app.py

from pathlib import Path
from threading import Thread

import gdown
import gradio as gr
import librosa
import numpy as np
import torch

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(type="filepath")
            text = gr.Textbox()
        with gr.Column():
            with gr.Column():
                output_audio = gr.Audio(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]
                )

demo.queue().launch(share=True)