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Documentation Index

Fetch the complete documentation index at: https://docs.pipeshub.com/llms.txt

Use this file to discover all available pages before exploring further.

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Whisper (Local) Speech to Text Configuration

Whisper Local STT Configuration Interface The Whisper local STT configuration screen in PipesHub where you’ll select your model size and compute settings PipesHub supports running Whisper entirely on your own infrastructure using the faster-whisper library. No API key is required — all audio processing happens locally, keeping your data private and eliminating per-request API costs.
Whisper model weights are downloaded from HuggingFace on first use. Ensure the PipesHub backend host has internet access during initial setup. The faster-whisper Python package must also be installed on the backend host.

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Required Fields

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Model *

Select the Whisper model size from the dropdown. Larger models are more accurate but require more memory and processing time.
ModelParametersNotes
tiny39MFastest, lowest accuracy. Good for simple, clear audio.
base74MDefault. Good balance of speed and accuracy for most use cases.
small244MBetter accuracy than base, moderate speed.
medium769MHigh accuracy, slower. Suitable for professional transcription.
large-v21.55BVery high accuracy. Requires significant RAM/VRAM.
large-v31.55BNewest large model, highest accuracy. Requires ~3 GB disk space and significant RAM/VRAM.
distil-large-v3756MApproximately 6x faster than large-v3 with similar accuracy. Recommended for production use.
How to choose a model size:
  • For real-time voice input where speed matters most, start with base or small
  • For high-accuracy transcription of important audio, use distil-large-v3 or large-v3
  • If you have a GPU available, larger models become much more practical

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Optional Fields

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Device

Controls which hardware the model runs on.
  • Auto (default) — PipesHub automatically uses a GPU if one is available, otherwise falls back to CPU
  • CPU — forces CPU inference (slower but works on any machine)
  • CUDA — forces NVIDIA GPU inference (requires a compatible NVIDIA GPU with CUDA support)

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Compute Type

Controls the numerical precision used during inference. Lower precision uses less memory and runs faster; higher precision is more accurate.
Compute TypeBest forNotes
int8CPU (default)Lowest memory usage. Recommended when running on CPU.
int8_float16GPUGood balance of speed and memory on GPU.
float16GPUFaster on GPU with moderate memory usage.
float32High precisionHighest numerical accuracy. Slowest and most memory-intensive.

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Model Cache Directory

A filesystem path on the backend host where downloaded model weights will be stored. Leave blank to use the faster-whisper default cache location. Example: /data/whisper-models This is useful if you want to pre-download models to a specific disk, or if the default cache location has limited storage.

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Configuration Steps

As shown in the image above:
  1. Click Configure on the Whisper (Local) provider card in the STT tab
  2. Select your desired Model size from the dropdown (marked with *)
  3. (Optional) Set Device — leave on Auto unless you need to force CPU or GPU
  4. (Optional) Set Compute Type — leave on int8 for CPU or int8_float16 for GPU
  5. (Optional) Set a Model Cache Directory to control where weights are stored
  6. (Optional) Set a Model Friendly Name
  7. Click Add Model to save your configuration
On first use after saving, PipesHub will download the selected model weights from HuggingFace. This may take several minutes depending on model size and network speed.

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Usage Considerations

  • No API costs — all transcription runs locally on your infrastructure
  • The large-v3 model requires approximately 3 GB of disk space and at least 4 GB of VRAM (or 8 GB of RAM for CPU inference)
  • distil-large-v3 offers the best accuracy-to-speed ratio and is recommended for most production deployments
  • First use requires an internet connection to download the model weights; subsequent uses work offline
  • Whisper supports transcription in 50+ languages automatically

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Troubleshooting

  • If the health check fails, confirm the faster-whisper Python package is installed on the backend host
  • If model weights fail to download, check that the backend host has internet access and can reach HuggingFace (huggingface.co)
  • If transcription is very slow, consider switching to a smaller model or enabling GPU inference with CUDA
  • If you get out-of-memory errors, switch to a smaller model or use int8 compute type
  • Ensure the Model Cache Directory (if set) exists and is writable by the PipesHub process
For additional support, refer to the faster-whisper documentation or contact PipesHub support.