| import click
|
| import os
|
| import json
|
| from acestep.pipeline_ace_step import ACEStepPipeline
|
| from acestep.data_sampler import DataSampler
|
|
|
|
|
| def sample_data(json_data):
|
| return (
|
| json_data["audio_duration"],
|
| json_data["prompt"],
|
| json_data["lyrics"],
|
| json_data["infer_step"],
|
| json_data["guidance_scale"],
|
| json_data["scheduler_type"],
|
| json_data["cfg_type"],
|
| json_data["omega_scale"],
|
| ", ".join(map(str, json_data["actual_seeds"])),
|
| json_data["guidance_interval"],
|
| json_data["guidance_interval_decay"],
|
| json_data["min_guidance_scale"],
|
| json_data["use_erg_tag"],
|
| json_data["use_erg_lyric"],
|
| json_data["use_erg_diffusion"],
|
| ", ".join(map(str, json_data["oss_steps"])),
|
| json_data["guidance_scale_text"] if "guidance_scale_text" in json_data else 0.0,
|
| (
|
| json_data["guidance_scale_lyric"]
|
| if "guidance_scale_lyric" in json_data
|
| else 0.0
|
| ),
|
| )
|
|
|
|
|
| @click.command()
|
| @click.option(
|
| "--checkpoint_path", type=str, default="", help="Path to the checkpoint directory"
|
| )
|
| @click.option("--bf16", type=bool, default=True, help="Whether to use bfloat16")
|
| @click.option(
|
| "--torch_compile", type=bool, default=False, help="Whether to use torch compile"
|
| )
|
| @click.option(
|
| "--cpu_offload", type=bool, default=False, help="Whether to use CPU offloading (only load current stage's model to GPU)"
|
| )
|
| @click.option(
|
| "--overlapped_decode", type=bool, default=False, help="Whether to use overlapped decoding (run dcae and vocoder using sliding windows)"
|
| )
|
| @click.option("--device_id", type=int, default=0, help="Device ID to use")
|
| @click.option("--output_path", type=str, default=None, help="Path to save the output")
|
| def main(checkpoint_path, bf16, torch_compile, cpu_offload, overlapped_decode, device_id, output_path):
|
|
|
|
|
| model_demo = ACEStepPipeline(
|
| checkpoint_dir=checkpoint_path,
|
| dtype="bfloat16" if bf16 else "float32",
|
| torch_compile=torch_compile,
|
| cpu_offload=cpu_offload,
|
| overlapped_decode=overlapped_decode
|
| )
|
| print(model_demo)
|
|
|
| data_sampler = DataSampler()
|
|
|
| inputs_dir = "./data/inputs"
|
| for id, name in enumerate(os.listdir(inputs_dir)):
|
| if not name.startswith("test"):
|
| continue
|
| path = os.path.join(inputs_dir, name)
|
| with open(path, 'r') as file:
|
| json_data = json.load(file)
|
| json_data = sample_data(json_data)
|
|
|
| pure_name = os.path.splitext(name)[0]
|
| output_path = f"./data/outputs/{pure_name}.wav"
|
| if os.path.exists(output_path):
|
| continue
|
| (
|
| audio_duration,
|
| prompt,
|
| lyrics,
|
| infer_step,
|
| guidance_scale,
|
| scheduler_type,
|
| cfg_type,
|
| omega_scale,
|
| manual_seeds,
|
| guidance_interval,
|
| guidance_interval_decay,
|
| min_guidance_scale,
|
| use_erg_tag,
|
| use_erg_lyric,
|
| use_erg_diffusion,
|
| oss_steps,
|
| guidance_scale_text,
|
| guidance_scale_lyric,
|
| ) = json_data
|
|
|
| model_demo(
|
| audio_duration=audio_duration,
|
| prompt=prompt,
|
| lyrics=lyrics,
|
| infer_step=infer_step,
|
| guidance_scale=guidance_scale,
|
| scheduler_type=scheduler_type,
|
| cfg_type=cfg_type,
|
| omega_scale=omega_scale,
|
| manual_seeds=manual_seeds,
|
| guidance_interval=guidance_interval,
|
| guidance_interval_decay=guidance_interval_decay,
|
| min_guidance_scale=min_guidance_scale,
|
| use_erg_tag=use_erg_tag,
|
| use_erg_lyric=use_erg_lyric,
|
| use_erg_diffusion=use_erg_diffusion,
|
| oss_steps=oss_steps,
|
| guidance_scale_text=guidance_scale_text,
|
| guidance_scale_lyric=guidance_scale_lyric,
|
| save_path=output_path,
|
| )
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
