flake8 checks

server.py

@@ -53,11 +53,11 @@ def get_title_and_summary(llm_input_text, last_timestamp):
 
     prompt = f"""
         ### Human:
-        Create a JSON object as response. The JSON object must have 2 fields: 
-        i) title and ii) summary. For the title field,generate a short title 
-        for the given text. For the summary field, summarize the given text 
+        Create a JSON object as response. The JSON object must have 2 fields:
+        i) title and ii) summary. For the title field,generate a short title
+        for the given text. For the summary field, summarize the given text
         in three sentences.
-        
+
         {llm_input_text}
 
         ### Assistant:
@@ -144,12 +144,12 @@ def get_transcription(frames):
     result_text = ""
 
     try:
-        segments, _ = model.transcribe(audiofilename,
-                                       language="en",
-                                       beam_size=5,
-                                       vad_filter=True,
-                                       vad_parameters=dict(min_silence_duration_ms=500)
-                                       )
+        segments, _ = \
+            model.transcribe(audiofilename,
+                             language="en",
+                             beam_size=5,
+                             vad_filter=True,
+                             vad_parameters=dict(min_silence_duration_ms=500))
         os.remove(audiofilename)
         segments = list(segments)
         result_text = ""

trials/server_multithreaded.py

@@ -16,8 +16,8 @@ from av import AudioFifo
 from sortedcontainers import SortedDict
 from whisper_jax import FlaxWhisperPipline
 
-from utils.log_utils import logger
-from utils.run_utils import config, Mutex
+from ..utils.log_utils import logger
+from ..utils.run_utils import config, Mutex
 
 WHISPER_MODEL_SIZE = config['DEFAULT']["WHISPER_REAL_TIME_MODEL_SIZE"]
 pcs = set()

trials/whisper-jax/whisjax.py

@@ -21,8 +21,8 @@ from whisper_jax import FlaxWhisperPipline
 from ...utils.file_utils import download_files, upload_files
 from ...utils.log_utils import logger
 from ...utils.run_utils import config
-from ...utils.text_utilities import post_process_transcription, summarize
-from ...utils.viz_utilities import create_talk_diff_scatter_viz, create_wordcloud
+from ...utils.text_utils import post_process_transcription, summarize
+from ...utils.viz_utils import create_talk_diff_scatter_viz, create_wordcloud
 
 nltk.download('punkt', quiet=True)
 nltk.download('stopwords', quiet=True)

trials/whisper-jax/whisjax_realtime.py

@@ -13,8 +13,8 @@ from whisper_jax import FlaxWhisperPipline
 from ...utils.file_utils import upload_files
 from ...utils.log_utils import logger
 from ...utils.run_utils import config
-from ...utils.text_utilities import post_process_transcription, summarize
-from ...utils.viz_utilities import create_talk_diff_scatter_viz, create_wordcloud
+from ...utils.text_utils import post_process_transcription, summarize
+from ...utils.viz_utils import create_talk_diff_scatter_viz, create_wordcloud
 
 WHISPER_MODEL_SIZE = config['DEFAULT']["WHISPER_MODEL_SIZE"]
 

utils/text_utils.py

@@ -154,7 +154,7 @@ def chunk_text(text,
 
 def summarize(transcript_text, timestamp,
               real_time=False,
-              summarize_using_chunks=config["DEFAULT"]["SUMMARIZE_USING_CHUNKS"]):
+              chunk_summarize=config["DEFAULT"]["SUMMARIZE_USING_CHUNKS"]):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     summary_model = config["DEFAULT"]["SUMMARY_MODEL"]
     if not summary_model:
@@ -166,27 +166,35 @@ def summarize(transcript_text, timestamp,
     model = BartForConditionalGeneration.from_pretrained(summary_model)
     model = model.to(device)
 
-    output_filename = "summary_" + timestamp.strftime("%m-%d-%Y_%H:%M:%S") + ".txt"
+    output_file = "summary_" + timestamp.strftime("%m-%d-%Y_%H:%M:%S") + ".txt"
     if real_time:
-        output_filename = "real_time_" + output_filename
+        output_file = "real_time_" + output_file
 
-    if summarize_using_chunks != "YES":
+    if chunk_summarize != "YES":
+        max_length = int(config["DEFAULT"]["INPUT_ENCODING_MAX_LENGTH"])
         inputs = tokenizer. \
             batch_encode_plus([transcript_text], truncation=True,
                               padding='longest',
-                              max_length=int(config["DEFAULT"]["INPUT_ENCODING_MAX_LENGTH"]),
+                              max_length=max_length,
                               return_tensors='pt')
         inputs = inputs.to(device)
 
         with torch.no_grad():
+            num_beans = int(config["DEFAULT"]["BEAM_SIZE"])
+            max_length = int(config["DEFAULT"]["MAX_LENGTH"])
             summaries = model.generate(inputs['input_ids'],
-                                       num_beams=int(config["DEFAULT"]["BEAM_SIZE"]), length_penalty=2.0,
-                                       max_length=int(config["DEFAULT"]["MAX_LENGTH"]), early_stopping=True)
+                                       num_beams=num_beans,
+                                       length_penalty=2.0,
+                                       max_length=max_length,
+                                       early_stopping=True)
 
-        decoded_summaries = [tokenizer.decode(summary, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-                             for summary in summaries]
+        decoded_summaries = \
+            [tokenizer.decode(summary,
+                              skip_special_tokens=True,
+                              clean_up_tokenization_spaces=False)
+             for summary in summaries]
         summary = " ".join(decoded_summaries)
-        with open("./artefacts/" + output_filename, 'w') as f:
+        with open("./artefacts/" + output_file, 'w') as f:
             f.write(summary.strip() + "\n")
     else:
         logger.info("Breaking transcript into smaller chunks")
@@ -195,8 +203,8 @@ def summarize(transcript_text, timestamp,
         logger.info(f"Transcript broken into {len(chunks)} "
                     f"chunks of at most 500 words")
 
-        logger.info(f"Writing summary text to: {output_filename}")
-        with open(output_filename, 'w') as f:
+        logger.info(f"Writing summary text to: {output_file}")
+        with open(output_file, 'w') as f:
             summaries = summarize_chunks(chunks, tokenizer, model)
             for summary in summaries:
                 f.write(summary.strip() + " ")

utils/viz_utils.py

@@ -45,14 +45,14 @@ def create_wordcloud(timestamp, real_time=False):
     plt.axis("off")
     plt.tight_layout(pad=0)
 
-    wordcloud_name = "wordcloud"
+    wordcloud = "wordcloud"
     if real_time:
-        wordcloud_name = "real_time_" + wordcloud_name + "_" + \
+        wordcloud = "real_time_" + wordcloud + "_" + \
                          timestamp.strftime("%m-%d-%Y_%H:%M:%S") + ".png"
     else:
-        wordcloud_name += "_" + timestamp.strftime("%m-%d-%Y_%H:%M:%S") + ".png"
+        wordcloud += "_" + timestamp.strftime("%m-%d-%Y_%H:%M:%S") + ".png"
 
-    plt.savefig("./artefacts/" + wordcloud_name)
+    plt.savefig("./artefacts/" + wordcloud)
 
 
 def create_talk_diff_scatter_viz(timestamp, real_time=False):