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from pathlib import Path
from threading import Thread
import gdown
import gradio as gr
import librosa
import numpy as np
import torch
from gradio_examples import EXAMPLES
from pipeline import build_audiosep
CHECKPOINTS_DIR = Path("checkpoint")
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# The model will be loaded in the future
MODEL_NAME = CHECKPOINTS_DIR / "audiosep_base_4M_steps.ckpt"
MODEL = build_audiosep(
config_yaml="config/audiosep_base.yaml",
checkpoint_path=MODEL_NAME,
device=DEVICE,
)
description = """
# AudioSep: Separate Anything You Describe
[[Project Page]](https://audio-agi.github.io/Separate-Anything-You-Describe) [[Paper]](https://audio-agi.github.io/Separate-Anything-You-Describe/AudioSep_arXiv.pdf) [[Code]](https://github.com/Audio-AGI/AudioSep)
AudioSep is a foundation model for open-domain sound separation with natural language queries.
AudioSep demonstrates strong separation performance and impressivezero-shot generalization ability on
numerous tasks such as audio event separation, musical instrument separation, and speech enhancement.
"""
def inference(audio_file_path: str, text: str):
print(f"Separate audio from [{audio_file_path}] with textual query [{text}]")
mixture, _ = librosa.load(audio_file_path, sr=32000, mono=True)
with torch.no_grad():
text = [text]
conditions = MODEL.query_encoder.get_query_embed(
modality="text", text=text, device=DEVICE
)
input_dict = {
"mixture": torch.Tensor(mixture)[None, None, :].to(DEVICE),
"condition": conditions,
}
sep_segment = MODEL.ss_model(input_dict)["waveform"]
sep_segment = sep_segment.squeeze(0).squeeze(0).data.cpu().numpy()
return 32000, np.round(sep_segment * 32767).astype(np.int16)
with gr.Blocks(title="AudioSep") as demo:
gr.Markdown(description)
with gr.Row():
with gr.Column():
input_audio = gr.Audio(label="Mixture", type="filepath")
text = gr.Textbox(label="Text Query")
with gr.Column():
with gr.Column():
output_audio = gr.Audio(label="Separation Result", scale=10)
button = gr.Button(
"Separate",
variant="primary",
scale=2,
size="lg",
interactive=True,
)
button.click(
fn=inference, inputs=[input_audio, text], outputs=[output_audio]
)
gr.Markdown("## Examples")
gr.Examples(examples=EXAMPLES, inputs=[input_audio, text])
demo.queue().launch(share=True)
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