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import logging
import os
import tempfile
logger = logging.getLogger(__name__)
def process_transcription(audio_content: bytes, whisper_model):
if not whisper_model:
raise ValueError("Whisper model not loaded.")
temp_file_path = None
try:
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
temp_file_path = temp_file.name
temp_file.write(audio_content)
segments, info = whisper_model.transcribe(temp_file_path, beam_size=5)
transcript = " ".join([seg.text.strip() for seg in segments])
return transcript, info
finally:
if temp_file_path and os.path.exists(temp_file_path):
os.remove(temp_file_path)
def process_summary(text: str, summarizer_pipeline, nlp_spacy, config):
if not summarizer_pipeline:
raise ValueError("Summarizer model not loaded.")
processed_text = text
if nlp_spacy:
try:
doc = nlp_spacy(text)
sentences = [sent.text.strip() for sent in doc.sents]
processed_text = " ".join(sentences)
except Exception as e:
logger.error(f"SpaCy processing failed: {e}", exc_info=True)
summary_output = summarizer_pipeline(
processed_text,
max_length=config.SUMMARIZER_MAX_LENGTH,
min_length=config.SUMMARIZER_MIN_LENGTH,
do_sample=False
)
final_summary = summary_output[0]['summary_text']
return final_summary |