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fix: Resolve issues #7, #8, #9 - moviepy, transformers, Whisper OOM

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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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Your Name
2026-03-03 02:10:52 -05:00
parent 70c5d32413
commit d1e1fedcae
4 changed files with 134 additions and 93 deletions
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197
app.py
View File

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

2
requirements.txt
View File

@ -13,7 +13,7 @@ humanize>=4.6.0
# torchaudio >= 2.1.0 is REQUIRED for diarization to work properly
# Transformers ecosystem
transformers>=4.35.0
transformers>=4.35.0,<5.0.0
tokenizers>=0.14.0
# ML dependencies - use flexible versions for compatibility

6
utils/audio_processing.py
View File

@ -19,7 +19,11 @@ def extract_audio(video_path: Path):
audio = AudioFileClip(str(video_path))
temp_dir = tempfile.mkdtemp(prefix="videotranscriber_")
audio_path = Path(temp_dir) / f"{video_path.stem}_audio.wav"
audio.write_audiofile(str(audio_path), verbose=False, logger=None)
try:
audio.write_audiofile(str(audio_path), logger=None)
except TypeError:
# moviepy 1.x uses verbose parameter; moviepy 2.x removed it
audio.write_audiofile(str(audio_path), verbose=False, logger=None)
audio.close()
_temp_audio_files.append(str(audio_path))
return audio_path

22
utils/transcription.py
View File

@ -22,13 +22,33 @@ logger = logging.getLogger(__name__)
WHISPER_MODEL = "base"
WHISPER_MODEL_SIZES = {
"tiny": 75,
"base": 140,
"small": 460,
"medium": 1500,
"large": 2900,
"large-v2": 2900,
"large-v3": 2900,
}
@st.cache_resource
def _load_whisper_model(model_name, device_str):
"""Load and cache a Whisper model. Cached across reruns."""
logger.info(f"Loading Whisper model: {model_name} on {device_str}")
device = torch.device(device_str)
return whisper.load_model(model_name, device=device if device.type != "mps" else "cpu")
try:
return whisper.load_model(model_name, device=device if device.type != "mps" else "cpu")
except (MemoryError, RuntimeError) as e:
err_str = str(e).lower()
if "out of memory" in err_str or "cannot allocate" in err_str or isinstance(e, MemoryError):
size_mb = WHISPER_MODEL_SIZES.get(model_name, "unknown")
raise MemoryError(
f"Not enough memory to load Whisper '{model_name}' model (~{size_mb}MB). "
f"Try a smaller model (tiny/base/small) or enable GPU acceleration."
) from e
raise
def transcribe_audio(audio_path: Path, model=WHISPER_MODEL, use_cache=True, cache_max_age=None,