build_dataset.py

import os
import subprocess
import glob
import sys
from tqdm import tqdm

# --- 配置路径 ---
# build_dataset.py 脚本所在的目录
# 你需要根据实际情况填写 build_dataset.py 的路径
BUILD_SCRIPT_DIR = r"D:\DiffSingerDatasets\MakeDiffSinger\acoustic_forced_alignment"
BUILD_SCRIPT_PATH = os.path.join(BUILD_SCRIPT_DIR, "build_dataset.py")

# add_ph_num.py 脚本的完整路径
# *** 请将这里修改为正确的 add_ph_num.py 路径 ***
ADD_SCRIPT_PATH = r"D:\DiffSingerDatasets\MakeDiffSinger\variance-temp-solution\add_ph_num.py"

# batch_infer.py 脚本的完整路径
BATCH_INFER_SCRIPT_PATH = r"D:\DiffSingerDatasets\MakeDiffSinger\SOME\batch_infer.py"

# 已处理的歌手数据根目录 (包含 wav/ 和 TextGrid/)
SOURCE_PROCESSED_ROOT = r"D:\DiffSingerDatasets\m4singer_processed"

# 输出数据集的根目录
OUTPUT_DATASET_ROOT = r"D:\DiffSingerDatasets\m4singer_dataset"

# 字典文件路径 (用于 add_ph_num.py)
DICTIONARY_PATH = r"D:\DiffSingerDatasets\SOFA\dictionary\opencpop-extension.txt"

# 预训练模型 checkpoint 文件路径 (用于 batch_infer.py)
MODEL_CKPT_PATH = r"D:\DiffSingerDatasets\MakeDiffSinger\SOME\pretrained\0119_continuous256_5spk\model_ckpt_steps_100000_simplified.ckpt"

# 需要处理的 CSV 文件名 (build_dataset.py 生成， add_ph_num.py 修改)
CSV_FILENAME = "transcriptions.csv"

# 获取当前正在运行的 Python 解释器的完整路径
PYTHON_EXECUTABLE = sys.executable
print(f"Using Python executable: {PYTHON_EXECUTABLE}")
print("-" * 50) # Use a longer separator

# --- 辅助函数：运行外部命令 ---
def _run_command(command, description, cwd=None):
    """Helper function to run a subprocess command and print output."""
    print(f"\nRunning: {description}")
    print(f"Command: {' '.join(command)}")
    if cwd:
        print(f"Working Directory: {cwd}")
    print("-" * 20) # Separator before command output

    try:
        # Execute the command
        result = subprocess.run(command, capture_output=True, text=True, check=False, cwd=cwd)

        # Print output
        if result.stdout:
            print("--- STDOUT ---")
            print(result.stdout)
        if result.stderr:
            print("--- STDERR ---")
            print(result.stderr)

        # Check return code
        if result.returncode != 0:
            print(f"!!! Command failed: {description} exited with code {result.returncode}")
            return False, result.returncode
        else:
            print(f"--- Command successful: {description} ---")
            return True, result.returncode

    except FileNotFoundError:
        print(f"!!! Error: Script or executable not found: {command[0]}")
        print("Please check if the script paths and Python executable path are correct.")
        return False, -1 # Indicate script not found
    except Exception as e:
        print(f"!!! An unexpected error occurred while running {description}: {e}")
        return False, -2 # Indicate other general error

# --- 主处理函数：处理单个歌手 ---
def process_single_singer(singer_dir, output_root, dictionary_path, model_ckpt_path, python_exec_path):
    """
    为单个歌手目录执行完整的建库流程。

    Args:
        singer_dir (str): 歌手的源数据目录 (e.g., D:\DiffSingerDatasets\m4singer_processed\Alto-1)
        output_root (str): 输出数据集的根目录 (e.g., D:\DiffSingerDatasets\m4singer_dataset)
        dictionary_path (str): 字典文件路径
        model_ckpt_path (str): 模型 checkpoint 文件路径
        python_exec_path (str): 用于执行外部脚本的 Python 解释器路径
    """
    singer_name = os.path.basename(singer_dir) # 获取目录名作为歌手名 (e.g., Alto-1)
    source_wav_dir = os.path.join(singer_dir, "wav")
    source_tg_dir = os.path.join(singer_dir, "TextGrid")

    # 构建输出数据集的目录名，遵循 {声部}={编号}-MAN 格式
    # singer_name 已经是 Alto-1, Bass-2 等格式，所以直接加上 -MAN 即可
    target_dataset_name = f"{singer_name}-MAN"
    target_dataset_path = os.path.join(output_root, target_dataset_name)

    print(f"\n{'='*60}") # Major separator for each singer
    print(f"=== Processing Singer: {singer_name} ===")
    print(f"  Source Wavs: {source_wav_dir}")
    print(f"  Source TextGrids: {source_tg_dir}")
    print(f"  Target Dataset Dir: {target_dataset_path}")
    print(f"{'='*60}\n")

    # 1. 执行 build_dataset.py
    print("\n--- Step 1: Running build_dataset.py ---")
    build_command = [
        python_exec_path,
        BUILD_SCRIPT_PATH,
        "--wavs", source_wav_dir,
        "--tg", source_tg_dir,
        "--dataset", target_dataset_path
    ]
    success, _ = _run_command(build_command, "build_dataset.py")

    if not success:
        print(f"\n!!! Skipping add_ph_num.py and batch_infer.py for {singer_name} due to build_dataset.py failure.")
        return # Stop processing this singer

    # 2. 执行 add_ph_num.py
    print("\n--- Step 2: Running add_ph_num.py ---")
    csv_path = os.path.join(target_dataset_path, CSV_FILENAME)
    if not os.path.exists(csv_path):
        print(f"\n!!! Error: {CSV_FILENAME} not found at {csv_path} after build_dataset.py. Skipping add_ph_num.py and batch_infer.py for {singer_name}.")
        return # Stop processing this singer

    add_command = [
        python_exec_path,
        ADD_SCRIPT_PATH,
        csv_path, # The script expects the CSV path as the first positional argument
        "--dictionary", dictionary_path
    ]
    success, _ = _run_command(add_command, "add_ph_num.py")

    if not success:
        print(f"\n!!! Skipping batch_infer.py for {singer_name} due to add_ph_num.py failure.")
        return # Stop processing this singer

    # 3. 执行 batch_infer.py
    print("\n--- Step 3: Running batch_infer.py (Pitch Inference) ---")
    # Check if model checkpoint exists before attempting inference
    if not os.path.exists(model_ckpt_path):
         print(f"\n!!! Error: Model checkpoint not found at {model_ckpt_path}. Skipping batch_infer.py for {singer_name}.")
         return # Stop processing this singer

    infer_command = [
        python_exec_path,
        BATCH_INFER_SCRIPT_PATH,
        "--model", model_ckpt_path,
        "--dataset", target_dataset_path, # Pass the dataset directory containing updated CSV
        "--overwrite"
    ]
    success, _ = _run_command(infer_command, "batch_infer.py")

    if not success:
        print(f"\n!!! Pitch inference failed for {singer_name}.")
    else:
        print(f"\n=== Successfully processed all steps for Singer: {singer_name} ===")


if __name__ == "__main__":
    # 检查所有必要的脚本和文件是否存在
    if not os.path.exists(BUILD_SCRIPT_PATH):
        print(f"Error: build_dataset.py not found at {BUILD_SCRIPT_PATH}. Exiting.")
        sys.exit(1)
    if not os.path.exists(ADD_SCRIPT_PATH):
        print(f"Error: add_ph_num.py not found at {ADD_SCRIPT_PATH}. Exiting.")
        sys.exit(1)
    if not os.path.exists(BATCH_INFER_SCRIPT_PATH):
        print(f"Error: batch_infer.py not found at {BATCH_INFER_SCRIPT_PATH}. Exiting.")
        sys.exit(1)
    if not os.path.exists(DICTIONARY_PATH):
        print(f"Error: Dictionary file not found at {DICTIONARY_PATH}. Exiting.")
        sys.exit(1)
    # Model checkpoint check is done within the processing function as it's the last step
    # But a warning upfront might be useful
    if not os.path.exists(MODEL_CKPT_PATH):
        print(f"Warning: Model checkpoint not found at {MODEL_CKPT_PATH}. Pitch inference step will be skipped for all singers.")


    # 查找所有歌手目录
    singer_directories = glob.glob(os.path.join(SOURCE_PROCESSED_ROOT, "*"))

    # 过滤出确实是目录的项
    singer_directories = [d for d in singer_directories if os.path.isdir(d)]

    if not singer_directories:
        print(f"No singer directories found in {SOURCE_PROCESSED_ROOT}. Please check the path.")
    else:
        print(f"Found {len(singer_directories)} singer directories to process.")
        print("-" * 50)

        # 使用 tqdm 包装循环以显示进度条
        # Leave=True so the final bar remains after completion, showing total count
        for singer_dir in tqdm(singer_directories, desc="Overall Dataset Building", leave=True):
             # Call the combined processing function for each singer
            process_single_singer(singer_dir, OUTPUT_DATASET_ROOT, DICTIONARY_PATH, MODEL_CKPT_PATH, PYTHON_EXECUTABLE)
            print("\n" + "="*60 + "\n") # Double line separator between singers

        print("\n" + "="*60)
        print("=== Finished processing all singers ===")
        print("="*60)