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import subprocess
import os
import threading
import numpy as np
import librosa
import gradio as gr
from functools import lru_cache
from transformers import pipeline
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch
from huggingface_hub import login
# Install required dependencies
def install_missing_packages():
required_packages = {
"librosa": None,
"diffusers": ">=0.14.0",
"gradio": ">=3.35.2",
"huggingface_hub": None,
"accelerate": ">=0.20.1",
"transformers": ">=4.31.0",
"torch": ">=1.11.0"
}
for package, version in required_packages.items():
try:
__import__(package)
except ImportError:
package_name = f"{package}{version}" if version else package
subprocess.check_call(["pip", "install", package_name])
install_missing_packages()
# Hugging Face token authentication
hf_token = os.getenv("HF_TOKEN")
if hf_token:
login(hf_token)
else:
raise ValueError("HF_TOKEN environment variable not set.")
# Load the speech-to-text model
speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")
# Load Stable Diffusion model
text_to_image = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16
)
device = "cuda" if torch.cuda.is_available() else "cpu"
text_to_image.to(device)
text_to_image.enable_attention_slicing() # Optimizes memory usage
text_to_image.safety_checker = None # Disables safety checker
text_to_image.scheduler = DPMSolverMultistepScheduler.from_config(text_to_image.scheduler.config)
# Preprocess audio file into NumPy array
def preprocess_audio(audio_path):
try:
audio, sr = librosa.load(audio_path, sr=16000) # Resample to 16kHz
return np.array(audio, dtype=np.float32)
except Exception as e:
return f"Error in preprocessing audio: {str(e)}"
# Transcribe audio to text
@lru_cache(maxsize=10)
def transcribe_audio(audio_path):
try:
audio_array = preprocess_audio(audio_path)
if isinstance(audio_array, str): # Error message from preprocessing
return audio_array
result = speech_to_text(audio_array)
return result["text"]
except Exception as e:
return f"Error in transcription: {str(e)}"
# Generate image from text
@lru_cache(maxsize=10)
def generate_image_from_text(text):
try:
image = text_to_image(text, height=512, width=512).images[0]
return image
except Exception as e:
return f"Error in image generation: {str(e)}"
# Process audio input (speech-to-image)
def speech_to_image(audio_path):
transcription = transcribe_audio(audio_path)
if "Error" in transcription:
return None, f"Transcription failed: {transcription}"
image = generate_image_from_text(transcription)
if isinstance(image, str) and "Error" in image:
return None, f"Image generation failed: {image}"
return image
# Process text input (text-to-image)
def text_to_image_interface(input_text):
try:
image = generate_image_from_text(input_text)
return image
except Exception as e:
return f"Error: {str(e)}"
# Gradio interface
speech_to_image_interface = gr.Interface(
fn=speech_to_image,
inputs=gr.Audio(type="filepath", label="Upload audio file (WAV/MP3)"),
outputs=gr.Image(label="Generated Image"),
title="Speech-to-Image Generator",
description="Upload an audio file to generate an image based on the transcribed speech."
)
text_to_image_interface = gr.Interface(
fn=text_to_image_interface,
inputs=gr.Textbox(label="Enter Text", placeholder="Describe an image..."),
outputs=gr.Image(label="Generated Image"),
title="Text-to-Image Generator",
description="Enter text to generate an image based on the description."
)
# Combine interfaces into a single Gradio app
app = gr.TabbedInterface(
interface_list=[speech_to_image_interface, text_to_image_interface],
tab_names=["Speech-to-Image", "Text-to-Image"]
)
# Launch Gradio interface
app.launch(debug=True, share=True)
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