Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab

tools/asr/fasterwhisper_asr.py

@@ -2,7 +2,7 @@ import argparse
 import os
 import traceback
 
-os.environ["HF_ENDPOINT"]          = "https://hf-mirror.com"
+os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 import torch
@@ -11,6 +11,7 @@ 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", 
@@ -32,82 +33,97 @@ language_code_list = [
     "te", "tg", "th", "tk", "tl", 
     "tr", "tt", "uk", "ur", "uz", 
     "vi", "yi", "yo", "zh", "yue",
-    "auto"]
+    "auto"] 
+# fmt: on
+
 
 def execute_asr(input_folder, output_folder, model_size, language, precision):
-    if '-local' in model_size:
+    if "-local" in model_size:
         model_size = model_size[:-6]
-        model_path = f'tools/asr/models/faster-whisper-{model_size}'
+        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'
+    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 = ''
+                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
+                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 == '':
+            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')
+    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__':
+
+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")
+    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,
+        input_folder=cmd.input_folder,
+        output_folder=cmd.output_folder,
+        model_size=cmd.model_size,
+        language=cmd.language,
+        precision=cmd.precision,
     )