import spaces import gradio as gr import json import torch import wavio from tqdm import tqdm from huggingface_hub import snapshot_download from pydub import AudioSegment from gradio import Markdown import uuid import torch from diffusers import DiffusionPipeline,AudioPipelineOutput from transformers import CLIPTextModel, T5EncoderModel, AutoModel, T5Tokenizer, T5TokenizerFast from typing import Union from diffusers.utils.torch_utils import randn_tensor from tqdm import tqdm from TangoFlux import TangoFluxInference import torchaudio # Define the description text description_text = """ # TangoFlux Text-to-Audio Generation Generate high-quality audio from text descriptions using TangoFlux. ## Instructions: 1. Enter your text description in the prompt box 2. Adjust the generation parameters if desired 3. Click submit to generate audio ## Parameters: - Steps: Higher values give better quality but take longer - Guidance Scale: Controls how closely the generation follows the prompt - Duration: Length of the generated audio in seconds """ tangoflux = TangoFluxInference(name="declare-lab/TangoFlux") @spaces.GPU(duration=15) def gradio_generate(prompt, steps, guidance, duration): # Ensure duration has a default value if None if duration is None: duration = 10 output = tangoflux.generate(prompt, steps=steps, guidance_scale=guidance, duration=duration) filename = 'temp.wav' output = output[:,:int(duration*44100)] torchaudio.save(filename, output, 44100) return filename # Create custom interface with HTML badges with gr.Blocks(theme="soft") as gr_interface: # Add HTML badges at the top gr.HTML( """
""" ) # Title and description gr.Markdown("# TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization") gr.Markdown(description_text) # Input components with gr.Row(): with gr.Column(): input_text = gr.Textbox(lines=2, label="Prompt") with gr.Row(): denoising_steps = gr.Slider(minimum=10, maximum=100, value=25, step=5, label="Steps", interactive=True) guidance_scale = gr.Slider(minimum=1, maximum=10, value=4.5, step=0.5, label="Guidance Scale", interactive=True) duration_scale = gr.Slider(minimum=1, maximum=30, value=10, step=1, label="Duration", interactive=True) submit_btn = gr.Button("Generate Audio", variant="primary") with gr.Column(): output_audio = gr.Audio(label="Generated Audio", type="filepath") # Examples gr.Examples( examples=[ # [prompt, steps, guidance, duration] ["Quiet whispered conversation gradually fading into distant jet engine roar diminishing into silence", 25, 4.5, 10], ["Clear sound of bicycle tires crunching on loose gravel and dirt, followed by deep male laughter echoing", 25, 4.5, 10], ["Multiple ducks quacking loudly with splashing water and piercing wild animal shriek in background", 25, 4.5, 10], ["Powerful ocean waves crashing and receding on sandy beach with distant seagulls", 25, 4.5, 10], ["Gentle female voice cooing and baby responding with happy gurgles and giggles", 25, 4.5, 10], ["Clear male voice speaking, sharp popping sound, followed by genuine group laughter", 25, 4.5, 10], ["Stream of water hitting empty ceramic cup, pitch rising as cup fills up", 25, 4.5, 10], ["Massive crowd erupting in thunderous applause and excited cheering", 25, 4.5, 10], ["Deep rolling thunder with bright lightning strikes crackling through sky", 25, 4.5, 10], ["Aggressive dog barking and distressed cat meowing as racing car roars past at high speed", 25, 4.5, 10], ["Peaceful stream bubbling and birds singing, interrupted by sudden explosive gunshot", 25, 4.5, 10], ["Man speaking outdoors, goat bleating loudly, metal gate scraping closed, ducks quacking frantically, wind howling into microphone", 25, 4.5, 10], ["Series of loud aggressive dog barks echoing", 25, 4.5, 10], ["Multiple distinct cat meows at different pitches", 25, 4.5, 10], ["Rhythmic wooden table tapping overlaid with steady water pouring sound", 25, 4.5, 10], ["Sustained crowd applause with camera clicks and amplified male announcer voice", 25, 4.5, 10], ["Two sharp gunshots followed by panicked birds taking flight with rapid wing flaps", 25, 4.5, 10], ["Melodic human whistling harmonizing with natural birdsong", 25, 4.5, 10], ["Deep rhythmic snoring with clear breathing patterns", 25, 4.5, 10], ["Multiple racing engines revving and accelerating with sharp whistle piercing through", 25, 4.5, 10], ], inputs=[input_text, denoising_steps, guidance_scale, duration_scale], outputs=output_audio, fn=gradio_generate, cache_examples="lazy", ) # Connect the button click to the generation function submit_btn.click( fn=gradio_generate, inputs=[input_text, denoising_steps, guidance_scale, duration_scale], outputs=output_audio ) # Launch the interface gr_interface.queue(15).launch()