style

examples/ace_step/model_training/train.py

@@ -1,31 +1,15 @@
-import torch, os, argparse, accelerate, warnings, torchaudio
+import os
+import torch
 import math
+import argparse
+import accelerate
 from diffsynth.core import UnifiedDataset
-from diffsynth.core.data.operators import ToAbsolutePath, RouteByType, DataProcessingOperator, LoadPureAudioWithTorchaudio
+from diffsynth.core.data.operators import ToAbsolutePath, LoadPureAudioWithTorchaudio
 from diffsynth.pipelines.ace_step import AceStepPipeline, ModelConfig
 from diffsynth.diffusion import *
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
-class LoadAceStepAudio(DataProcessingOperator):
-    """Load audio file and return waveform tensor [2, T] at 48kHz."""
-    def __init__(self, target_sr=48000):
-        self.target_sr = target_sr
-
-    def __call__(self, data: str):
-        try:
-            waveform, sample_rate = torchaudio.load(data)
-            if sample_rate != self.target_sr:
-                resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=self.target_sr)
-                waveform = resampler(waveform)
-            if waveform.shape[0] == 1:
-                waveform = waveform.repeat(2, 1)
-            return waveform
-        except Exception as e:
-            warnings.warn(f"Cannot load audio from {data}: {e}")
-            return None
-
-
 class AceStepTrainingModule(DiffusionTrainingModule):
     def __init__(
         self,
@@ -43,17 +27,15 @@ class AceStepTrainingModule(DiffusionTrainingModule):
         task="sft",
     ):
         super().__init__()
-        # ===== 解析模型配置（固定写法） =====
         model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
-        # ===== Tokenizer 配置 =====
         text_tokenizer_config = self.parse_path_or_model_id(tokenizer_path, default_value=ModelConfig(model_id="ACE-Step/Ace-Step1.5", origin_file_pattern="Qwen3-Embedding-0.6B/"))
         silence_latent_config = self.parse_path_or_model_id(silence_latent_path, default_value=ModelConfig(model_id="ACE-Step/Ace-Step1.5", origin_file_pattern="acestep-v15-turbo/silence_latent.pt"))
-        # ===== 构建 Pipeline =====
-        self.pipe = AceStepPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, text_tokenizer_config=text_tokenizer_config, silence_latent_config=silence_latent_config)
-        # ===== 拆分 Pipeline Units（固定写法） =====
+        self.pipe = AceStepPipeline.from_pretrained(
+            torch_dtype=torch.bfloat16, device=device, model_configs=model_configs,
+            text_tokenizer_config=text_tokenizer_config, silence_latent_config=silence_latent_config,
+        )
         self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
 
-        # ===== 切换到训练模式（固定写法） =====
         self.switch_pipe_to_training_mode(
             self.pipe, trainable_models,
             lora_base_model, lora_target_modules, lora_rank, lora_checkpoint,
@@ -61,13 +43,11 @@ class AceStepTrainingModule(DiffusionTrainingModule):
             task=task,
         )
 
-        # ===== 其他配置（固定写法） =====
         self.use_gradient_checkpointing = use_gradient_checkpointing
         self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
         self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
         self.fp8_models = fp8_models
         self.task = task
-        # ===== 任务模式路由（固定写法） =====
         self.task_to_loss = {
             "sft:data_process": lambda pipe, *args: args,
             "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
@@ -78,11 +58,8 @@ class AceStepTrainingModule(DiffusionTrainingModule):
         inputs_posi = {"prompt": data["prompt"], "positive": True}
         inputs_nega = {"positive": False}
         duration = math.floor(data['audio'][0].shape[1] / data['audio'][1]) if data.get("audio") is not None else data.get("duration", 60)
-        # ===== 共享参数 =====
         inputs_shared = {
-            # ===== 核心字段映射 =====
             "input_audio": data["audio"],
-            # ===== 音频生成任务所需元数据 =====
             "lyrics": data["lyrics"],
             "task_type": "text2music",
             "duration": duration,
@@ -90,18 +67,15 @@ class AceStepTrainingModule(DiffusionTrainingModule):
             "keyscale": data.get("keyscale", "C major"),
             "timesignature": data.get("timesignature", "4"),
             "vocal_language": data.get("vocal_language", "unknown"),
-            # ===== 框架控制参数（固定写法） =====
             "cfg_scale": 1,
             "rand_device": self.pipe.device,
             "use_gradient_checkpointing": self.use_gradient_checkpointing,
             "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
         }
-        # ===== 额外字段注入：通过 --extra_inputs 配置的数据集列名（固定写法） =====
         inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
         return inputs_shared, inputs_posi, inputs_nega
 
     def forward(self, data, inputs=None):
-        # ===== 标准实现，不要修改（固定写法） =====
         if inputs is None: inputs = self.get_pipeline_inputs(data)
         inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
         for unit in self.pipe.units:
@@ -122,12 +96,10 @@ def ace_step_parser():
 if __name__ == "__main__":
     parser = ace_step_parser()
     args = parser.parse_args()
-    # ===== Accelerator 配置（固定写法） =====
     accelerator = accelerate.Accelerator(
         gradient_accumulation_steps=args.gradient_accumulation_steps,
         kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)],
     )
-    # ===== 数据集定义 =====
     dataset = UnifiedDataset(
         base_path=args.dataset_base_path,
         metadata_path=args.dataset_metadata_path,
@@ -135,10 +107,11 @@ if __name__ == "__main__":
         data_file_keys=args.data_file_keys.split(","),
         main_data_operator=None,
         special_operator_map={
-            "audio": ToAbsolutePath(args.dataset_base_path) >> LoadPureAudioWithTorchaudio(target_sample_rate=48000),
+            "audio": ToAbsolutePath(args.dataset_base_path) >> LoadPureAudioWithTorchaudio(
+                target_sample_rate=48000,
+            ),
         },
     )
-    # ===== TrainingModule =====
     model = AceStepTrainingModule(
         model_paths=args.model_paths,
         model_id_with_origin_paths=args.model_id_with_origin_paths,
@@ -159,12 +132,10 @@ if __name__ == "__main__":
         task=args.task,
         device="cpu" if args.initialize_model_on_cpu else accelerator.device,
     )
-    # ===== ModelLogger（固定写法） =====
     model_logger = ModelLogger(
         args.output_path,
         remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
     )
-    # ===== 任务路由（固定写法） =====
     launcher_map = {
         "sft:data_process": launch_data_process_task,
         "sft": launch_training_task,