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on
L40S
Running
on
L40S
import torchaudio | |
import os | |
import torch | |
from third_party.demucs.models.pretrained import get_model_from_yaml | |
class Separator(torch.nn.Module): | |
def __init__(self, dm_model_path='third_party/demucs/ckpt/htdemucs.pth', dm_config_path='third_party/demucs/ckpt/htdemucs.yaml', gpu_id=0) -> None: | |
super().__init__() | |
if torch.cuda.is_available() and gpu_id < torch.cuda.device_count(): | |
self.device = torch.device(f"cuda:{gpu_id}") | |
else: | |
self.device = torch.device("cpu") | |
self.demucs_model = self.init_demucs_model(dm_model_path, dm_config_path) | |
def init_demucs_model(self, model_path, config_path): | |
model = get_model_from_yaml(config_path, model_path) | |
model.to(self.device) | |
model.eval() | |
return model | |
def load_audio(self, f): | |
a, fs = torchaudio.load(f) | |
if (fs != 48000): | |
a = torchaudio.functional.resample(a, fs, 48000) | |
if a.shape[-1] >= 48000*10: | |
a = a[..., :48000*10] | |
else: | |
a = torch.cat([a, a], -1) | |
return a[:, 0:48000*10] | |
def run(self, audio_path, output_dir='tmp', ext=".flac"): | |
os.makedirs(output_dir, exist_ok=True) | |
name, _ = os.path.splitext(os.path.split(audio_path)[-1]) | |
output_paths = [] | |
for stem in self.demucs_model.sources: | |
output_path = os.path.join(output_dir, f"{name}_{stem}{ext}") | |
if os.path.exists(output_path): | |
output_paths.append(output_path) | |
if len(output_paths) == 1: # 4 | |
vocal_path = output_paths[0] | |
else: | |
drums_path, bass_path, other_path, vocal_path = self.demucs_model.separate(audio_path, output_dir, device=self.device) | |
for path in [drums_path, bass_path, other_path]: | |
os.remove(path) | |
full_audio = self.load_audio(audio_path) | |
vocal_audio = self.load_audio(vocal_path) | |
bgm_audio = full_audio - vocal_audio | |
return full_audio, vocal_audio, bgm_audio | |