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Running
on
Zero
File size: 10,053 Bytes
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import os
from typing import List, Tuple
import torch
from loguru import logger
from .models import AudioSample
from .preprocess_audio import load_audio_stereo
from .preprocess_context import build_context_latents
from .preprocess_encoder import run_encoder
from .preprocess_lyrics import encode_lyrics
from .preprocess_manifest import save_manifest
from .preprocess_text import build_text_prompt, encode_text
from .preprocess_utils import select_genre_indices
from .preprocess_vae import vae_encode
from acestep.debug_utils import (
debug_log_for,
debug_log_verbose_for,
debug_start_verbose_for,
debug_end_verbose_for,
)
class PreprocessMixin:
"""Preprocess labeled samples to tensor files."""
def preprocess_to_tensors(
self,
dit_handler,
output_dir: str,
max_duration: float = 240.0,
progress_callback=None,
) -> Tuple[List[str], str]:
"""Preprocess all labeled samples to tensor files for efficient training."""
debug_log_for("dataset", f"preprocess_to_tensors: output_dir='{output_dir}', max_duration={max_duration}")
if not self.samples:
return [], "❌ No samples to preprocess"
labeled_samples = [s for s in self.samples if s.labeled]
if not labeled_samples:
return [], "❌ No labeled samples to preprocess"
if dit_handler is None or dit_handler.model is None:
return [], "❌ Model not initialized. Please initialize the service first."
os.makedirs(output_dir, exist_ok=True)
output_paths: List[str] = []
success_count = 0
fail_count = 0
model = dit_handler.model
vae = dit_handler.vae
text_encoder = dit_handler.text_encoder
text_tokenizer = dit_handler.text_tokenizer
silence_latent = dit_handler.silence_latent
device = dit_handler.device
dtype = dit_handler.dtype
target_sample_rate = 48000
genre_indices = select_genre_indices(labeled_samples, self.metadata.genre_ratio)
debug_log_verbose_for("dataset", f"selected genre indices: count={len(genre_indices)}")
for i, sample in enumerate(labeled_samples):
try:
debug_log_verbose_for("dataset", f"sample[{i}] id={sample.id} file={sample.filename}")
if progress_callback:
progress_callback(f"Preprocessing {i+1}/{len(labeled_samples)}: {sample.filename}")
use_genre = i in genre_indices
t0 = debug_start_verbose_for("dataset", f"load_audio_stereo[{i}]")
audio, _ = load_audio_stereo(sample.audio_path, target_sample_rate, max_duration)
debug_end_verbose_for("dataset", f"load_audio_stereo[{i}]", t0)
debug_log_verbose_for("dataset", f"audio shape={tuple(audio.shape)} dtype={audio.dtype}")
audio = audio.unsqueeze(0).to(device).to(vae.dtype)
debug_log_verbose_for(
"dataset",
f"vae device={next(vae.parameters()).device} vae dtype={vae.dtype} "
f"audio device={audio.device} audio dtype={audio.dtype}",
)
with torch.no_grad():
t0 = debug_start_verbose_for("dataset", f"vae_encode[{i}]")
target_latents = vae_encode(vae, audio, dtype)
debug_end_verbose_for("dataset", f"vae_encode[{i}]", t0)
latent_length = target_latents.shape[1]
attention_mask = torch.ones(1, latent_length, device=device, dtype=dtype)
debug_log_verbose_for(
"dataset",
f"target_latents shape={tuple(target_latents.shape)} latent_length={latent_length}",
)
caption = sample.get_training_prompt(self.metadata.tag_position, use_genre=use_genre)
text_prompt = build_text_prompt(sample, self.metadata.tag_position, use_genre)
if i == 0:
logger.info(f"\n{'='*70}")
logger.info("🔍 [DEBUG] DiT TEXT ENCODER INPUT (Training Preprocess)")
logger.info(f"{'='*70}")
logger.info(f"text_prompt:\n{text_prompt}")
logger.info(f"{'='*70}\n")
t0 = debug_start_verbose_for("dataset", f"encode_text[{i}]")
text_hidden_states, text_attention_mask = encode_text(
text_encoder, text_tokenizer, text_prompt, device, dtype
)
debug_end_verbose_for("dataset", f"encode_text[{i}]", t0)
debug_log_verbose_for(
"dataset",
f"text_hidden_states shape={tuple(text_hidden_states.shape)} "
f"text_attention_mask shape={tuple(text_attention_mask.shape)}",
)
lyrics = sample.lyrics if sample.lyrics else "[Instrumental]"
t0 = debug_start_verbose_for("dataset", f"encode_lyrics[{i}]")
lyric_hidden_states, lyric_attention_mask = encode_lyrics(
text_encoder, text_tokenizer, lyrics, device, dtype
)
debug_end_verbose_for("dataset", f"encode_lyrics[{i}]", t0)
debug_log_verbose_for(
"dataset",
f"lyric_hidden_states shape={tuple(lyric_hidden_states.shape)} "
f"lyric_attention_mask shape={tuple(lyric_attention_mask.shape)}",
)
t0 = debug_start_verbose_for("dataset", f"run_encoder[{i}]")
# Ensure DiT encoder runs on the active residency device (GPU when loaded via
# offload context). This prevents flash-attn CPU backend crashes.
with dit_handler._load_model_context("model"):
model_device = next(model.parameters()).device
model_dtype = next(model.parameters()).dtype
if text_hidden_states.device != model_device:
text_hidden_states = text_hidden_states.to(model_device)
if text_attention_mask.device != model_device:
text_attention_mask = text_attention_mask.to(model_device)
if lyric_hidden_states.device != model_device:
lyric_hidden_states = lyric_hidden_states.to(model_device)
if lyric_attention_mask.device != model_device:
lyric_attention_mask = lyric_attention_mask.to(model_device)
if text_hidden_states.dtype != model_dtype:
text_hidden_states = text_hidden_states.to(model_dtype)
if lyric_hidden_states.dtype != model_dtype:
lyric_hidden_states = lyric_hidden_states.to(model_dtype)
encoder_hidden_states, encoder_attention_mask = run_encoder(
model,
text_hidden_states=text_hidden_states,
text_attention_mask=text_attention_mask,
lyric_hidden_states=lyric_hidden_states,
lyric_attention_mask=lyric_attention_mask,
device=model_device,
dtype=model_dtype,
)
debug_end_verbose_for("dataset", f"run_encoder[{i}]", t0)
debug_log_verbose_for(
"dataset",
f"encoder_hidden_states shape={tuple(encoder_hidden_states.shape)} "
f"encoder_attention_mask shape={tuple(encoder_attention_mask.shape)}",
)
t0 = debug_start_verbose_for("dataset", f"build_context_latents[{i}]")
context_latents = build_context_latents(silence_latent, latent_length, device, dtype)
debug_end_verbose_for("dataset", f"build_context_latents[{i}]", t0)
output_data = {
"target_latents": target_latents.squeeze(0).cpu(),
"attention_mask": attention_mask.squeeze(0).cpu(),
"encoder_hidden_states": encoder_hidden_states.squeeze(0).cpu(),
"encoder_attention_mask": encoder_attention_mask.squeeze(0).cpu(),
"context_latents": context_latents.squeeze(0).cpu(),
"metadata": {
"audio_path": sample.audio_path,
"filename": sample.filename,
"caption": caption,
"lyrics": lyrics,
"duration": sample.duration,
"bpm": sample.bpm,
"keyscale": sample.keyscale,
"timesignature": sample.timesignature,
"language": sample.language,
"is_instrumental": sample.is_instrumental,
},
}
output_path = os.path.join(output_dir, f"{sample.id}.pt")
t0 = debug_start_verbose_for("dataset", f"torch.save[{i}]")
torch.save(output_data, output_path)
debug_end_verbose_for("dataset", f"torch.save[{i}]", t0)
output_paths.append(output_path)
success_count += 1
except Exception as e:
logger.exception(f"Error preprocessing {sample.filename}")
fail_count += 1
if progress_callback:
progress_callback(f"❌ Failed: {sample.filename}: {str(e)}")
t0 = debug_start_verbose_for("dataset", "save_manifest")
save_manifest(output_dir, self.metadata, output_paths)
debug_end_verbose_for("dataset", "save_manifest", t0)
status = f"✅ Preprocessed {success_count}/{len(labeled_samples)} samples to {output_dir}"
if fail_count > 0:
status += f" ({fail_count} failed)"
return output_paths, status
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