import torch, os, argparse, accelerate, warnings, torchaudio import math from diffsynth.core import UnifiedDataset from diffsynth.core.data.operators import ToAbsolutePath, RouteByType, DataProcessingOperator, 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, model_paths=None, model_id_with_origin_paths=None, tokenizer_path=None, silence_latent_path=None, trainable_models=None, lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, preset_lora_path=None, preset_lora_model=None, use_gradient_checkpointing=True, use_gradient_checkpointing_offload=False, extra_inputs=None, fp8_models=None, offload_models=None, device="cpu", 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 = 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, preset_lora_path, preset_lora_model, 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), "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), } def get_pipeline_inputs(self, data): 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, "bpm": data.get("bpm", 100), "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: inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) loss = self.task_to_loss[self.task](self.pipe, *inputs) return loss def ace_step_parser(): parser = argparse.ArgumentParser(description="ACE-Step training.") parser = add_general_config(parser) parser.add_argument("--tokenizer_path", type=str, default=None, help="Tokenizer path in format model_id:origin_pattern.") parser.add_argument("--silence_latent_path", type=str, default=None, help="Silence latent path in format model_id:origin_pattern.") parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.") return 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, repeat=args.dataset_repeat, 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), }, ) # ===== TrainingModule ===== model = AceStepTrainingModule( model_paths=args.model_paths, model_id_with_origin_paths=args.model_id_with_origin_paths, tokenizer_path=args.tokenizer_path, silence_latent_path=args.silence_latent_path, trainable_models=args.trainable_models, lora_base_model=args.lora_base_model, lora_target_modules=args.lora_target_modules, lora_rank=args.lora_rank, lora_checkpoint=args.lora_checkpoint, preset_lora_path=args.preset_lora_path, preset_lora_model=args.preset_lora_model, use_gradient_checkpointing=args.use_gradient_checkpointing, use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, extra_inputs=args.extra_inputs, fp8_models=args.fp8_models, offload_models=args.offload_models, 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, "sft:train": launch_training_task, } launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)