Spaces:
Running
Running
File size: 4,513 Bytes
b4aec50 0a02aec b4aec50 6b448ad b4aec50 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
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="cpu"):
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 Audio Generator",
description="Generate audio using Tango by providing a text prompt.",
allow_flagging=False,
examples=[
["A Dog Barking"],
["A loud thunderstorm"],
],
)
# Launch Gradio app
gr_interface.launch() |