Remove: code that is not referenced anywhere

spec_gen.py

@@ -1,87 +0,0 @@
-import torch
-from tqdm import tqdm
-from multiprocessing import Pool
-from mel_processing import spectrogram_torch, mel_spectrogram_torch
-from utils import load_wav_to_torch
-
-
-class AudioProcessor:
-    def __init__(
-        self,
-        max_wav_value,
-        use_mel_spec_posterior,
-        filter_length,
-        n_mel_channels,
-        sampling_rate,
-        hop_length,
-        win_length,
-        mel_fmin,
-        mel_fmax,
-    ):
-        self.max_wav_value = max_wav_value
-        self.use_mel_spec_posterior = use_mel_spec_posterior
-        self.filter_length = filter_length
-        self.n_mel_channels = n_mel_channels
-        self.sampling_rate = sampling_rate
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.mel_fmin = mel_fmin
-        self.mel_fmax = mel_fmax
-
-    def process_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if self.use_mel_spec_posterior:
-            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
-        try:
-            spec = torch.load(spec_filename)
-        except:
-            if self.use_mel_spec_posterior:
-                spec = mel_spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.n_mel_channels,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    self.mel_fmin,
-                    self.mel_fmax,
-                    center=False,
-                )
-            else:
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-
-# 使用示例
-processor = AudioProcessor(
-    max_wav_value=32768.0,
-    use_mel_spec_posterior=False,
-    filter_length=2048,
-    n_mel_channels=128,
-    sampling_rate=44100,
-    hop_length=512,
-    win_length=2048,
-    mel_fmin=0.0,
-    mel_fmax="null",
-)
-
-with open("filelists/train.list", "r") as f:
-    filepaths = [line.split("|")[0] for line in f]  # 取每一行的第一部分作为audiopath
-
-# 使用多进程处理
-with Pool(processes=32) as pool:  # 使用4个进程
-    with tqdm(total=len(filepaths)) as pbar:
-        for i, _ in enumerate(pool.imap_unordered(processor.process_audio, filepaths)):
-            pbar.update()

update_status.py

@@ -1,93 +0,0 @@
-import os
-import gradio as gr
-
-lang_dict = {"EN(英文)": "_en", "ZH(中文)": "_zh", "JP(日语)": "_jp"}
-
-
-def raw_dir_convert_to_path(target_dir: str, lang):
-    res = target_dir.rstrip("/").rstrip("\\")
-    if (not target_dir.startswith("raw")) and (not target_dir.startswith("./raw")):
-        res = os.path.join("./raw", res)
-    if (
-        (not res.endswith("_zh"))
-        and (not res.endswith("_jp"))
-        and (not res.endswith("_en"))
-    ):
-        res += lang_dict[lang]
-    return res
-
-
-def update_g_files():
-    g_files = []
-    cnt = 0
-    for root, dirs, files in os.walk(os.path.abspath("./logs")):
-        for file in files:
-            if file.startswith("G_") and file.endswith(".pth"):
-                g_files.append(os.path.join(root, file))
-                cnt += 1
-    print(g_files)
-    return f"更新模型列表完成, 共找到{cnt}个模型", gr.Dropdown.update(choices=g_files)
-
-
-def update_c_files():
-    c_files = []
-    cnt = 0
-    for root, dirs, files in os.walk(os.path.abspath("./logs")):
-        for file in files:
-            if file.startswith("config.json"):
-                c_files.append(os.path.join(root, file))
-                cnt += 1
-    print(c_files)
-    return f"更新模型列表完成, 共找到{cnt}个配置文件", gr.Dropdown.update(
-        choices=c_files
-    )
-
-
-def update_model_folders():
-    subdirs = []
-    cnt = 0
-    for root, dirs, files in os.walk(os.path.abspath("./logs")):
-        for dir_name in dirs:
-            if os.path.basename(dir_name) != "eval":
-                subdirs.append(os.path.join(root, dir_name))
-                cnt += 1
-    print(subdirs)
-    return f"更新模型文件夹列表完成, 共找到{cnt}个文件夹", gr.Dropdown.update(
-        choices=subdirs
-    )
-
-
-def update_wav_lab_pairs():
-    wav_count = tot_count = 0
-    for root, _, files in os.walk("./raw"):
-        for file in files:
-            # print(file)
-            file_path = os.path.join(root, file)
-            if file.lower().endswith(".wav"):
-                lab_file = os.path.splitext(file_path)[0] + ".lab"
-                if os.path.exists(lab_file):
-                    wav_count += 1
-                tot_count += 1
-    return f"{wav_count} / {tot_count}"
-
-
-def update_raw_folders():
-    subdirs = []
-    cnt = 0
-    script_path = os.path.dirname(os.path.abspath(__file__))  # 获取当前脚本的绝对路径
-    raw_path = os.path.join(script_path, "raw")
-    print(raw_path)
-    os.makedirs(raw_path, exist_ok=True)
-    for root, dirs, files in os.walk(raw_path):
-        for dir_name in dirs:
-            relative_path = os.path.relpath(
-                os.path.join(root, dir_name), script_path
-            )  # 获取相对路径
-            subdirs.append(relative_path)
-            cnt += 1
-    print(subdirs)
-    return (
-        f"更新raw音频文件夹列表完成, 共找到{cnt}个文件夹",
-        gr.Dropdown.update(choices=subdirs),
-        gr.Textbox.update(value=update_wav_lab_pairs()),
-    )