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)
    print(f"Audio saved: {unique_filename}")

    # 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 audio using Tango2 by providing a text prompt. Tango2 was built from Tango and was trained on <a href="https://huggingface.co/datasets/declare-lab/audio-alpaca">Audio-alpaca</a>
<br/><br/> This is the demo for Tango2 for text to audio generation: <a href="https://arxiv.org/abs/2404.09956">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: ",
    description=description_text,
    allow_flagging=False,




    examples=[
        ["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 man talking followed by a goat baaing then a metal gate sliding shut as ducks quack and wind blows into a microphone."],
        ["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()