use module-level logging

pywhispercpp/constants.py

@@ -4,7 +4,6 @@
 """
 Constants
 """
-import logging
 from pathlib import Path
 from typing import Tuple
 
@@ -21,10 +20,10 @@
 
 PACKAGE_NAME = 'pywhispercpp'
 
-LOGGIN_LEVEL = logging.INFO
 
 MODELS_DIR = Path(user_data_dir(PACKAGE_NAME)) / 'models'
 
+
 AVAILABLE_MODELS = [
                 "base",
                 "base-q5_1",

pywhispercpp/model.py

@@ -13,7 +13,6 @@
 from typing import Union, Callable, List
 import _pywhispercpp as pw
 import numpy as np
-from pywhispercpp._logger import set_log_level
 import pywhispercpp.utils as utils
 import pywhispercpp.constants as constants
 import subprocess
@@ -27,6 +26,8 @@
 __version__ = importlib.metadata.version('pywhispercpp')
 
 
+logger = logging.getLogger(__name__)
+
 class Segment:
     """
     A small class representing a transcription segment
@@ -67,21 +68,16 @@ def __init__(self,
                  model: str = 'tiny',
                  models_dir: str = None,
                  params_sampling_strategy: int = 0,
-                 log_level: int = logging.INFO,
                  **params):
         """
         :param model: The name of the model, one of the [AVAILABLE_MODELS](/pywhispercpp/#pywhispercpp.constants.AVAILABLE_MODELS),
                         (default to `tiny`), or a direct path to a `ggml` model.
         :param models_dir: The directory where the models are stored, or where they will be downloaded if they don't
                             exist, default to [MODELS_DIR](/pywhispercpp/#pywhispercpp.constants.MODELS_DIR) <user_data_dir/pywhsipercpp/models>
         :param params_sampling_strategy: 0 -> GREEDY, else BEAM_SEARCH
-        :param log_level: logging level, set to INFO by default
         :param params: keyword arguments for different whisper.cpp parameters,
                         see [PARAMS_SCHEMA](/pywhispercpp/#pywhispercpp.constants.PARAMS_SCHEMA)
         """
-        # set logging level
-        set_log_level(log_level)
-
         if Path(model).is_file():
             self.model_path = model
         else:
@@ -129,10 +125,10 @@ def transcribe(self,
 
         # run inference
         start_time = time()
-        logging.info(f"Transcribing ...")
+        logger.info("Transcribing ...")
         res = self._transcribe(audio, n_processors=n_processors)
         end_time = time()
-        logging.info(f"Inference time: {end_time - start_time:.3f} s")
+        logger.info(f"Inference time: {end_time - start_time:.3f} s")
         return res
 
     @staticmethod
@@ -220,7 +216,7 @@ def _init_model(self) -> None:
         Private method to initialize the method from the bindings, it will be called automatically from the __init__
         :return:
         """
-        logging.info("Initializing the model ...")
+        logger.info("Initializing the model ...")
         self._ctx = pw.whisper_init_from_file(self.model_path)
         self._params = pw.whisper_full_default_params(pw.whisper_sampling_strategy.WHISPER_SAMPLING_GREEDY)
 

pywhispercpp/utils.py

@@ -12,6 +12,9 @@
 from pywhispercpp.constants import MODELS_BASE_URL, MODELS_PREFIX_URL, AVAILABLE_MODELS, MODELS_DIR
 
 
+logger = logging.getLogger(__name__)
+
+
 def _get_model_url(model_name: str) -> str:
     """
     Returns the url of the `ggml` model
@@ -31,19 +34,19 @@ def download_model(model_name: str, download_dir=None, chunk_size=1024) -> str:
     :return: Absolute path of the downloaded model
     """
     if model_name not in AVAILABLE_MODELS:
-        logging.error(f"Invalid model name `{model_name}`, available models are: {AVAILABLE_MODELS}")
+        logger.error(f"Invalid model name `{model_name}`, available models are: {AVAILABLE_MODELS}")
         return
     if download_dir is None:
         download_dir = MODELS_DIR
-        logging.info(f"No download directory was provided, models will be downloaded to {download_dir}")
+        logger.info(f"No download directory was provided, models will be downloaded to {download_dir}")
 
     os.makedirs(download_dir, exist_ok=True)
 
     url = _get_model_url(model_name=model_name)
     file_path = Path(download_dir) / os.path.basename(url)
     # check if the file is already there
     if file_path.exists():
-        logging.info(f"Model {model_name} already exists in {download_dir}")
+        logger.info(f"Model {model_name} already exists in {download_dir}")
     else:
         # download it from huggingface
         resp = requests.get(url, stream=True)
@@ -60,7 +63,7 @@ def download_model(model_name: str, download_dir=None, chunk_size=1024) -> str:
                 for data in resp.iter_content(chunk_size=chunk_size):
                     size = file.write(data)
                     progress_bar.update(size)
-            logging.info(f"Model downloaded to {file_path.absolute()}")
+            logger.info(f"Model downloaded to {file_path.absolute()}")
         except Exception as e:
             # error download, just remove the file
             os.remove(file_path)