Spaces:
Sleeping
Sleeping
File size: 2,142 Bytes
fbe31d2 |
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 |
import gradio as gr
from model.CLAPSep import CLAPSep
import torch
import librosa
import numpy as np
model_config = {"lan_embed_dim": 1024,
"depths": [1, 1, 1, 1],
"embed_dim": 128,
"encoder_embed_dim": 128,
"phase": False,
"spec_factor": 8,
"d_attn": 640,
"n_masker_layer": 3,
"conv": False}
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
CLAP_path = "model/music_audioset_epoch_15_esc_90.14.pt"
model = CLAPSep(model_config, CLAP_path).to(DEVICE)
ckpt = torch.load('model/best_model.ckpt', map_location=DEVICE)
model.load_state_dict(ckpt, strict=False)
model.eval()
def inference(audio_file_path: str, text_p: str, text_n: str):
print(f"Separate audio from [{audio_file_path}] with textual query p: [{text_p}] and n: [{text_n}]")
mixture, _ = librosa.load(audio_file_path, sr=32000)
pad = (320000 - (len(mixture) % 320000))if len(mixture) % 320000 != 0 else 0
mixture =torch.tensor(np.pad(mixture,(0,pad)))
mixture_chunks = torch.chunk(mixture, dim=0, chunks=len(mixture)//320000)
sep_segments = []
for chunk in mixture_chunks:
with torch.no_grad():
sep_segments.append(model.inference_from_data(chunk.unsqueeze(0), [text_p], [text_n]))
sep_segment = torch.concat(sep_segments, dim=1)
return 32000, sep_segment.squeeze().numpy()
with gr.Blocks(title="CLAPSep") as demo:
with gr.Row():
with gr.Column():
input_audio = gr.Audio(label="Mixture", type="filepath")
text_p = gr.Textbox(label="Positive Query")
text_n = gr.Textbox(label="Negative 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_p, text_n], outputs=[output_audio]
)
demo.queue().launch(share=True) |