Upload 4 files

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 cp_8_gm_post/vae_00035000 filter=lfs diff=lfs merge=lfs -text
 cp_8_vgg3/vae_00620000 filter=lfs diff=lfs merge=lfs -text
+cp_8_vq3/vae_00430000 filter=lfs diff=lfs merge=lfs -text

cp_8_vq3/config_96.json

@@ -0,0 +1,29 @@
+{
+    "input_training_wav_list": "/cto_studio/lijingyi/recon/filelist.train",
+    "input_validation_wav_list": "/cto_studio/lijingyi/recon/filelist.val",
+    "test_input_wavs_dir":"/cto_studio/vistring/zhaozhiyuan/datasets/AudioSet/wavs/test",
+    "test_wav_output_dir":"cosmos96_output",
+
+    "batch_size": 64,
+    "learning_rate": 0.0004,
+    "adam_b1": 0.9,
+    "adam_b2": 0.999,
+    "lr_decay": 0.999,
+    "seed": 1234,
+    "training_epochs": 3100,
+    "stdout_interval":5,
+    "checkpoint_interval": 5000,
+    "summary_interval": 100,
+    "validation_interval": 5000,
+    "checkpoint_path": "cp_cosmos96_vq3",
+    "checkpoint_file_load_Encoder": "cp_cosmos96_vq3/encoder_01000000",
+    "checkpoint_file_load_Decoder": "cp_cosmos96_vq3/decoder_01000000",
+
+    "segment_size": 24320,
+    "num_mels": 96,
+    "n_fft": 1024,
+    "hop_length": 256,
+
+    "sampling_rate": 44100,
+    "num_workers": 4
+}

cp_8_vq3/logs/events.out.tfevents.1757242860.dgx056.scc.idea.1476461.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b65909a44d61d7f3fb99fbf30b1955be1f94f5ccbc32de1077b9c91276f4ab2
+size 11966985

cp_8_vq3/train_96.py

@@ -0,0 +1,227 @@
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+import itertools
+import os
+import time
+import argparse
+import json
+import torch
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+from torch.utils.data import DistributedSampler, DataLoader
+import torch.multiprocessing as mp
+from torch.distributed import init_process_group
+from torch.nn.parallel import DistributedDataParallel
+from models import amplitude_loss
+from dataset import Dataset, mel_spectrogram, get_dataset_filelist
+import cosmos_tokenizer
+from discrete_img import DiscreteImageTokenizer
+from utils import AttrDict, build_env, plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
+from vocos import Vocos
+import shutil
+torch.backends.cudnn.benchmark = True
+
+import torch.multiprocessing as mp
+mp.set_start_method("spawn", force=True)
+
+from CosmosTokenizer.cosmos_tokenizer.modules import DecoderType, DiscreteQuantizer, EncoderType
+
+params = dict(
+    attn_resolutions=[6, 12],
+    channels=128,
+    channels_mult=[2, 4, 4],
+    dropout=0.0,
+    in_channels=1,
+    spatial_compression=8,
+    num_res_blocks=2,
+    out_channels=1,
+    resolution=96,
+    patch_size=2,
+    patch_method="haar",
+    z_channels=256,
+    z_factor=2,
+    quantizer=DiscreteQuantizer.VQ.name,
+    embedding_dim=64,
+    num_embeddings=8192,
+    num_quantizers=1,
+    name="DI",
+    encoder=EncoderType.Default.name,
+    decoder=DecoderType.Default.name,
+)
+
+def train(h):
+
+    torch.cuda.manual_seed(h.seed)
+    device = torch.device('cuda:{:d}'.format(0))
+    model = DiscreteImageTokenizer(**params).to(device)
+
+    print("Model: ")
+    print(model)
+    os.makedirs(h.checkpoint_path, exist_ok=True)
+    print("checkpoints directory : ", h.checkpoint_path)
+
+    if os.path.isdir(h.checkpoint_path):
+        cp_model = scan_checkpoint(h.checkpoint_path, 'vae_')
+
+
+    steps = 0
+    if cp_model is None:
+        state_dict_vae = None
+        last_epoch = -1
+    else:
+        state_dict_vae = load_checkpoint(cp_model, device)
+        model.load_state_dict(state_dict_vae['encoder'])
+        steps = 0
+        last_epoch = -1
+
+    optim_g = torch.optim.AdamW(itertools.chain(model.parameters()), h.learning_rate, betas=[h.adam_b1, h.adam_b2])
+
+    if state_dict_vae is not None:
+        model.load_state_dict(state_dict_vae['encoder'])
+
+    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch)
+    
+    training_filelist, validation_filelist = get_dataset_filelist(h.input_training_wav_list, h.input_validation_wav_list)
+
+    trainset = Dataset(training_filelist, h.segment_size, h.n_fft, h.num_mels,
+                       h.hop_length, h.sampling_rate, shuffle=True, device=device, train=True)
+
+    train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False,
+                              sampler=None,
+                              batch_size=h.batch_size,
+                              pin_memory=True,
+                              drop_last=True)
+
+    validset = Dataset(validation_filelist, h.segment_size, h.n_fft, h.num_mels,
+                       h.hop_length, h.sampling_rate, shuffle=False, device=device, train=False)
+    
+    validation_loader = DataLoader(validset, num_workers=1, shuffle=False,
+                                   sampler=None,
+                                   batch_size=1,
+                                   pin_memory=True,
+                                   drop_last=True)
+
+    sw = SummaryWriter(os.path.join(h.checkpoint_path, 'logs'))
+
+    #model = model.to(dtype=torch.bfloat16)
+    model.train()
+
+    for epoch in range(max(0, last_epoch), h.training_epochs):
+
+        start = time.time()
+        print("Epoch: {}".format(epoch+1))
+
+        for i, batch in enumerate(train_loader):
+            start_b = time.time()
+            y_mel = batch
+            y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
+            #y_mel = y_mel.to(dtype=torch.bfloat16)
+            out_train = model(y_mel)
+            y_g_mel = out_train["reconstructions"]
+            # Generator
+            optim_g.zero_grad()
+            # Losses defined on log mel spectra
+            L_M = F.l1_loss(y_mel, y_g_mel)*5.0
+            Mel_L2_error = amplitude_loss(y_mel, y_g_mel)*25.0
+            quant_loss = out_train["quant_loss"].mean()
+            L_G =  L_M + quant_loss*0.25
+            L_G.backward()
+            optim_g.step()
+
+            # STDOUT logging
+            if steps % h.stdout_interval == 0:
+                with torch.no_grad():
+                    Mel_error = (F.l1_loss(y_mel, y_g_mel)*5.0).item()
+                    Mel_L2_error = (amplitude_loss(y_mel, y_g_mel)*25.0).item()
+                    quant_loss = quant_loss.item()
+
+                print('Steps : {:d}, Gen Loss Total : {:4.3f}, Mel Spectrogram Loss : {:4.3f}, Mel Spectrogram L2 Loss : {:4.3f}, Quant Loss : {:4.3f}, s/b : {:4.3f}'.
+                      format(steps, L_G,  Mel_error, Mel_L2_error, quant_loss, time.time() - start_b))
+
+            # checkpointing
+            if steps % h.checkpoint_interval == 0 and steps != 0:
+                checkpoint_path = "{}/vae_{:08d}".format(h.checkpoint_path, steps)
+                save_checkpoint(checkpoint_path,
+                                {'encoder': model.state_dict(),
+                                'steps': steps,
+                                 'epoch': epoch})
+
+            # Tensorboard summary logging
+            if steps % h.summary_interval == 0:
+                sw.add_scalar("Training/Generator_Total_Loss", L_G, steps)
+                sw.add_scalar("Training/Mel_Spectrogram_Loss", Mel_error, steps)
+
+            # Validation
+            if steps % h.validation_interval == 0:  # and steps != 0:
+                model.eval()
+                torch.cuda.empty_cache()
+                val_Mel_err_tot = 0
+                val_Mel_L2_err_tot = 0
+                with torch.no_grad():
+                    for j, batch in enumerate(validation_loader):
+                        y_mel = batch
+                        #y_mel = y_mel.to(dtype=torch.bfloat16)
+                        out_eval = model(y_mel.to(device))
+                        y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))                        
+                        val_Mel_err_tot += (F.l1_loss(y_mel, out_eval.reconstructions)*5.0).item()
+                        val_Mel_L2_err_tot += (amplitude_loss(y_mel, out_eval.reconstructions)*25.0).item()
+
+
+                        if j <= 4:
+                            if steps == 0:
+                                y_plot_tensor = y_mel[0, 0] * 5.0
+                                y_plot = y_plot_tensor.cpu().float().numpy()  # 再转 numpy
+                                sw.add_figure('gt/y_mel_{}'.format(j), plot_spectrogram(y_plot), steps)
+
+                            y_plot_tensor_g = y_g_mel[0, 0] * 5.0
+                            y_plot_g = y_plot_tensor_g.cpu().float().numpy()
+                            sw.add_figure('generated/y_g_mel_{}'.format(j), plot_spectrogram(y_plot_g), steps)
+
+                    val_Mel_err = val_Mel_err_tot / (j+1)
+                    val_Mel_L2_err = val_Mel_L2_err_tot / (j+1)
+                    sw.add_scalar("Validation/Mel_Spectrogram_loss", val_Mel_err, steps)
+                    sw.add_scalar("Validation/Mel_Spectrogram_L2_loss", val_Mel_L2_err, steps)
+
+                model.train()
+
+            steps += 1
+
+        scheduler_g.step()
+        
+        print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start)))
+
+
+def main():
+    print('Initializing Training Process..')
+
+    config_file = 'config_96.json'
+
+    with open(config_file) as f:
+        data = f.read()
+
+    json_config = json.loads(data)
+    h = AttrDict(json_config)
+    build_env(config_file, 'config_96.json', h.checkpoint_path)
+    
+    src = "train_96.py"
+    dst_dir = h.checkpoint_path
+    os.makedirs(dst_dir, exist_ok=True)
+    dst = os.path.join(dst_dir, "train_96.py")
+    if not os.path.exists(src):
+        raise FileNotFoundError(f"{src} 不存在！")
+    shutil.copyfile(src, dst)
+    print(f"已将 {src} 复制到 {dst}")
+    
+    
+    torch.manual_seed(h.seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(h.seed)
+    else:
+        pass
+
+    train(h)
+
+
+if __name__ == '__main__':
+    main()
+

cp_8_vq3/vae_00430000

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50e8b4e9da2208f25197a4a303afa4c340d45fe147f3c466b6490175d67c758f
+size 357630932