File size: 3,124 Bytes
b0b5043 4a997af b0b5043 60b0b0c b0b5043 4a997af b0b5043 4a997af 60b0b0c b0b5043 4a997af 60b0b0c b0b5043 60b0b0c b0b5043 4a997af b0b5043 60b0b0c b0b5043 60b0b0c b0b5043 4a997af b0b5043 60b0b0c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
import os
import torch
import streamlit as st
from groq import Groq
from diffusers import AutoPipelineForText2Image
import tempfile
import soundfile as sf
# Load API keys
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
HF_API_KEY = os.getenv("HF_API_KEY")
# Initialize Groq client with API key
client = Groq(api_key=GROQ_API_KEY)
# Load lightweight Hugging Face image generation model
image_gen = AutoPipelineForText2Image.from_pretrained(
"stabilityai/sdxl-turbo", use_auth_token=HF_API_KEY
)
image_gen.to("cuda" if torch.cuda.is_available() else "cpu")
# Function to transcribe Tamil audio using Groq's Whisper
def transcribe(audio_path):
with open(audio_path, "rb") as file:
transcription = client.audio.transcriptions.create(
file=(audio_path, file.read()),
model="whisper-large-v3",
language="ta", # Tamil
response_format="verbose_json"
)
return transcription["text"]
# Function to translate Tamil to English using Groq's Gemma
def translate_text(tamil_text):
response = client.chat.completions.create(
model="gemma-7b-it",
messages=[{"role": "user", "content": f"Translate this Tamil text to English: {tamil_text}"}]
)
return response.choices[0].delta.content
# Function to generate text using Groq's DeepSeek R1
def generate_text(prompt):
response = client.chat.completions.create(
model="deepseek-coder-r1-7b",
messages=[{"role": "user", "content": f"Write a short story about: {prompt}"}]
)
return response.choices[0].delta.content
# Function to generate an image
def generate_image(prompt):
img = image_gen(prompt=prompt).images[0]
return img
# Streamlit UI
st.title("Tamil Speech to Image & Story Generator")
# Audio input - Recording or Uploading
st.subheader("Upload or Record Audio")
recorded_audio = st.audio("", format='audio/wav', start_time=0)
uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "m4a"])
audio_path = None
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
temp_audio.write(uploaded_file.read())
audio_path = temp_audio.name
elif recorded_audio:
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
audio_data, samplerate = sf.read(recorded_audio)
sf.write(temp_audio.name, audio_data, samplerate)
audio_path = temp_audio.name
if st.button("Generate") and audio_path:
with st.spinner("Transcribing Tamil speech..."):
tamil_text = transcribe(audio_path)
with st.spinner("Translating to English..."):
english_text = translate_text(tamil_text)
with st.spinner("Generating story..."):
story = generate_text(english_text)
with st.spinner("Generating image..."):
image = generate_image(english_text)
st.subheader("Tamil Transcription")
st.write(tamil_text)
st.subheader("English Translation")
st.write(english_text)
st.subheader("Generated Story")
st.write(story)
st.subheader("Generated Image")
st.image(image)
|