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import gradio as gr
import json
import torch
import wavio
from tqdm import tqdm
from huggingface_hub import snapshot_download
from models import AudioDiffusion, DDPMScheduler
from audioldm.audio.stft import TacotronSTFT
from audioldm.variational_autoencoder import AutoencoderKL
from gradio import Markdown
class Tango:
def __init__(self, name="declare-lab/tango", device="cuda:0"):
path = snapshot_download(repo_id=name)
vae_config = json.load(open("{}/vae_config.json".format(path)))
stft_config = json.load(open("{}/stft_config.json".format(path)))
main_config = json.load(open("{}/main_config.json".format(path)))
self.vae = AutoencoderKL(**vae_config).to(device)
self.stft = TacotronSTFT(**stft_config).to(device)
self.model = AudioDiffusion(**main_config).to(device)
vae_weights = torch.load("{}/pytorch_model_vae.bin".format(path), map_location=device)
stft_weights = torch.load("{}/pytorch_model_stft.bin".format(path), map_location=device)
main_weights = torch.load("{}/pytorch_model_main.bin".format(path), map_location=device)
self.vae.load_state_dict(vae_weights)
self.stft.load_state_dict(stft_weights)
self.model.load_state_dict(main_weights)
print ("Successfully loaded checkpoint from:", name)
self.vae.eval()
self.stft.eval()
self.model.eval()
self.scheduler = DDPMScheduler.from_pretrained(main_config["scheduler_name"], subfolder="scheduler")
def chunks(self, lst, n):
""" Yield successive n-sized chunks from a list. """
for i in range(0, len(lst), n):
yield lst[i:i + n]
def generate(self, prompt, steps=100, guidance=3, samples=1, disable_progress=True):
""" Genrate audio for a single prompt string. """
with torch.no_grad():
latents = self.model.inference([prompt], self.scheduler, steps, guidance, samples, disable_progress=disable_progress)
mel = self.vae.decode_first_stage(latents)
wave = self.vae.decode_to_waveform(mel)
return wave[0]
def generate_for_batch(self, prompts, steps=200, guidance=3, samples=1, batch_size=8, disable_progress=True):
""" Genrate audio for a list of prompt strings. """
outputs = []
for k in tqdm(range(0, len(prompts), batch_size)):
batch = prompts[k: k+batch_size]
with torch.no_grad():
latents = self.model.inference(batch, self.scheduler, steps, guidance, samples, disable_progress=disable_progress)
mel = self.vae.decode_first_stage(latents)
wave = self.vae.decode_to_waveform(mel)
outputs += [item for item in wave]
if samples == 1:
return outputs
else:
return list(self.chunks(outputs, samples))
# Initialize Tango model
tango = Tango()
def gradio_generate(prompt):
output_wave = tango.generate(prompt)
# Save the output_wave as a temporary WAV file
output_filename = "temp_output.wav"
wavio.write(output_filename, output_wave, rate=16000, sampwidth=2)
return output_filename
# Add the description text box
description_text = '''
TANGO is a latent diffusion model (LDM) for text-to-audio (TTA) generation. TANGO can generate realistic audios including human sounds, animal sounds, natural and artificial sounds and sound effects from textual prompts. We use the frozen instruction-tuned LLM Flan-T5 as the text encoder and train a UNet based diffusion model for audio generation. We perform comparably to current state-of-the-art models for TTA across both objective and subjective metrics, despite training the LDM on a 63 times smaller dataset. We release our model, training, inference code, and pre-trained checkpoints for the research community.
'''
# Define Gradio input and output components
input_text = gr.inputs.Textbox(lines=2, label="Prompt")
output_audio = gr.outputs.Audio(label="Generated Audio", type="filepath")
# Create Gradio interface
gr_interface = gr.Interface(
fn=gradio_generate,
inputs=input_text,
outputs=[output_audio],
title="TANGO: Text to Audio using Instruction-Guided Diffusion",
description="Generate audio using TANGO by providing a text prompt.",
allow_flagging=False,
examples=[
["An audience cheering and clapping"],
["Rolling thunder with lightning strikes"],
["A car engine revving"],
["A dog barking"],
["Emergency sirens wailing"],
["Whistling with birds chirping"],
["A dog barking and a man talking and a racing car passes by"],
["Motor vehicles are driving with loud engines and a person whistles"],
["People cheering in a stadium while rolling thunder and lightning strikes"],
["A helicopter is in flight"],
["A person snoring"]
],
cache_examples=False,
)
# Launch Gradio app
gr_interface.launch() |