Issue #7: Handle moviepy 2.x removing verbose param from write_audiofile Issue #8: Pin transformers<5.0.0 to fix summarization pipeline task registry Issue #9: Add Whisper model memory warnings and OOM error handling
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197
app.py
197
app.py
@ -113,9 +113,16 @@ def render_sidebar():
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index=["tiny", "base", "small", "medium", "large"].index(
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st.session_state.transcription_model
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),
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help="Larger models are more accurate but slower.",
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help="Larger models are more accurate but slower. "
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"Memory: tiny ~75MB, base ~140MB, small ~460MB, medium ~1.5GB, large ~2.9GB",
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key="sb_whisper_model",
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)
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if st.session_state.transcription_model in ("large", "large-v2", "large-v3") and not st.session_state.get("use_gpu", False):
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st.warning(
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"The **large** Whisper model requires ~2.9GB of memory. "
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"Without GPU, this may crash the application. Consider using "
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"**medium** or smaller, or enable GPU acceleration."
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)
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summarization_options = (
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["Hugging Face (Online)", "Ollama (Local)"]
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@ -407,109 +414,119 @@ def process_recording(file_path, sidebar_opts):
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results = {}
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start_time = time.time()
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with st.status("Processing recording...", expanded=True) as status:
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try:
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with st.status("Processing recording...", expanded=True) as status:
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# Step 1: Transcription
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st.write(f"Transcribing with Whisper ({st.session_state.transcription_model} model)...")
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t0 = time.time()
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if st.session_state.use_diarization and DIARIZATION_AVAILABLE and sidebar_opts["hf_token"]:
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num_spk = int(sidebar_opts["num_speakers"]) if sidebar_opts["num_speakers"] > 0 else None
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segments, transcript = transcribe_with_diarization(
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file_path,
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whisper_model=st.session_state.transcription_model,
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num_speakers=num_spk,
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use_gpu=st.session_state.use_gpu,
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hf_token=sidebar_opts["hf_token"],
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)
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results["diarized"] = True
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elif st.session_state.use_translation and TRANSLATION_AVAILABLE:
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st.write("Transcribing and translating...")
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orig_seg, trans_seg, orig_text, trans_text = transcribe_and_translate(
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file_path,
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whisper_model=st.session_state.transcription_model,
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target_lang=sidebar_opts["target_lang"],
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use_gpu=st.session_state.use_gpu,
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)
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segments = trans_seg
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transcript = trans_text
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results["original_text"] = orig_text
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results["original_segments"] = orig_seg
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results["translated"] = True
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else:
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segments, transcript = transcribe_audio(
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file_path,
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model=st.session_state.transcription_model,
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use_cache=st.session_state.use_cache,
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use_gpu=st.session_state.use_gpu,
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memory_fraction=st.session_state.memory_fraction,
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)
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transcription_time = time.time() - t0
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st.write(f"Transcription complete ({transcription_time:.1f}s)")
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if not transcript:
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status.update(label="Processing failed", state="error")
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return None
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results["segments"] = segments
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results["transcript"] = transcript
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# Step 2: Keyword extraction
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if st.session_state.use_keywords and KEYWORD_EXTRACTION_AVAILABLE:
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st.write("Extracting keywords...")
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# Step 1: Transcription
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st.write(f"Transcribing with Whisper ({st.session_state.transcription_model} model)...")
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t0 = time.time()
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kw_ts, ent_ts = extract_keywords_from_transcript(
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transcript, segments,
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max_keywords=sidebar_opts["max_keywords"],
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use_gpu=st.session_state.use_gpu,
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if st.session_state.use_diarization and DIARIZATION_AVAILABLE and sidebar_opts["hf_token"]:
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num_spk = int(sidebar_opts["num_speakers"]) if sidebar_opts["num_speakers"] > 0 else None
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segments, transcript = transcribe_with_diarization(
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file_path,
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whisper_model=st.session_state.transcription_model,
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num_speakers=num_spk,
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use_gpu=st.session_state.use_gpu,
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hf_token=sidebar_opts["hf_token"],
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)
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results["diarized"] = True
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elif st.session_state.use_translation and TRANSLATION_AVAILABLE:
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st.write("Transcribing and translating...")
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orig_seg, trans_seg, orig_text, trans_text = transcribe_and_translate(
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file_path,
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whisper_model=st.session_state.transcription_model,
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target_lang=sidebar_opts["target_lang"],
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use_gpu=st.session_state.use_gpu,
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)
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segments = trans_seg
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transcript = trans_text
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results["original_text"] = orig_text
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results["original_segments"] = orig_seg
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results["translated"] = True
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else:
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segments, transcript = transcribe_audio(
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file_path,
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model=st.session_state.transcription_model,
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use_cache=st.session_state.use_cache,
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use_gpu=st.session_state.use_gpu,
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memory_fraction=st.session_state.memory_fraction,
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)
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transcription_time = time.time() - t0
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st.write(f"Transcription complete ({transcription_time:.1f}s)")
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if not transcript:
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status.update(label="Processing failed", state="error")
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return None
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results["segments"] = segments
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results["transcript"] = transcript
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# Step 2: Keyword extraction
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if st.session_state.use_keywords and KEYWORD_EXTRACTION_AVAILABLE:
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st.write("Extracting keywords...")
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t0 = time.time()
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kw_ts, ent_ts = extract_keywords_from_transcript(
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transcript, segments,
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max_keywords=sidebar_opts["max_keywords"],
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use_gpu=st.session_state.use_gpu,
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)
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results["keyword_timestamps"] = kw_ts
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results["entity_timestamps"] = ent_ts
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results["keyword_index"] = generate_keyword_index(kw_ts, ent_ts)
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results["interactive_transcript"] = generate_interactive_transcript(segments, kw_ts, ent_ts)
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st.write(f"Keywords extracted ({time.time() - t0:.1f}s)")
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# Step 3: Summarization
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st.write("Generating summary...")
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t0 = time.time()
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use_ollama = (
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OLLAMA_AVAILABLE
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and st.session_state.summarization_method == "Ollama (Local)"
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and sidebar_opts["ollama_model"]
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)
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results["keyword_timestamps"] = kw_ts
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results["entity_timestamps"] = ent_ts
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results["keyword_index"] = generate_keyword_index(kw_ts, ent_ts)
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results["interactive_transcript"] = generate_interactive_transcript(segments, kw_ts, ent_ts)
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st.write(f"Keywords extracted ({time.time() - t0:.1f}s)")
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# Step 3: Summarization
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st.write("Generating summary...")
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t0 = time.time()
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use_ollama = (
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OLLAMA_AVAILABLE
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and st.session_state.summarization_method == "Ollama (Local)"
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and sidebar_opts["ollama_model"]
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)
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if use_ollama:
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summary = chunk_and_summarize(transcript, model=sidebar_opts["ollama_model"])
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if not summary:
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st.write("Ollama failed, falling back to Hugging Face...")
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if use_ollama:
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summary = chunk_and_summarize(transcript, model=sidebar_opts["ollama_model"])
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if not summary:
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st.write("Ollama failed, falling back to Hugging Face...")
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summary = summarize_text(
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transcript,
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use_gpu=st.session_state.use_gpu,
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memory_fraction=st.session_state.memory_fraction,
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)
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results["ollama_streaming"] = True
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else:
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summary = summarize_text(
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transcript,
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use_gpu=st.session_state.use_gpu,
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memory_fraction=st.session_state.memory_fraction,
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)
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results["ollama_streaming"] = True
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else:
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summary = summarize_text(
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transcript,
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use_gpu=st.session_state.use_gpu,
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memory_fraction=st.session_state.memory_fraction,
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)
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results["summary"] = summary
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st.write(f"Summary generated ({time.time() - t0:.1f}s)")
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results["summary"] = summary
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st.write(f"Summary generated ({time.time() - t0:.1f}s)")
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# Cleanup temp audio files
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cleanup_temp_audio()
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# Cleanup temp audio files
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cleanup_temp_audio()
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total_time = time.time() - start_time
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results["processing_time"] = total_time
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results["word_count"] = len(transcript.split())
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total_time = time.time() - start_time
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results["processing_time"] = total_time
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results["word_count"] = len(transcript.split())
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status.update(label=f"Complete in {total_time:.1f}s", state="complete")
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status.update(label=f"Complete in {total_time:.1f}s", state="complete")
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return results
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return results
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except MemoryError as e:
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st.error(str(e))
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logger.error(f"Out of memory: {e}")
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return None
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except Exception as e:
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st.error(f"Processing error: {e}")
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logger.error(f"Processing error: {e}", exc_info=True)
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return None
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def render_results(results, sidebar_opts):
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