File size: 12,017 Bytes
8337fa0 | 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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 | import json
import torch
from tqdm import tqdm
import sys
from model import PromptCondAudioDiffusion
from diffusers import DDIMScheduler, DDPMScheduler
import torchaudio
import librosa
import os
import math
import numpy as np
from tools.get_melvaehifigan48k import build_pretrained_models
import tools.torch_tools as torch_tools
from safetensors.torch import load_file
class MuCodec:
def __init__(self, \
model_path, \
layer_num, \
load_main_model=True, \
device="cuda:0"):
self.layer_num = layer_num - 1
self.sample_rate = 48000
self.device = device
self.MAX_DURATION = 360
if load_main_model:
audio_ldm_path = os.path.dirname(os.path.abspath(__file__)) + "/tools/audioldm_48k.pth"
self.vae, self.stft = build_pretrained_models(audio_ldm_path)
self.vae, self.stft = self.vae.eval().to(device), self.stft.eval().to(device)
main_config = {
"num_channels":32,
"unet_model_name":None,
"unet_model_config_path":os.path.dirname(os.path.abspath(__file__)) + "/configs/models/transformer2D.json",
"snr_gamma":None,
}
self.model = PromptCondAudioDiffusion(**main_config)
if model_path.endswith('.safetensors'):
main_weights = load_file(model_path)
else:
main_weights = torch.load(model_path, map_location='cpu')
self.model.load_state_dict(main_weights, strict=False)
self.model = self.model.to(device)
print ("Successfully loaded checkpoint from:", model_path)
else:
main_config = {
"num_channels":32,
"unet_model_name":None,
"unet_model_config_path":None,
"snr_gamma":None,
}
self.model = PromptCondAudioDiffusion(**main_config).to(device)
main_weights = torch.load(model_path, map_location='cpu')
self.model.load_state_dict(main_weights, strict=False)
self.model = self.model.to(device)
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)
def file2code(self, fname):
orig_samples, fs = torchaudio.load(fname)
if(fs!=self.sample_rate):
orig_samples = torchaudio.functional.resample(orig_samples, fs, self.sample_rate)
fs = self.sample_rate
if orig_samples.shape[0] == 1:
orig_samples = torch.cat([orig_samples, orig_samples], 0)
return self.sound2code(orig_samples)
@torch.no_grad()
@torch.autocast(device_type="cuda", dtype=torch.float32)
def sound2code(self, orig_samples, batch_size=3):
if(orig_samples.ndim == 2):
audios = orig_samples.unsqueeze(0).to(self.device)
elif(orig_samples.ndim == 3):
audios = orig_samples.to(self.device)
else:
assert orig_samples.ndim in (2,3), orig_samples.shape
audios = self.preprocess_audio(audios)
audios = audios.squeeze(0)
orig_length = audios.shape[-1]
min_samples = int(40.96 * self.sample_rate)
output_len = int(orig_length / float(self.sample_rate) * 25) + 1
print("output_len: ", output_len)
while(audios.shape[-1] < min_samples + 480):
audios = torch.cat([audios, audios], -1)
int_max_len=audios.shape[-1]//min_samples+1
# print("int_max_len: ", int_max_len)
audios = torch.cat([audios, audios], -1)
# print("audios:",audios.shape)
audios=audios[:,:int(int_max_len*(min_samples+480))]
codes_list=[]
audio_input = audios.reshape(2, -1, min_samples+480).permute(1, 0, 2).reshape(-1, 2, min_samples+480)
for audio_inx in range(0, audio_input.shape[0], batch_size):
# import pdb; pdb.set_trace()
codes, _, spk_embeds = self.model.fetch_codes_batch((audio_input[audio_inx:audio_inx+batch_size]), additional_feats=[],layer=self.layer_num)
codes_list.append(torch.cat(codes, 1))
# print("codes_list",codes_list[0].shape)
codes = torch.cat(codes_list, 0).permute(1,0,2).reshape(1, -1)[None] # B 3 T -> 3 B T
codes=codes[:,:,:output_len]
return codes
@torch.no_grad()
def code2sound(self, codes, prompt=None, duration=40.96, guidance_scale=1.5, num_steps=20, disable_progress=False):
codes = codes.to(self.device)
first_latent = torch.randn(codes.shape[0], 32, 512, 32).to(self.device)
first_latent_length = 0
first_latent_codes_length = 0
if(isinstance(prompt, torch.Tensor)):
prompt = prompt.to(self.device)
if(prompt.ndim == 3):
assert prompt.shape[0] == 1, prompt.shape
prompt = prompt[0]
elif(prompt.ndim == 1):
prompt = prompt.unsqueeze(0).repeat(2,1)
elif(prompt.ndim == 2):
if(prompt.shape[0] == 1):
prompt = prompt.repeat(2,1)
if(prompt.shape[-1] < int(30.76 * self.sample_rate)):
prompt = prompt[:,:int(10.24*self.sample_rate)] # limit max length to 10.24
else:
prompt = prompt[:,int(20.48*self.sample_rate):int(30.72*self.sample_rate)] # limit max length to 10.24
true_mel , _, _ = torch_tools.wav_to_fbank2(prompt, -1, fn_STFT=self.stft) # maximum 10.24s
true_mel = true_mel.unsqueeze(1).to(self.device)
true_latent = torch.cat([self.vae.get_first_stage_encoding(self.vae.encode_first_stage(true_mel[[m]])) for m in range(true_mel.shape[0])],0)
true_latent = true_latent.reshape(true_latent.shape[0]//2, -1, true_latent.shape[2], true_latent.shape[3]).detach()
first_latent[:,:,0:true_latent.shape[2],:] = true_latent
first_latent_length = true_latent.shape[2]
first_latent_codes = self.sound2code(prompt)[:,:,0:first_latent_length*2] # B 4 T
first_latent_codes_length = first_latent_codes.shape[-1]
codes = torch.cat([first_latent_codes, codes], -1)
min_samples = 1024
hop_samples = min_samples // 4 * 3
ovlp_samples = min_samples - hop_samples
hop_frames = hop_samples // 2
ovlp_frames = ovlp_samples // 2
codes_len= codes.shape[-1]
target_len = int((codes_len - first_latent_codes_length) / 100 * 4 * self.sample_rate)
if(codes_len < min_samples):
while(codes.shape[-1] < min_samples):
codes = torch.cat([codes, codes], -1)
codes = codes[:,:,0:min_samples]
codes_len = codes.shape[-1]
if((codes_len - ovlp_frames) % hop_samples > 0):
len_codes=math.ceil((codes_len - ovlp_samples) / float(hop_samples)) * hop_samples + ovlp_samples
while(codes.shape[-1] < len_codes):
codes = torch.cat([codes, codes], -1)
codes = codes[:,:,0:len_codes]
latent_length = 512
latent_list = []
spk_embeds = torch.zeros([1, 32, 1, 32], device=codes.device)
with torch.autocast(device_type="cuda", dtype=torch.float16):
for sinx in range(0, codes.shape[-1]-hop_samples, hop_samples):
codes_input=[]
codes_input.append(codes[:,:,sinx:sinx+min_samples])
if(sinx == 0):
incontext_length = first_latent_length
latents = self.model.inference_codes(codes_input, spk_embeds, first_latent, latent_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:,:]
len_add_to_512 = 512 - true_latent.shape[-2]
incontext_length = true_latent.shape[-2]
true_latent = torch.cat([true_latent, torch.randn(true_latent.shape[0], true_latent.shape[1], len_add_to_512, true_latent.shape[-1]).to(self.device)], -2)
latents = self.model.inference_codes(codes_input, spk_embeds, true_latent, latent_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(duration * self.sample_rate)
hop_samples = min_samples // 4 * 3
ovlp_samples = min_samples - hop_samples
with torch.no_grad():
output = None
for i in range(len(latent_list)):
latent = latent_list[i]
bsz , ch, t, f = latent.shape
latent = latent.reshape(bsz*2, ch//2, t, f)
mel = self.vae.decode_first_stage(latent)
cur_output = self.vae.decode_to_waveform(mel)
cur_output = torch.from_numpy(cur_output)[:, 0:min_samples]
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, threshold=0.8):
assert len(input_audios.shape) == 3, input_audios.shape
nchan = input_audios.shape[1]
input_audios = input_audios.reshape(input_audios.shape[0], -1)
norm_value = torch.ones_like(input_audios[:,0])
max_volume = input_audios.abs().max(dim=-1)[0]
norm_value[max_volume>threshold] = max_volume[max_volume>threshold] / threshold
return input_audios.reshape(input_audios.shape[0], nchan, -1)/norm_value.unsqueeze(-1).unsqueeze(-1)
@torch.no_grad()
def sound2sound(self, sound, prompt=None, min_duration=40.96, steps=50, disable_progress=False):
codes = self.sound2code(sound)
wave = self.code2sound(codes, prompt, duration=min_duration, guidance_scale=1.5, num_steps=steps, disable_progress=disable_progress)
return wave
if __name__=="__main__":
ckpt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "ckpt/mucodec.pt")
mucodec = MuCodec(model_path=ckpt_path,layer_num=7,load_main_model=True)
filelist = []
root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "test_wav")
for f in [os.path.join(root_dir, f) for f in os.listdir(root_dir) if '.flac' in f or '.wav' in f or '.mp3' in f]:
a, fs = torchaudio.load(f)
if(fs!=48000):
a = torchaudio.functional.resample(a, fs, 48000)
if(a.shape[0]==1):
a = torch.cat([a,a],0)
ori_len = a.shape[-1]
filelist.append([a, '', [0, a.shape[-1]/48000.], f,ori_len])
reconstructed_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "reconstructed")
os.makedirs(reconstructed_dir, exist_ok=True)
for sample_idx, (orig_samples, lyric, st_et, fname,ori_len) in enumerate(filelist):
print(fname, lyric)
wave = mucodec.sound2sound(orig_samples,None)
wave = wave[:,0:ori_len]
torchaudio.save(os.path.join(reconstructed_dir, os.path.basename(fname)),wave.detach().cpu(), 48000)
|