app.py

import gradio as gr
import numpy as np
import torch
import random
from diffusers import DiffusionPipeline
from datasets import load_dataset
from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline

device = "cuda:0" if torch.cuda.is_available() else "cpu"

title = "GenAI StoryTeller"
description = """
Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for Speech Translation, 
Microsoft's [SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for Text-to-Speech and 
StabilityAI's [StableDiffusion](https://huggingface.co/stabilityai/sdxl-turbo) model for Image Generation
"""

# Load speech translation pipeline
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)

# Load text-to-speech processor from pretrained dataset
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")

model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)

embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)

# Load diffusion pipeline for image generation
if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
    pipe = pipe.to(device)

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

# Limit the file size
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

# Speech GenAI
# Function for translating different language using pretrained models
def translate(audio):
    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
    return outputs["text"]

# Function to synthesise the text using the processor above
def synthesise(text):
    inputs = processor(text=text, return_tensors="pt")
    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
    return speech.cpu()

# Main function
def speech_to_speech_translation(audio):
    translated_text = translate(audio)
    synthesised_speech = synthesise(translated_text)
    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)  # Ensure int16 format
    return 16000, synthesised_speech

# Function for text to speech
def text_to_speech(text):
    synthesised_speech = synthesise(text)
    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)  # Ensure int16 format
    return 16000, synthesised_speech

# Image GenAI
# Text to Image
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    image = pipe(
        prompt = prompt, 
        negative_prompt = negative_prompt,
        guidance_scale = guidance_scale, 
        num_inference_steps = num_inference_steps, 
        width = width, 
        height = height,
        generator = generator
    ).images[0] 
    
    return image

demo = gr.Blocks()

# Audio translation using microphone as the input
audio_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs=gr.Audio(label="Generated Speech", type="numpy"),
    examples=[["./english.wav"], ["./chinese.wav"]],
    title=title,
    description=description,
)

# File translation using uploaded files as input
file_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(source="upload", type="filepath"),
    outputs=gr.Audio(label="Generated Speech", type="numpy"),
    examples=[["./english.wav"], ["./chinese.wav"]],
    title=title,
    description=description,
)

 # Text translation using text as input
text_translate = gr.Interface(
    fn=text_to_speech, 
    inputs="textbox",
    outputs=gr.Audio(label="Generated Speech", type="numpy"),
    title=title,
    description=description
)

# Inputs for Image Generation
prompt = gr.Text(
                label="Prompt",
                show_label=True,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
                
height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )

guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,
                )
                
num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=12,
                    step=1,
                    value=2,
                )

result = gr.Image(label="Result", show_label=False)

# Text to Image interface
image_generation = gr.Interface(
    fn=infer,
    inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
    outputs=[result],
    title=title,
    description=description,
)

# Showcase the demo using different tabs of the different features
with demo:
    gr.TabbedInterface([audio_translate, file_translate, text_translate, image_generation], ["Speech to Text", "Audio to Text", "Text to Speech", "Text to Image"])

demo.launch()