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import spaces |
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import gradio as gr |
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import json |
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import torch |
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import wavio |
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from tqdm import tqdm |
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from huggingface_hub import snapshot_download |
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from models import AudioDiffusion, DDPMScheduler |
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from audioldm.audio.stft import TacotronSTFT |
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from audioldm.variational_autoencoder import AutoencoderKL |
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from pydub import AudioSegment |
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from gradio import Markdown |
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import torch |
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from diffusers import DiffusionPipeline,AudioPipelineOutput |
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from transformers import CLIPTextModel, T5EncoderModel, AutoModel, T5Tokenizer, T5TokenizerFast |
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from typing import Union |
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from diffusers.utils.torch_utils import randn_tensor |
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from tqdm import tqdm |
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from TangoFlux import TangoFluxInference |
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tangoflux = TangoFluxInference(path="declare-lab/TangoFlux") |
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@spaces.GPU(duration=15) |
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def gradio_generate(prompt, output_format, steps, guidance,duration=10): |
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output_wave = tangoflux.generate(prompt,steps=steps,guidance=guidance,duration=duration) |
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output_wave = pipe(prompt,steps,guidance) |
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output_wave = output_wave.audios[0] |
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output_filename = "temp.wav" |
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wavio.write(output_filename, output_wave, rate=16000, sampwidth=2) |
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if (output_format == "mp3"): |
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AudioSegment.from_wav("temp.wav").export("temp.mp3", format = "mp3") |
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output_filename = "temp.mp3" |
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return output_filename |
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description_text = """ |
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<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/> |
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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> |
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<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> |
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<p/> |
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""" |
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input_text = gr.Textbox(lines=2, label="Prompt") |
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output_format = gr.Radio(label = "Output format", info = "The file you can dowload", choices = ["mp3", "wav"], value = "wav") |
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output_audio = gr.Audio(label="Generated Audio", type="filepath") |
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denoising_steps = gr.Slider(minimum=10, maximum=100, value=25, step=1, label="Steps", interactive=True) |
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guidance_scale = gr.Slider(minimum=1, maximum=10, value=3, step=0.1, label="Guidance Scale", interactive=True) |
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duration_scale = gr.Slider(minimum=1, maximum=30, value=10, step=1, label="Duration", interactive=True) |
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gr_interface = gr.Interface( |
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fn=gradio_generate, |
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inputs=[input_text, output_format, denoising_steps, guidance_scale,duration_scale], |
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outputs=[output_audio], |
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title="TangoFlux: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization", |
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description=description_text, |
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allow_flagging=False, |
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examples=[ |
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["Quiet speech and then and airplane flying away"], |
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["A bicycle peddling on dirt and gravel followed by a man speaking then laughing"], |
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["Ducks quack and water splashes with some animal screeching in the background"], |
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["Describe the sound of the ocean"], |
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["A woman and a baby are having a conversation"], |
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["A man speaks followed by a popping noise and laughter"], |
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["A cup is filled from a faucet"], |
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["An audience cheering and clapping"], |
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["Rolling thunder with lightning strikes"], |
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["A dog barking and a cat mewing and a racing car passes by"], |
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["Gentle water stream, birds chirping and sudden gun shot"], |
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["A man talking followed by a goat baaing then a metal gate sliding shut as ducks quack and wind blows into a microphone."], |
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["A dog barking"], |
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["A cat meowing"], |
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["Wooden table tapping sound while water pouring"], |
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["Applause from a crowd with distant clicking and a man speaking over a loudspeaker"], |
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["two gunshots followed by birds flying away while chirping"], |
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["Whistling with birds chirping"], |
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["A person snoring"], |
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["Motor vehicles are driving with loud engines and a person whistles"], |
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["People cheering in a stadium while thunder and lightning strikes"], |
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["A helicopter is in flight"], |
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["A dog barking and a man talking and a racing car passes by"], |
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], |
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cache_examples="lazy", |
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) |
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gr_interface.queue(10).launch() |
