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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"))
def load_audio(file, sr):
try:
# https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
file = clean_path(file) # 防止小白拷路径头尾带了空格和"和回车
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)
) # Expose the Error
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"
) # path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\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) # type: ignore
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) # type: ignore
print(f"[INFO] Loaded: {so_path}")
except OSError as e:
print(f"[WARNING] Failed to load {so_path}: {e}")
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