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