| import ctypes
|
| import os
|
| import sys
|
| from pathlib import Path
|
|
|
| import ffmpeg
|
| import gradio as gr
|
| import numpy as np
|
| import pandas as pd
|
|
|
| from tools.i18n.i18n import I18nAuto
|
|
|
| i18n = I18nAuto(language=os.environ.get("language", "Auto"))
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|
|
|
|
| def load_audio(file, sr):
|
| try:
|
|
|
|
|
|
|
| file = clean_path(file)
|
| if os.path.exists(file) is False:
|
| raise RuntimeError("You input a wrong audio path that does not exists, please fix it!")
|
| out, _ = (
|
| ffmpeg.input(file, threads=0)
|
| .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
|
| .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
|
| )
|
| except Exception:
|
| out, _ = (
|
| ffmpeg.input(file, threads=0)
|
| .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
|
| .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True)
|
| )
|
| raise RuntimeError(i18n("音频加载失败"))
|
|
|
| return np.frombuffer(out, np.float32).flatten()
|
|
|
|
|
| def clean_path(path_str: str):
|
| if path_str.endswith(("\\", "/")):
|
| return clean_path(path_str[0:-1])
|
| path_str = path_str.replace("/", os.sep).replace("\\", os.sep)
|
| return path_str.strip(
|
| " '\n\"\u202a"
|
| )
|
|
|
|
|
| def check_for_existance(file_list: list = None, is_train=False, is_dataset_processing=False):
|
| files_status = []
|
| if is_train == True and file_list:
|
| file_list.append(os.path.join(file_list[0], "2-name2text.txt"))
|
| file_list.append(os.path.join(file_list[0], "3-bert"))
|
| file_list.append(os.path.join(file_list[0], "4-cnhubert"))
|
| file_list.append(os.path.join(file_list[0], "5-wav32k"))
|
| file_list.append(os.path.join(file_list[0], "6-name2semantic.tsv"))
|
| for file in file_list:
|
| if os.path.exists(file):
|
| files_status.append(True)
|
| else:
|
| files_status.append(False)
|
| if sum(files_status) != len(files_status):
|
| if is_train:
|
| for file, status in zip(file_list, files_status):
|
| if status:
|
| pass
|
| else:
|
| gr.Warning(file)
|
| gr.Warning(i18n("以下文件或文件夹不存在"))
|
| return False
|
| elif is_dataset_processing:
|
| if files_status[0]:
|
| return True
|
| elif not files_status[0]:
|
| gr.Warning(file_list[0])
|
| elif not files_status[1] and file_list[1]:
|
| gr.Warning(file_list[1])
|
| gr.Warning(i18n("以下文件或文件夹不存在"))
|
| return False
|
| else:
|
| if file_list[0]:
|
| gr.Warning(file_list[0])
|
| gr.Warning(i18n("以下文件或文件夹不存在"))
|
| else:
|
| gr.Warning(i18n("路径不能为空"))
|
| return False
|
| return True
|
|
|
|
|
| def check_details(path_list=None, is_train=False, is_dataset_processing=False):
|
| if is_dataset_processing:
|
| list_path, audio_path = path_list
|
| if not list_path.endswith(".list"):
|
| gr.Warning(i18n("请填入正确的List路径"))
|
| return
|
| if audio_path:
|
| if not os.path.isdir(audio_path):
|
| gr.Warning(i18n("请填入正确的音频文件夹路径"))
|
| return
|
| with open(list_path, "r", encoding="utf8") as f:
|
| line = f.readline().strip("\n").split("\n")
|
| wav_name, _, __, ___ = line[0].split("|")
|
| wav_name = clean_path(wav_name)
|
| if audio_path != "" and audio_path != None:
|
| wav_name = os.path.basename(wav_name)
|
| wav_path = "%s/%s" % (audio_path, wav_name)
|
| else:
|
| wav_path = wav_name
|
| if os.path.exists(wav_path):
|
| ...
|
| else:
|
| gr.Warning(wav_path + i18n("路径错误"))
|
| return
|
| if is_train:
|
| path_list.append(os.path.join(path_list[0], "2-name2text.txt"))
|
| path_list.append(os.path.join(path_list[0], "4-cnhubert"))
|
| path_list.append(os.path.join(path_list[0], "5-wav32k"))
|
| path_list.append(os.path.join(path_list[0], "6-name2semantic.tsv"))
|
| phone_path, hubert_path, wav_path, semantic_path = path_list[1:]
|
| with open(phone_path, "r", encoding="utf-8") as f:
|
| if f.read(1):
|
| ...
|
| else:
|
| gr.Warning(i18n("缺少音素数据集"))
|
| if os.listdir(hubert_path):
|
| ...
|
| else:
|
| gr.Warning(i18n("缺少Hubert数据集"))
|
| if os.listdir(wav_path):
|
| ...
|
| else:
|
| gr.Warning(i18n("缺少音频数据集"))
|
| df = pd.read_csv(semantic_path, delimiter="\t", encoding="utf-8")
|
| if len(df) >= 1:
|
| ...
|
| else:
|
| gr.Warning(i18n("缺少语义数据集"))
|
|
|
|
|
| def load_cudnn():
|
| import torch
|
|
|
| if not torch.cuda.is_available():
|
| print("[INFO] CUDA is not available, skipping cuDNN setup.")
|
| return
|
|
|
| if sys.platform == "win32":
|
| torch_lib_dir = Path(torch.__file__).parent / "lib"
|
| if torch_lib_dir.exists():
|
| os.add_dll_directory(str(torch_lib_dir))
|
| print(f"[INFO] Added DLL directory: {torch_lib_dir}")
|
| matching_files = sorted(torch_lib_dir.glob("cudnn_cnn*.dll"))
|
| if not matching_files:
|
| print(f"[ERROR] No cudnn_cnn*.dll found in {torch_lib_dir}")
|
| return
|
| for dll_path in matching_files:
|
| dll_name = os.path.basename(dll_path)
|
| try:
|
| ctypes.CDLL(dll_name)
|
| print(f"[INFO] Loaded: {dll_name}")
|
| except OSError as e:
|
| print(f"[WARNING] Failed to load {dll_name}: {e}")
|
| else:
|
| print(f"[WARNING] Torch lib directory not found: {torch_lib_dir}")
|
|
|
| elif sys.platform == "linux":
|
| site_packages = Path(torch.__file__).resolve().parents[1]
|
| cudnn_dir = site_packages / "nvidia" / "cudnn" / "lib"
|
|
|
| if not cudnn_dir.exists():
|
| print(f"[ERROR] cudnn dir not found: {cudnn_dir}")
|
| return
|
|
|
| matching_files = sorted(cudnn_dir.glob("libcudnn_cnn*.so*"))
|
| if not matching_files:
|
| print(f"[ERROR] No libcudnn_cnn*.so* found in {cudnn_dir}")
|
| return
|
|
|
| for so_path in matching_files:
|
| try:
|
| ctypes.CDLL(so_path, mode=ctypes.RTLD_GLOBAL)
|
| print(f"[INFO] Loaded: {so_path}")
|
| except OSError as e:
|
| print(f"[WARNING] Failed to load {so_path}: {e}")
|
|
|
|
|
| def load_nvrtc():
|
| import torch
|
|
|
| if not torch.cuda.is_available():
|
| print("[INFO] CUDA is not available, skipping nvrtc setup.")
|
| return
|
|
|
| if sys.platform == "win32":
|
| torch_lib_dir = Path(torch.__file__).parent / "lib"
|
| if torch_lib_dir.exists():
|
| os.add_dll_directory(str(torch_lib_dir))
|
| print(f"[INFO] Added DLL directory: {torch_lib_dir}")
|
| matching_files = sorted(torch_lib_dir.glob("nvrtc*.dll"))
|
| if not matching_files:
|
| print(f"[ERROR] No nvrtc*.dll found in {torch_lib_dir}")
|
| return
|
| for dll_path in matching_files:
|
| dll_name = os.path.basename(dll_path)
|
| try:
|
| ctypes.CDLL(dll_name)
|
| print(f"[INFO] Loaded: {dll_name}")
|
| except OSError as e:
|
| print(f"[WARNING] Failed to load {dll_name}: {e}")
|
| else:
|
| print(f"[WARNING] Torch lib directory not found: {torch_lib_dir}")
|
|
|
| elif sys.platform == "linux":
|
| site_packages = Path(torch.__file__).resolve().parents[1]
|
| nvrtc_dir = site_packages / "nvidia" / "cuda_nvrtc" / "lib"
|
|
|
| if not nvrtc_dir.exists():
|
| print(f"[ERROR] nvrtc dir not found: {nvrtc_dir}")
|
| return
|
|
|
| matching_files = sorted(nvrtc_dir.glob("libnvrtc*.so*"))
|
| if not matching_files:
|
| print(f"[ERROR] No libnvrtc*.so* found in {nvrtc_dir}")
|
| return
|
|
|
| for so_path in matching_files:
|
| try:
|
| ctypes.CDLL(so_path, mode=ctypes.RTLD_GLOBAL)
|
| print(f"[INFO] Loaded: {so_path}")
|
| except OSError as e:
|
| print(f"[WARNING] Failed to load {so_path}: {e}")
|
|
|