WhisperX transcription returns only hallucinated filler ("Thank you.") — the identical command works when run by hand, and Scriberr's subprocess never touches the GPU

[EmmanuelZapata]

Summary

Every transcription through Scriberr returns a handful of tiny hallucinated segments (" Thank you." / " you.") instead of the real speech, regardless of model size, VAD method, or whether diarization is on. The audio is fine and the exact WhisperX command Scriberr logs — run manually inside the same container, on the byte-identical converted WAV — produces the full, correct transcript. The one observable difference: when Scriberr runs WhisperX, the subprocess never allocates any GPU memory (nvidia-smi VRAM stays flat), whereas a manual run of the same command does.

Environment

• Image: ghcr.io/rishikanthc/scriberr-cuda:latest (pulled July 2026)
• Docker, runtime: nvidia, NVIDIA_VISIBLE_DEVICES=all, PUID=99/PGID=100
• GPU: GTX 1660 SUPER (6 GB, Turing); nvidia-smi -L works inside the container as appuser (uid 99)
• WhisperX 3.0.0, --compute_type int8, --device cuda
• Input: normal phone .m4a, ~86 s, mean −25.9 dB / peak −0.6 dB (clearly audible speech)

Symptom

{"text":" Thank you.  Thank you.  Thank you.",
 "segments":[{"start":9.183,"end":9.723,"text":" Thank you."}, ...],
 "model_used":"large-v3","metadata":{"model_id":"whisperx","model_version":"3.0.0","device":"cuda"}}

Scriberr logs segments=3 words=6 for an 86 s clip containing a full conversation.

Exact command Scriberr runs (from its logs)

uv run --native-tls --project /app/whisperx-env/WhisperX python -m whisperx \
  /app/data/uploads/<id>_converted.wav --output_dir data/temp/whisperx/<id> \
  --model large-v3 --device cuda --device_index 0 --batch_size 8 --compute_type int8 \
  --output_format all --verbose True --task transcribe --language en \
  --vad_method pyannote --vad_onset 0.500 --vad_offset 0.363 \
  --diarize --diarize_model pyannote/speaker-diarization-3.1 \
  --temperature 0.00 --best_of 5 --beam_size 5 --patience 1.00 \
  --hf_token <HF_TOKEN> --print_progress False

What I ruled out

All of these still produce garbage through Scriberr:

Profile change	Result via Scriberr
model large-v3 → medium → small	garbage (all)
VAD pyannote → silero	garbage (both)
diarization on → off	garbage (both)

Audio/file is provably fine: Scriberr's <id>_converted.wav is correct 16 kHz mono PCM, full length, normal levels, and byte-identical (same md5) across runs. faster-whisper on the same audio → full correct transcript.

The smoking gun

Running Scriberr's exact logged command by hand on Scriberr's byte-identical converted WAV gives the full correct transcript every way I tried:

Invocation	GPU used?	Result
By Scriberr	No (VRAM flat)	" Thank you." ×3
Manual --device cuda, as root	Yes	✅ full transcript
Manual --device cuda, as appuser (uid 99) (same user Scriberr uses)	Yes	✅ full transcript
Manual --device cpu	n/a	✅ full transcript

Same command, same file (md5 match), same container, same user → works by hand, fails under Scriberr. During a Scriberr job the WhisperX subprocess is running (pgrep -f "python -m whisperx") but nvidia-smi --query-compute-apps shows it is not on the GPU. Scriberr does not set CUDA_VISIBLE_DEVICES (no device-masking env in the binary), and the GPU nodes are world-accessible.

Repro

1. Transcribe any multi-minute recording with a WhisperX device=cuda profile.
2. Output = repeated " Thank you.", segments tiny.
3. During the job: nvidia-smi --query-compute-apps=... → WhisperX not on GPU.
4. Grab the temp <id>_converted.wav, run the exact logged command by hand → correct transcript, GPU used.

Hypothesis / ask

Something about how Scriberr spawns the WhisperX subprocess prevents it from engaging CUDA (and it then transcribes ~nothing rather than erroring). Worth checking: the cmd.Env passed to the child vs. an interactive shell; whether the startup "Initializing registered models in parallel" leaves CUDA/torch in a bad state the child inherits; and how the produced JSON is parsed back.

Workaround

Setting the profile to device=cpu transcribes correctly (CPU is only modestly slower, fine for async processing). GPU via Scriberr remains broken.

Yep... I asked Ai for help since I gave up trying to figure it out. This is what it came out with. Any help?