"])
if uploaded_file is not None:
# Save the audio file locally
input_audio_path = "input_audio.wav"
with open(input_audio_path, "wb") as f:
f.write(uploaded_file.read())
st.audio(input_audio_path, format="audio/wav")
# Step 1: Transcribe audio
with st.spinner("Transcribing audio..."):
transcription = whisper_model.transcribe(input_audio_path)["text"]
st.write(f"**Transcription:** {transcription}")
# Step 2: Generate response using Llama-2
with st.spinner("Generating response..."):
inputs = llama_tokenizer(transcription, return_tensors="pt")
outputs = llama_model.generate(**inputs, max_length=150)
response_text = llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write(f"**Response:** {response_text}")
# Step 3: Convert text response to audio
with st.spinner("Converting response to audio..."):
response_audio_path = "response_audio.mp3"
tts = gTTS(text=response_text, lang="en")
tts.save(response_audio_path)
st.audio(response_audio_path, format="audio/mp3")
if __name__ == "__main__":
main()
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