fix: logging format

apps/audio_cloning/cheched_vallex.py

@@ -154,7 +154,7 @@ def infer_from_cached_prompt(
     timings: List[Tuple[str, float]] = []
     start_time = time.perf_counter()
     try:
-        print("Loading cached prompt from: %s", prompt_path)
+        print(f"Loading cached prompt from: {prompt_path}")
         prompt_data = np.load(prompt_path)
         audio_tokens = torch.from_numpy(prompt_data["audio_tokens"]).to(
             dtype=torch.long
@@ -201,7 +201,7 @@ def infer_from_cached_prompt(
         audio_prompts = audio_prompts.unsqueeze(0)
 
     start_time = time.perf_counter()
-    print("Start inferring from cached prompt: %s", prompt_path)
+    print(f"Start inferring from cached prompt: {prompt_path}")
     encoded_frames = vallex.model.inference(
         text_tokens.to(vallex.device),
         text_tokens_lens.to(vallex.device),
@@ -235,12 +235,12 @@ def infer_from_cached_prompt(
     )
 
     for step, duration in timings:
-        print("%s：%.4f sec", step, duration)
+        print(f"{step}：{duration:.4f} sec")
 
     timing_report = "\n↓\n".join(
         f"{step}：{duration:.4f} sec" for step, duration in timings
     )
-    print("推論ステップ計測結果\n%s", timing_report)
+    print(f"推論ステップ計測結果\n{timing_report}")
 
     return message, (24000, samples.squeeze(0).cpu().numpy())
 

apps/audio_cloning/vallex/main.py

@@ -43,25 +43,25 @@ logger = logging.getLogger(__name__)
 # set base directory
 OUTPUT_BASE_DIR = os.getenv("HF_HOME", ".")
 PREPARED_BASE_DIR = "."
-print("Base directory: %s", OUTPUT_BASE_DIR)
-print("Prepared base directory: %s", PREPARED_BASE_DIR)
+print(f"Base directory: {OUTPUT_BASE_DIR}")
+print(f"Prepared base directory: {PREPARED_BASE_DIR}")
 
 # set languages
 langid.set_languages(["en", "zh", "ja"])
 
 # set nltk data path
 nltk.data.path = nltk.data.path + [os.path.join(os.getcwd(), "nltk_data")]
-print("nltk_data path: %s", nltk.data.path)
+print(f"nltk_data path: {nltk.data.path}")
 
 # get encoding
 print(
-    "default encoding is %s,file system encoding is %s",
-    sys.getdefaultencoding(),
-    sys.getfilesystemencoding(),
+    "default encoding is "
+    f"{sys.getdefaultencoding()},"
+    f"file system encoding is {sys.getfilesystemencoding()}"
 )
 
 # check python version
-print("You are using Python version %s", platform.python_version())
+print(f"You are using Python version {platform.python_version()}")
 if sys.version_info[0] < 3 or sys.version_info[1] < 7:
     logger.warning("The Python version is too low and may cause problems")
 if platform.system().lower() == "windows":
@@ -74,7 +74,7 @@ os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
 
 # set torch threads (guarded for hot-reload)
 thread_count = multiprocessing.cpu_count()
-print("Use %d cpu cores for computing", thread_count)
+print(f"Use {thread_count} cpu cores for computing")
 if not getattr(torch, "_vallex_threads_configured", False):
     torch.set_num_threads(thread_count)
     try:
@@ -105,7 +105,7 @@ if torch.cuda.is_available():
     device = torch.device("cuda", 0)
 # if torch.backends.mps.is_available():
 #     device = torch.device("mps")
-print("Device set to %s", device)
+print(f"Device set to {device}")
 
 # Download VALL-E-X model weights if not exists
 OUTPUT_DIR_CHECKPOINTS = os.path.join(OUTPUT_BASE_DIR, "models/checkpoints")
@@ -436,12 +436,12 @@ def infer_from_audio(
     timings.append(("ボコーダ復号", time.perf_counter() - start_time))
 
     for step, duration in timings:
-        print("%s：%.4f sec", step, duration)
+        print(f"{step}：{duration:.4f} sec")
 
     timing_report = "\n↓\n".join(
         f"{step}：{duration:.4f} sec" for step, duration in timings
     )
-    print("推論ステップ計測結果\n%s", timing_report)
+    print(f"推論ステップ計測結果\n{timing_report}")
 
     message = f"text prompt: {text_pr}\nsythesized text: {text}"
     return message, (24000, samples.squeeze(0).cpu().numpy())