repaired auraloss loss calc during eval/val

data/config.yaml

@@ -1,49 +1,49 @@
 dataset:
-  training: [
+  training: []
-  ]
 
-  validation: [
+  validation: []
-  ]
   
   speaker_name_getter: "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
   
   use_hdf5: True
+  hdf5_flag: r
   validate: True
 
-  workers: 8
+  workers: 4
   cache: True
 
-  phones_range: [4, 128]
+  phones_range: [4, 256]
-  duration_range: [1.0, 12.0]
+  duration_range: [1.0, 16.0]
 
   random_utterance: 1.0
   max_prompts: 3
   prompt_duration: 3.0
 
+  sample_type: speaker
+
 models:
   _models:
   - name: "ar"
     size: "full"
     resp_levels: 1
-    arch_type: "retnet"
     prom_levels: 2
     tasks: 8
+    arch_type: "retnet"
 
   - name: "nar"
     size: "full"
     resp_levels: 1
-    arch_type: "retnet"
     prom_levels: 2
     tasks: 8
-  
+    arch_type: "retnet"
 
 hyperparameters:
-  batch_size: 8
+  batch_size: 16
-  gradient_accumulation_steps: 16
+  gradient_accumulation_steps: 2
   gradient_clipping: 100
   
   optimizer: Adamw
-  learning_rate: 1.0e-4
+  learning_rate: 1.0e-5
   
   scheduler_type: ""
   #scheduler_type: OneCycle
@@ -66,40 +66,42 @@ hyperparameters:
 
 evaluation:
   batch_size: 32
-  frequency: 250
+  frequency: 500
   size: 32
   
   steps: 300
   ar_temperature: 1.0
-  nar_temperature: 0.2
+  nar_temperature: 1.0
 
 trainer:
-  iterations: 100_000
+  iterations: 1_000_000
   
   save_tag: step
   save_on_oom: True
   save_on_quit: True
-  save_frequency: 100
+  save_frequency: 1000
+
+  keep_last_checkpoints: 4
 
   aggressive_optimizations: False
 
-  #load_tag: "9500"
   #load_state_dict: True
-  #load_states: False
   #strict_loading: False
+  #load_tag: "9500"
+  #load_states: False
   #restart_step_count: True
   
   gc_mode: None # "global_step"
 
-  weight_dtype: bfloat16 # float16, float32
+  weight_dtype: bfloat16
 
   backend: deepspeed
   deepspeed:
-    zero_optimization_level: 2
+    zero_optimization_level: 0
     use_compression_training: True
 
 inference:
   use_vocos: True
 
 bitsandbytes:
-  enabled: false
+  enabled: false

vall_e/train.py

@@ -20,15 +20,6 @@ from collections import defaultdict
 from tqdm import tqdm
 
 mel_stft_loss = auraloss.freq.MelSTFTLoss(24_000, device="cpu")
-
-def center_crop(x, len):
-	start = (x.shape[-1] - len) // 2
-	stop = start + len
-	return x[..., start:stop]
-
-def left_crop(x, len):
-	return x[..., 0:len]
-
 _logger = logging.getLogger(__name__)
 
 def train_feeder(engine, batch):
@@ -87,17 +78,18 @@ def run_eval(engines, eval_name, dl):
 
 			# pseudo loss calculation since we don't get the logits during eval
 			min_length = min( ref_audio.shape[-1], hyp_audio.shape[-1] )
-			ref_audio = center_crop(ref_audio, min_length) #ref_audio[..., 0:min_length]
+			ref_audio = ref_audio[..., 0:min_length]
-			hyp_audio = center_crop(hyp_audio, min_length) #hyp_audio[..., 0:min_length]
+			hyp_audio = hyp_audio[..., 0:min_length]
 			try:
-				stats['loss'].append(mel_stft_loss(hyp_audio, ref_audio).item())
+				stats['loss'].append(mel_stft_loss(hyp_audio[None, :, :], ref_audio[None, :, :]).item())
 			except Exception as e:
 				stats['loss'].append(0)
 				print(traceback.format_exc())
 	
 	processed = 0
-	for batch in tqdm(dl):
+	while processed < cfg.evaluation.size:
-		batch: dict = to_device(batch, cfg.device)
+		batch: dict = to_device(next(iter(dl)), cfg.device)
+		processed += len(batch["text"])
 
 		# if we're training both models, provide output for both
 		if AR is not None and NAR is not None:
@@ -132,9 +124,6 @@ def run_eval(engines, eval_name, dl):
 
 				process( name, batch, resps_list )
 
-		processed += len(batch["text"])
-		if processed >= cfg.evaluation.size:
-			break
 
 	stats = {k: sum(v) / len(v) for k, v in stats.items()}
 	engines_stats.update(flatten_dict({ name: stats }))