mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-29 05:39:04 +08:00
53cac93589
* ruff check --fix * ruff format --line-length 120 --target-version py39 * Change the link for G2PW Model * update pytorch version and colab
130 lines
4.2 KiB
Python
130 lines
4.2 KiB
Python
import argparse
|
|
import os
|
|
import traceback
|
|
|
|
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
|
|
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
|
|
|
|
import torch
|
|
from faster_whisper import WhisperModel
|
|
from tqdm import tqdm
|
|
|
|
from tools.asr.config import check_fw_local_models
|
|
|
|
# fmt: off
|
|
language_code_list = [
|
|
"af", "am", "ar", "as", "az",
|
|
"ba", "be", "bg", "bn", "bo",
|
|
"br", "bs", "ca", "cs", "cy",
|
|
"da", "de", "el", "en", "es",
|
|
"et", "eu", "fa", "fi", "fo",
|
|
"fr", "gl", "gu", "ha", "haw",
|
|
"he", "hi", "hr", "ht", "hu",
|
|
"hy", "id", "is", "it", "ja",
|
|
"jw", "ka", "kk", "km", "kn",
|
|
"ko", "la", "lb", "ln", "lo",
|
|
"lt", "lv", "mg", "mi", "mk",
|
|
"ml", "mn", "mr", "ms", "mt",
|
|
"my", "ne", "nl", "nn", "no",
|
|
"oc", "pa", "pl", "ps", "pt",
|
|
"ro", "ru", "sa", "sd", "si",
|
|
"sk", "sl", "sn", "so", "sq",
|
|
"sr", "su", "sv", "sw", "ta",
|
|
"te", "tg", "th", "tk", "tl",
|
|
"tr", "tt", "uk", "ur", "uz",
|
|
"vi", "yi", "yo", "zh", "yue",
|
|
"auto"]
|
|
# fmt: on
|
|
|
|
|
|
def execute_asr(input_folder, output_folder, model_size, language, precision):
|
|
if "-local" in model_size:
|
|
model_size = model_size[:-6]
|
|
model_path = f"tools/asr/models/faster-whisper-{model_size}"
|
|
else:
|
|
model_path = model_size
|
|
if language == "auto":
|
|
language = None # 不设置语种由模型自动输出概率最高的语种
|
|
print("loading faster whisper model:", model_size, model_path)
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
try:
|
|
model = WhisperModel(model_path, device=device, compute_type=precision)
|
|
except:
|
|
return print(traceback.format_exc())
|
|
|
|
input_file_names = os.listdir(input_folder)
|
|
input_file_names.sort()
|
|
|
|
output = []
|
|
output_file_name = os.path.basename(input_folder)
|
|
|
|
for file_name in tqdm(input_file_names):
|
|
try:
|
|
file_path = os.path.join(input_folder, file_name)
|
|
segments, info = model.transcribe(
|
|
audio=file_path,
|
|
beam_size=5,
|
|
vad_filter=True,
|
|
vad_parameters=dict(min_silence_duration_ms=700),
|
|
language=language,
|
|
)
|
|
text = ""
|
|
|
|
if info.language == "zh":
|
|
print("检测为中文文本, 转 FunASR 处理")
|
|
if "only_asr" not in globals():
|
|
from tools.asr.funasr_asr import only_asr # 如果用英文就不需要导入下载模型
|
|
text = only_asr(file_path, language=info.language.lower())
|
|
|
|
if text == "":
|
|
for segment in segments:
|
|
text += segment.text
|
|
output.append(f"{file_path}|{output_file_name}|{info.language.upper()}|{text}")
|
|
except:
|
|
print(traceback.format_exc())
|
|
|
|
output_folder = output_folder or "output/asr_opt"
|
|
os.makedirs(output_folder, exist_ok=True)
|
|
output_file_path = os.path.abspath(f"{output_folder}/{output_file_name}.list")
|
|
|
|
with open(output_file_path, "w", encoding="utf-8") as f:
|
|
f.write("\n".join(output))
|
|
print(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
|
|
return output_file_path
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"-i", "--input_folder", type=str, required=True, help="Path to the folder containing WAV files."
|
|
)
|
|
parser.add_argument("-o", "--output_folder", type=str, required=True, help="Output folder to store transcriptions.")
|
|
parser.add_argument(
|
|
"-s",
|
|
"--model_size",
|
|
type=str,
|
|
default="large-v3",
|
|
choices=check_fw_local_models(),
|
|
help="Model Size of Faster Whisper",
|
|
)
|
|
parser.add_argument(
|
|
"-l", "--language", type=str, default="ja", choices=language_code_list, help="Language of the audio files."
|
|
)
|
|
parser.add_argument(
|
|
"-p",
|
|
"--precision",
|
|
type=str,
|
|
default="float16",
|
|
choices=["float16", "float32", "int8"],
|
|
help="fp16, int8 or fp32",
|
|
)
|
|
|
|
cmd = parser.parse_args()
|
|
output_file_path = execute_asr(
|
|
input_folder=cmd.input_folder,
|
|
output_folder=cmd.output_folder,
|
|
model_size=cmd.model_size,
|
|
language=cmd.language,
|
|
precision=cmd.precision,
|
|
)
|