import json import torch from tqdm import tqdm from model_septoken import PromptCondAudioDiffusion from diffusers import DDIMScheduler, DDPMScheduler import torchaudio import librosa import os import math import numpy as np # from tools.get_mulan import get_mulan from tools.get_1dvae_large import get_model import tools.torch_tools as torch_tools from safetensors.torch import load_file from third_party.demucs.models.pretrained import get_model_from_yaml from filelock import FileLock import kaldiio # os.path.join(args.model_dir, "htdemucs.pth"), os.path.join(args.model_dir, "htdemucs.yaml") class Separator: def __init__(self, dm_model_path='demucs/ckpt/htdemucs.pth', dm_config_path='demucs/ckpt/htdemucs.yaml', gpu_id=0) -> None: 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] return a def run(self, audio_path, output_dir='demucs/test_output', ext=".flac"): name, _ = os.path.splitext(os.path.split(audio_path)[-1]) output_paths = [] # lock_path = os.path.join(output_dir, f"{name}.lock") # with FileLock(lock_path): # 加一个避免多卡访问时死锁 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 # drums_path, bass_path, other_path, vocal_path = output_paths vocal_path = output_paths[0] else: lock_path = os.path.join(output_dir, f"{name}_separate.lock") with FileLock(lock_path): drums_path, bass_path, other_path, vocal_path = self.demucs_model.separate(audio_path, output_dir, device=self.device) full_audio = self.load_audio(audio_path) vocal_audio = self.load_audio(vocal_path) minlen = min(full_audio.shape[-1], vocal_audio.shape[-1]) # bgm_audio = full_audio[:, 0:minlen] - vocal_audio[:, 0:minlen] bgm_audio = self.load_audio(drums_path) + self.load_audio(bass_path) + self.load_audio(other_path) for path in [drums_path, bass_path, other_path, vocal_path]: os.remove(path) return full_audio, vocal_audio, bgm_audio class Tango: def __init__(self, \ model_path, \ vae_config, vae_model, layer_vocal=7,\ layer_bgm=3,\ device="cuda:0"): self.sample_rate = 48000 scheduler_name = "configs/scheduler/stable_diffusion_2.1_largenoise_sample.json" self.device = device self.vae = get_model(vae_config, vae_model) self.vae = self.vae.to(device) self.vae=self.vae.eval() self.layer_vocal=layer_vocal self.layer_bgm=layer_bgm self.MAX_DURATION = 360 main_config = { "num_channels":32, "unet_model_name":None, "unet_model_config_path":"configs/models/transformer2D_wocross_inch112_1x4_multi_large.json", "snr_gamma":None, } self.model = PromptCondAudioDiffusion(**main_config).to(device) if model_path.endswith(".safetensors"): main_weights = load_file(model_path) else: main_weights = torch.load(model_path, map_location=device) self.model.load_state_dict(main_weights, strict=False) print ("Successfully loaded checkpoint from:", model_path) self.model.eval() self.model.init_device_dtype(torch.device(device), torch.float32) print("scaling factor: ", self.model.normfeat.std) # self.scheduler = DDIMScheduler.from_pretrained( \ # scheduler_name, subfolder="scheduler") # self.scheduler = DDPMScheduler.from_pretrained( \ # scheduler_name, subfolder="scheduler") print("Successfully loaded inference scheduler from {}".format(scheduler_name)) @torch.no_grad() @torch.autocast(device_type="cuda", dtype=torch.float32) def sound2code(self, orig_vocal, orig_bgm, batch_size=8): if(orig_vocal.ndim == 2): audios_vocal = orig_vocal.unsqueeze(0).to(self.device) elif(orig_vocal.ndim == 3): audios_vocal = orig_vocal.to(self.device) else: assert orig_vocal.ndim in (2,3), orig_vocal.shape if(orig_bgm.ndim == 2): audios_bgm = orig_bgm.unsqueeze(0).to(self.device) elif(orig_bgm.ndim == 3): audios_bgm = orig_bgm.to(self.device) else: assert orig_bgm.ndim in (2,3), orig_bgm.shape audios_vocal = self.preprocess_audio(audios_vocal) audios_vocal = audios_vocal.squeeze(0) audios_bgm = self.preprocess_audio(audios_bgm) audios_bgm = audios_bgm.squeeze(0) if audios_vocal.shape[-1] > audios_bgm.shape[-1]: audios_vocal = audios_vocal[:,:audios_bgm.shape[-1]] else: audios_bgm = audios_bgm[:,:audios_vocal.shape[-1]] orig_length = audios_vocal.shape[-1] min_samples = int(40 * self.sample_rate) # 40秒对应10个token output_len = int(orig_length / float(self.sample_rate) * 25) + 1 while(audios_vocal.shape[-1] < min_samples): audios_vocal = torch.cat([audios_vocal, audios_vocal], -1) audios_bgm = torch.cat([audios_bgm, audios_bgm], -1) int_max_len=audios_vocal.shape[-1]//min_samples+1 audios_vocal = torch.cat([audios_vocal, audios_vocal], -1) audios_bgm = torch.cat([audios_bgm, audios_bgm], -1) audios_vocal=audios_vocal[:,:int(int_max_len*(min_samples))] audios_bgm=audios_bgm[:,:int(int_max_len*(min_samples))] codes_vocal_list=[] codes_bgm_list=[] audio_vocal_input = audios_vocal.reshape(2, -1, min_samples).permute(1, 0, 2).reshape(-1, 2, min_samples) audio_bgm_input = audios_bgm.reshape(2, -1, min_samples).permute(1, 0, 2).reshape(-1, 2, min_samples) for audio_inx in range(0, audio_vocal_input.shape[0], batch_size): [codes_vocal,codes_bgm], _, spk_embeds = self.model.fetch_codes_batch((audio_vocal_input[audio_inx:audio_inx+batch_size]), (audio_bgm_input[audio_inx:audio_inx+batch_size]), additional_feats=[],layer_vocal=self.layer_vocal,layer_bgm=self.layer_bgm) codes_vocal_list.append(codes_vocal) codes_bgm_list.append(codes_bgm) codes_vocal = torch.cat(codes_vocal_list, 0).permute(1,0,2).reshape(1, -1)[None] codes_bgm = torch.cat(codes_bgm_list, 0).permute(1,0,2).reshape(1, -1)[None] codes_vocal=codes_vocal[:,:,:output_len] codes_bgm=codes_bgm[:,:,:output_len] return codes_vocal, codes_bgm @torch.no_grad() def code2sound(self, codes, prompt_vocal=None, prompt_bgm=None, duration=40, guidance_scale=1.5, num_steps=20, disable_progress=False): codes_vocal,codes_bgm = codes codes_vocal = codes_vocal.to(self.device) codes_bgm = codes_bgm.to(self.device) min_samples = duration * 25 # 40ms per frame hop_samples = min_samples // 4 * 3 ovlp_samples = min_samples - hop_samples hop_frames = hop_samples ovlp_frames = ovlp_samples first_latent = torch.randn(codes_vocal.shape[0], min_samples, 64).to(self.device) first_latent_length = 0 first_latent_codes_length = 0 if(isinstance(prompt_vocal, torch.Tensor)): # prepare prompt prompt_vocal = prompt_vocal.to(self.device) prompt_bgm = prompt_bgm.to(self.device) if(prompt_vocal.ndim == 3): assert prompt_vocal.shape[0] == 1, prompt_vocal.shape prompt_vocal = prompt_vocal[0] prompt_bgm = prompt_bgm[0] elif(prompt_vocal.ndim == 1): prompt_vocal = prompt_vocal.unsqueeze(0).repeat(2,1) prompt_bgm = prompt_bgm.unsqueeze(0).repeat(2,1) elif(prompt_vocal.ndim == 2): if(prompt_vocal.shape[0] == 1): prompt_vocal = prompt_vocal.repeat(2,1) prompt_bgm = prompt_bgm.repeat(2,1) if(prompt_vocal.shape[-1] < int(30 * self.sample_rate)): # if less than 30s, just choose the first 10s prompt_vocal = prompt_vocal[:,:int(10*self.sample_rate)] # limit max length to 10.24 prompt_bgm = prompt_bgm[:,:int(10*self.sample_rate)] # limit max length to 10.24 else: # else choose from 20.48s which might includes verse or chorus prompt_vocal = prompt_vocal[:,int(20*self.sample_rate):int(30*self.sample_rate)] # limit max length to 10.24 prompt_bgm = prompt_bgm[:,int(20*self.sample_rate):int(30*self.sample_rate)] # limit max length to 10.24 true_latent = self.vae.encode_audio(prompt_vocal+prompt_bgm).permute(0,2,1) first_latent[:,0:true_latent.shape[1],:] = true_latent first_latent_length = true_latent.shape[1] first_latent_codes = self.sound2code(prompt_vocal, prompt_bgm) first_latent_codes_vocal = first_latent_codes[0] first_latent_codes_bgm = first_latent_codes[1] first_latent_codes_length = first_latent_codes_vocal.shape[-1] codes_vocal = torch.cat([first_latent_codes_vocal, codes_vocal], -1) codes_bgm = torch.cat([first_latent_codes_bgm, codes_bgm], -1) codes_len= codes_vocal.shape[-1] target_len = int((codes_len - first_latent_codes_length) / 100 * 4 * self.sample_rate) # target_len = int(codes_len / 100 * 4 * self.sample_rate) # code repeat if(codes_len < min_samples): while(codes_vocal.shape[-1] < min_samples): codes_vocal = torch.cat([codes_vocal, codes_vocal], -1) codes_bgm = torch.cat([codes_bgm, codes_bgm], -1) codes_vocal = codes_vocal[:,:,0:min_samples] codes_bgm = codes_bgm[:,:,0:min_samples] codes_len = codes_vocal.shape[-1] if((codes_len - ovlp_samples) % hop_samples > 0): len_codes=math.ceil((codes_len - ovlp_samples) / float(hop_samples)) * hop_samples + ovlp_samples while(codes_vocal.shape[-1] < len_codes): codes_vocal = torch.cat([codes_vocal, codes_vocal], -1) codes_bgm = torch.cat([codes_bgm, codes_bgm], -1) codes_vocal = codes_vocal[:,:,0:len_codes] codes_bgm = codes_bgm[:,:,0:len_codes] latent_length = min_samples latent_list = [] spk_embeds = torch.zeros([1, 32, 1, 32], device=codes_vocal.device) with torch.autocast(device_type="cuda", dtype=torch.float16): for sinx in range(0, codes_vocal.shape[-1]-hop_samples, hop_samples): codes_vocal_input=codes_vocal[:,:,sinx:sinx+min_samples] codes_bgm_input=codes_bgm[:,:,sinx:sinx+min_samples] if(sinx == 0): incontext_length = first_latent_length latents = self.model.inference_codes([codes_vocal_input,codes_bgm_input], spk_embeds, first_latent, latent_length, incontext_length=incontext_length, additional_feats=[], guidance_scale=1.5, num_steps = num_steps, disable_progress=disable_progress, scenario='other_seg') latent_list.append(latents) else: true_latent = latent_list[-1][:,:,-ovlp_frames:].permute(0,2,1) len_add_to_1000 = min_samples - true_latent.shape[-2] incontext_length = true_latent.shape[-2] true_latent = torch.cat([true_latent, torch.randn(true_latent.shape[0], len_add_to_1000, true_latent.shape[-1]).to(self.device)], -2) latents = self.model.inference_codes([codes_vocal_input,codes_bgm_input], spk_embeds, true_latent, latent_length, incontext_length=incontext_length, additional_feats=[], guidance_scale=1.5, num_steps = num_steps, disable_progress=disable_progress, scenario='other_seg') latent_list.append(latents) latent_list = [l.float() for l in latent_list] latent_list[0] = latent_list[0][:,:,first_latent_length:] min_samples = int(min_samples * self.sample_rate // 1000 * 40) hop_samples = int(hop_samples * self.sample_rate // 1000 * 40) ovlp_samples = min_samples - hop_samples with torch.no_grad(): output = None for i in range(len(latent_list)): latent = latent_list[i] cur_output = self.vae.decode_audio(latent)[0].detach().cpu() if output is None: output = cur_output else: ov_win = torch.from_numpy(np.linspace(0, 1, ovlp_samples)[None, :]) ov_win = torch.cat([ov_win, 1 - ov_win], -1) output[:, -ovlp_samples:] = output[:, -ovlp_samples:] * ov_win[:, -ovlp_samples:] + cur_output[:, 0:ovlp_samples] * ov_win[:, 0:ovlp_samples] output = torch.cat([output, cur_output[:, ovlp_samples:]], -1) output = output[:, 0:target_len] return output @torch.no_grad() def preprocess_audio(self, input_audios_vocal, threshold=0.8): assert len(input_audios_vocal.shape) == 3, input_audios_vocal.shape nchan = input_audios_vocal.shape[1] input_audios_vocal = input_audios_vocal.reshape(input_audios_vocal.shape[0], -1) norm_value = torch.ones_like(input_audios_vocal[:,0]) max_volume = input_audios_vocal.abs().max(dim=-1)[0] norm_value[max_volume>threshold] = max_volume[max_volume>threshold] / threshold return input_audios_vocal.reshape(input_audios_vocal.shape[0], nchan, -1)/norm_value.unsqueeze(-1).unsqueeze(-1) @torch.no_grad() def sound2sound(self, orig_vocal,orig_bgm, prompt_vocal=None,prompt_bgm=None, steps=50, disable_progress=False): codes_vocal, codes_bgm = self.sound2code(orig_vocal,orig_bgm) codes=[codes_vocal, codes_bgm] wave = self.code2sound(codes, prompt_vocal,prompt_bgm, guidance_scale=1.5, num_steps=steps, disable_progress=disable_progress) return wave