Pass string format arguments as logging method parameters

HHousen · Jul 7, 2021 · 04cd749 · 04cd749
1 parent f95d349
commit 04cd749
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Showing 4 changed files with 12 additions and 12 deletions.
diff --git a/lecture2notes/dataset/transcripts_wer.py b/lecture2notes/dataset/transcripts_wer.py
@@ -136,7 +136,7 @@
  audio_path = process_folder / (video_id + "." + ARGS.audio_format)
 
  end_time = timer() - start_time
- logger.info("Stage 1 (Download and Convert Audio) took %s" % end_time)
+ logger.info("Stage 1 (Download and Convert Audio) took %s", end_time)
 
  # Transcribe
  start_time = timer()
@@ -157,7 +157,7 @@
  )
 
  end_time = timer() - start_time
- logger.info("Stage 2 (Transcribe) took %s" % end_time)
+ logger.info("Stage 2 (Transcribe) took %s", end_time)
 
  transcribe.write_to_file(transcript, transcript_ml_path)
  logger.info(

diff --git a/lecture2notes/end_to_end/summarization_approaches.py b/lecture2notes/end_to_end/summarization_approaches.py
@@ -293,8 +293,8 @@ def extract_features_bow(
  )
  features = vectorizer.fit_transform(data)
 
- logger.debug("done in %fs" % (time() - t0))
- logger.debug("n_samples: %d, n_features: %d" % features.shape)
+ logger.debug("done in %fs", (time() - t0))
+ logger.debug("n_samples: %d, n_features: %d", features.shape)
 
  if return_lsa_svd:
  doc_term_matrix = features.toarray()
@@ -314,7 +314,7 @@ def extract_features_bow(
 
  features = lsa.fit_transform(features)
 
- logger.debug("done in %fs" % (time() - t0))
+ logger.debug("done in %fs", (time() - t0))
 
  explained_variance = svd.explained_variance_ratio_.sum()
  logger.debug(
@@ -555,10 +555,10 @@ def cluster(
  else:
  km = KMeans(n_clusters=num_topics, max_iter=100)
 
- logger.debug("Clustering data with %s" % km)
+ logger.debug("Clustering data with %s", km)
  t0 = time()
  km.fit(X)
- logger.debug("done in %0.3fs" % (time() - t0))
+ logger.debug("done in %0.3fs", (time() - t0))
 
  sentence_clusters = [
  [] for _ in range(num_topics)

diff --git a/lecture2notes/end_to_end/transcribe/transcribe_main.py b/lecture2notes/end_to_end/transcribe/transcribe_main.py
@@ -412,10 +412,10 @@ def resolve_deepspeech_models(dir_name):
  """
 
  pb = glob.glob(dir_name + "/*.pbmm")[0]
- logging.debug("Found model: %s" % pb)
+ logging.debug("Found model: %s", pb)
 
  scorer = glob.glob(dir_name + "/*.scorer")[0]
- logging.debug("Found scorer: %s" % scorer)
+ logging.debug("Found scorer: %s", scorer)
 
  return pb, scorer
 
@@ -515,7 +515,7 @@ def transcribe_audio_deepspeech(
  transcript_json_converted = convert_deepspeech_json(transcript_json)
 
  inference_end = timer() - inference_start
- logger.debug("Inference (transcription) took %0.3fs." % inference_end)
+ logger.debug("Inference (transcription) took %0.3fs.", inference_end)
 
  return transcript_text, transcript_json_converted
 
@@ -682,7 +682,7 @@ def process_segments(
  segment, model, raw_audio_data=True
  )
 
- logging.debug("Chunk Transcript: %s" % transcript)
+ logging.debug("Chunk Transcript: %s", transcript)
 
  full_transcript_json.extend(transcript_json)
 

diff --git a/lecture2notes/end_to_end/transcribe/webrtcvad_utils.py b/lecture2notes/end_to_end/transcribe/webrtcvad_utils.py
@@ -129,7 +129,7 @@ def vad_segment_generator(wavFile, aggressiveness, desired_sample_rate=None):
  """
  from .transcribe_main import read_wave
 
- logging.debug("Caught the wav file @: %s" % (wavFile))
+ logging.debug("Caught the wav file @: %s", (wavFile))
  audio, sample_rate, audio_length = read_wave(wavFile, desired_sample_rate)
  if sample_rate not in (
  8000,