Palash Debnath
e7911e3e97
Review follow-up. The budget matched model names by substring, so a custom repo whose name contains "turbo", "small" or "base" (or a word such as "database") got a reduced budget and could be admitted to CUDA without enough memory. Reduced budgets now apply only to the exact OpenAI checkpoint ids, .en variants included. Any other repository, fine-tunes included, keeps the conservative 5.0 GB, as the engine doc says.
3.3 KiB
VoiceStudio — PyTorch Whisper Engine
Whisper through the plain transformers pipeline, riding torch itself. No
extra install — transformers ships with the app — and because it runs on
torch's own stack (including torch's bundled cuDNN 9), it works on machines
where the CTranslate2 engines can't load. It is also the engine that
genuinely uses AMD ROCm GPUs, so auto-detect picks it on ROCm hosts
(#1529).
Selecting it
- Model Catalogue, ASR tab → Use on the PyTorch Whisper
row, or
OMNIVOICE_ASR_BACKEND=pytorch-whisper. OMNIVOICE_ASR_BACKEND=omnivoiceis accepted as a compatibility alias and selects this same PyTorch-native ASR path on ROCm hosts.- Auto-detect picks it on ROCm, and as the last resort everywhere else.
Best at
- ROCm dubbing/transcription — the only Whisper engine that uses the HIP GPU (CTranslate2 has no HIP build, MLX is Apple-only).
- Rescue engine when whisperx/faster-whisper can't load — e.g. the missing-cuDNN-8 case (#255) — since it needs neither CTranslate2 nor cuDNN 8.
For lip-sync-grade word timing prefer whisperx or mlx-whisper; this engine returns the pipeline's own word timestamps.
Platform support
CUDA, Apple Silicon (MPS), ROCm (HIP), and CPU — wherever torch runs, on macOS, Windows, and Linux.
Model selection
OMNIVOICE_PYTORCH_ASR_MODEL — default openai/whisper-large-v3-turbo. Any
transformers-format Whisper repo works. Weights download on first load — see
downloading-models.
VRAM preflight
Loading a model onto a nearly-full card "succeeds", and then the first transcribe runs out of memory with zero segments. So on CUDA the engine checks free VRAM before loading and uses the CPU instead when the card is too full (flush the TTS model to restore GPU-speed ASR).
For the OpenAI Whisper checkpoints, the budget follows the model it loads in fp16: the weights, plus about 1.5 GB of working memory and 0.5 GB of headroom. Any other repository, including a fine-tune, keeps 5 GB.
| Model | Free VRAM needed |
|---|---|
openai/whisper-large-v3-turbo (default) |
3.6 GB |
openai/whisper-large, -large-v2, -large-v3 |
5 GB |
openai/whisper-medium / -small / -base / -tiny (and .en) |
3.5 / 2.5 / 2.2 / 2.1 GB |
| any other repository | 5 GB |
A 6 GB card with nothing else loaded runs the default model on the GPU
(#2041). Disable
the check with OMNIVOICE_ASR_VRAM_PREFLIGHT=0.
Quirks
- If the pipeline fails to import (
AutoFeatureExtractorerrors), the cause is either an incomplete transformers install or a torch/torchvision version mismatch — the error message names the exact reinstall command; the trio has to move together at the pinned versions (#549, #1376). - Transcribes are time-bounded like every local engine:
OMNIVOICE_TRANSCRIBE_CHUNK_TIMEOUT_S(default 120 s per dub chunk),OMNIVOICE_ASR_TRANSCRIBE_TIMEOUT_S(default 300 s whole-file).
Speed comparisons across engines live in performance.