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code style updates

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This commit is contained in:
Gokul Mohanarangan
2023-07-26 09:59:25 +05:30
parent b892fc0562
commit c970fc89dd
8 changed files with 54 additions and 56 deletions
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16
utils/text_utils.py
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@@ -121,9 +121,9 @@ def summarize_chunks(chunks, tokenizer, model):
with torch.no_grad():
summary_ids = \
model.generate(input_ids,
num_beams=int(config["DEFAULT"]["BEAM_SIZE"]),
num_beams=int(config["SUMMARIZER"]["BEAM_SIZE"]),
length_penalty=2.0,
max_length=int(config["DEFAULT"]["MAX_LENGTH"]),
max_length=int(config["SUMMARIZER"]["MAX_LENGTH"]),
early_stopping=True)
summary = tokenizer.decode(summary_ids[0],
skip_special_tokens=True)
@@ -132,7 +132,7 @@ def summarize_chunks(chunks, tokenizer, model):
def chunk_text(text,
max_chunk_length=int(config["DEFAULT"]["MAX_CHUNK_LENGTH"])):
max_chunk_length=int(config["SUMMARIZER"]["MAX_CHUNK_LENGTH"])):
"""
Split text into smaller chunks.
:param text: Text to be chunked
@@ -154,9 +154,9 @@ def chunk_text(text,
def summarize(transcript_text, timestamp,
real_time=False,
chunk_summarize=config["DEFAULT"]["SUMMARIZE_USING_CHUNKS"]):
chunk_summarize=config["SUMMARIZER"]["SUMMARIZE_USING_CHUNKS"]):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
summary_model = config["DEFAULT"]["SUMMARY_MODEL"]
summary_model = config["SUMMARIZER"]["SUMMARY_MODEL"]
if not summary_model:
summary_model = "facebook/bart-large-cnn"
@@ -171,7 +171,7 @@ def summarize(transcript_text, timestamp,
output_file = "real_time_" + output_file
if chunk_summarize != "YES":
max_length = int(config["DEFAULT"]["INPUT_ENCODING_MAX_LENGTH"])
max_length = int(config["SUMMARIZER"]["INPUT_ENCODING_MAX_LENGTH"])
inputs = tokenizer. \
batch_encode_plus([transcript_text], truncation=True,
padding='longest',
@@ -180,8 +180,8 @@ def summarize(transcript_text, timestamp,
inputs = inputs.to(device)
with torch.no_grad():
num_beans = int(config["DEFAULT"]["BEAM_SIZE"])
max_length = int(config["DEFAULT"]["MAX_LENGTH"])
num_beans = int(config["SUMMARIZER"]["BEAM_SIZE"])
max_length = int(config["SUMMARIZER"]["MAX_LENGTH"])
summaries = model.generate(inputs['input_ids'],
num_beams=num_beans,
length_penalty=2.0,