app.py

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



tangoflux = TangoFluxInference(name="declare-lab/TangoFlux")



@spaces.GPU(duration=15)
def gradio_generate(prompt, steps, guidance,duration=10):

    output = tangoflux.generate(prompt,steps=steps,guidance_scale=guidance,duration=duration)
    #output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
    
    
    #wavio.write(output_filename, output_wave, rate=44100, sampwidth=2)
    filename = 'temp.wav'
    #print(f"Saving audio to file: {unique_filename}")

    # Save to file
    torchaudio.save(filename, output, 44100)
   

    # Return the path to the generated audio file
    return filename

    #if (output_format == "mp3"):
     #   AudioSegment.from_wav("temp.wav").export("temp.mp3", format = "mp3")
      #  output_filename = "temp.mp3"

    #return output_filename

description_text = """
<p><a href="https://huggingface.co/spaces/declare-lab/tango2/blob/main/app.py?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings. <br/><br/>
Generate high quality and faithful audio using TangoFlux by providing a text prompt in just a few seconds. TangoFlux was trained from scratch and was undergone alignment using a new method called Claped-Ranked Preference Optimization (CRPO).
<br/><br/> <a href="https://arxiv.org/abs/2412.xxxx">Read our paper.</a>
<p/>
"""
# Gradio input and output components
input_text = gr.Textbox(lines=2, label="Prompt")
#output_format = gr.Radio(label = "Output format", info = "The file you can dowload", choices =  "wav"], value = "wav")
output_audio = gr.Audio(label="Generated Audio", type="filepath")
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)


# Gradio interface
gr_interface = gr.Interface(
    fn=gradio_generate,
    inputs=[input_text, denoising_steps, guidance_scale,duration_scale],
    outputs=output_audio,
    title="TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization",
    description=description_text,
    allow_flagging=False,
    examples=[
["Quiet whispered conversation gradually fading into distant jet engine roar diminishing into silence"],
["Clear sound of bicycle tires crunching on loose gravel and dirt, followed by deep male laughter echoing"],
["Multiple ducks quacking loudly with splashing water and piercing wild animal shriek in background"],
["Powerful ocean waves crashing and receding on sandy beach with distant seagulls"],
["Gentle female voice cooing and baby responding with happy gurgles and giggles"],
["Clear male voice speaking, sharp popping sound, followed by genuine group laughter"],
["Stream of water hitting empty ceramic cup, pitch rising as cup fills up"],
["Massive crowd erupting in thunderous applause and excited cheering"],
["Deep rolling thunder with bright lightning strikes crackling through sky"],
["Aggressive dog barking and distressed cat meowing as racing car roars past at high speed"],
["Peaceful stream bubbling and birds singing, interrupted by sudden explosive gunshot"],
["Man speaking outdoors, goat bleating loudly, metal gate scraping closed, ducks quacking frantically, wind howling into microphone"],
["Series of loud aggressive dog barks echoing"],
["Multiple distinct cat meows at different pitches"],
["Rhythmic wooden table tapping overlaid with steady water pouring sound"],
["Sustained crowd applause with camera clicks and amplified male announcer voice"],
["Two sharp gunshots followed by panicked birds taking flight with rapid wing flaps"],
["Melodic human whistling harmonizing with natural birdsong"],
["Deep rhythmic snoring with clear breathing patterns"],
["Multiple racing engines revving and accelerating with sharp whistle piercing through"],
["Massive stadium crowd cheering as thunder crashes and lightning strikes"],
["Heavy helicopter blades chopping through air with engine and wind noise"],
["Dog barking excitedly and man shouting as race car engine roars past"],
["Quiet speech and then and airplane flying away"],
["A bicycle peddling on dirt and gravel followed by a man speaking then laughing"],
["Ducks quack and water splashes with some animal screeching in the background"],
["Describe the sound of the ocean"],
["A woman and a baby are having a conversation"],
["A man speaks followed by a popping noise and laughter"],
["A cup is filled from a faucet"],
["An audience cheering and clapping"],
["Rolling thunder with lightning strikes"],
["A dog barking and a cat mewing and a racing car passes by"],
["Gentle water stream, birds chirping and sudden gun shot"],
["A dog barking"],
["A cat meowing"],
["Wooden table tapping sound while water pouring"],
["Applause from a crowd with distant clicking and a man speaking over a loudspeaker"],
["two gunshots followed by birds flying away while chirping"],
["Whistling with birds chirping"],
["A person snoring"],
["Motor vehicles are driving with loud engines and a person whistles"],
["People cheering in a stadium while thunder and lightning strikes"],
["A helicopter is in flight"],
["A dog barking and a man talking and a racing car passes by"],
    ],
    cache_examples="lazy", # Turn on to cache.
)



gr_interface.queue(15).launch()