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Running
on
Zero
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 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") | |
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) | |
unique_filename = f"output_{uuid.uuid4().hex}.wav" | |
print(f"Saving audio to file: {unique_filename}") | |
# Save to file | |
torchaudio.save(unique_filename, output, sample_rate) | |
print(f"Audio saved: {unique_filename}") | |
# Return the path to the generated audio file | |
return unique_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) | |
interface = gr.Interface( | |
fn=gradio_generate, | |
inputs=[ | |
gr.Textbox(label="Prompt", placeholder="Enter your text prompt here"), | |
gr.Slider(0, 30, value=10, label="Duration in Seconds"), | |
gr.Slider(10, 150, value=50, step=5, label="Number of Diffusion Steps"), | |
gr.Slider(1, 10, value=4.5, step=0.5, label="CFG Scale") | |
], | |
outputs=gr.Audio(type="filepath", label="Generated Audio"), | |
title="TangoFlux Generator", | |
description="Generate variable-length stereo audio at 44.1kHz from text prompts using TangoFlux.", | |
examples=[ | |
[ | |
"Create a serene soundscape of a quiet beach at sunset.", # Text prompt | |
15, # Duration in Seconds | |
100, # Number of Diffusion Steps | |
4.5, # CFG Scale | |
], | |
["Rock beat played in a treated studio, session drumming on an acoustic kit.", | |
30, # Duration in Seconds | |
100, # Number of Diffusion Steps | |
7, # CFG Scale | |
] | |
]) | |
interface.launch() |