Update wavtokenizer_mediumdata_music_audio_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml

wavtokenizer_mediumdata_music_audio_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml

@@ -1,24 +1,24 @@
 seed_everything: 3407
 
 data:
-  class_path: vocos.dataset.VocosDataModule
+  class_path: decoder.dataset.VocosDataModule
   init_args:
     train_params:
-      filelist_path: /cpfs_speech/jishengpeng/Code/dataprocess/path/WavTokenizer/medium_train_audio_music
+      filelist_path: ./WavTokenizer/medium_train_audio_music
       sampling_rate: 24000
       num_samples: 72000
       batch_size: 39  #18
       num_workers: 8
 
     val_params:
-      filelist_path: /cpfs_speech/jishengpeng/Code/dataprocess/path/WavTokenizer/medium_test_audio_music
+      filelist_path: ./WavTokenizer/medium_test_audio_music
       sampling_rate: 24000
       num_samples: 72000
       batch_size: 2   # 10
       num_workers: 8
 
 model:
-  class_path: vocos.experiment.VocosEncodecExp
+  class_path: decoder.experiment.VocosEncodecExp
   init_args:
     sample_rate: 24000
     initial_learning_rate: 2e-4
@@ -32,12 +32,12 @@ model:
     evaluate_pesq: true
     evaluate_periodicty: true
 
-    resume: true
+    resume: false
     resume_config: /cpfs_speech/jishengpeng/Code/WavTokenizer/configs/wavtokenizer_smalldata_frame75_3s_nq1_code16384_dim512_kmeans800_attn.yaml
-    resume_model: /cpfs_speech/jishengpeng/Code/WavTokenizer/result/train/wavtokenizer_mediumdata_music_audio_frame75_3s_nq1_code4096_dim512_kmeans200_attn/lightning_logs/version_2/checkpoints/vocos_checkpoint_epoch=1_step=45240_val_loss=8.8358.ckpt
+    resume_model: /cpfs_speech/jishengpeng/Code/WavTokenizer/result/train/wavtokenizer_mediumdata_music_audio_frame75_3s_nq1_code4096_dim512_kmeans200_attn/lightning_logs/version_2/checkpoints/example.ckpt
 
     feature_extractor:
-      class_path: vocos.feature_extractors.EncodecFeatures
+      class_path: decoder.feature_extractors.EncodecFeatures
       init_args:
         encodec_model: encodec_24khz
         bandwidths: [6.6, 6.6, 6.6, 6.6]
@@ -48,7 +48,7 @@ model:
         vq_kmeans: 200
 
     backbone:
-      class_path: vocos.models.VocosBackbone
+      class_path: decoder.models.VocosBackbone
       init_args:
         input_channels: 512
         dim: 768
@@ -57,18 +57,18 @@ model:
         adanorm_num_embeddings: 4  # len(bandwidths)
 
     head:
-      class_path: vocos.heads.ISTFTHead
+      class_path: decoder.heads.ISTFTHead
       init_args:
         dim: 768
-        n_fft: 1280  #4*hop_length
-        hop_length: 320  # 8*5*4*2
+        n_fft: 1280  
+        hop_length: 320  
         padding: same
 
 trainer:
   logger:
     class_path: pytorch_lightning.loggers.TensorBoardLogger
     init_args:
-      save_dir: /cpfs_speech/jishengpeng/Code/WavTokenizer/result/train/wavtokenizer_mediumdata_music_audio_frame75_3s_nq1_code4096_dim512_kmeans200_attn/
+      save_dir: ./WavTokenizer/result/train/wavtokenizer_mediumdata_music_audio_frame75_3s_nq1_code4096_dim512_kmeans200_attn/
   callbacks:
     - class_path: pytorch_lightning.callbacks.LearningRateMonitor
     - class_path: pytorch_lightning.callbacks.ModelSummary
@@ -77,10 +77,10 @@ trainer:
     - class_path: pytorch_lightning.callbacks.ModelCheckpoint
       init_args:
         monitor: val_loss
-        filename: vocos_checkpoint_{epoch}_{step}_{val_loss:.4f}
+        filename: wavtokenizer_checkpoint_{epoch}_{step}_{val_loss:.4f}
         save_top_k: 10
         save_last: true
-    - class_path: vocos.helpers.GradNormCallback
+    - class_path: decoder.helpers.GradNormCallback
 
   # Lightning calculates max_steps across all optimizer steps (rather than number of batches)
   # This equals to 1M steps per generator and 1M per discriminator