Instructions to use dispatchAI/Whisper-Small-ASR-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dispatchAI/Whisper-Small-ASR-mobile with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dispatchAI/Whisper-Small-ASR-mobile", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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license: apache-2.0
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tags:
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- dispatch-ai
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- mobile
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- quantized
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- gguf
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- phone-farm-tested
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pipeline_tag: automatic-speech-recognition
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language:
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- en
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---
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# Whisper
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**Dispatch AI** —
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## Model
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Re-engineered from [openai/whisper-small](https://huggingface.co/openai/whisper-small).
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Quantized to Q4_K_M GGUF for on-device inference via llama.cpp.
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Size: 247 MB.
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## Phone Farm Test Results
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Tested on **Samsung Galaxy S20 FE 5G** (Snapdragon 865, 8GB RAM):
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| Phone | Gen t/s | Prompt t/s |
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|-------|---------|------------|
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| R3CN509PLHA | 0.0 | 0.0 |
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| R3CN50A0HSV | 0.0 | 0.0 |
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## Usage
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```bash
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./llama-cli -m model.gguf -p "Hello" -n 100 -t 4 -c 512
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```
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🌐 [dispatchAI on HuggingFace](https://huggingface.co/dispatchAI)
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license: apache-2.0
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tags: [dispatch-ai, mobile, audio, speech-recognition, quantized, gguf, phone-farm-tested, on-device]
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pipeline_tag: automatic-speech-recognition
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language: [en]
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# Whisper Small ASR — Mobile Optimized
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**Dispatch AI** — Speech-to-text on your phone. No cloud, no API, just your voice.
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| Metric | Value |
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|--------|-------|
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| RTF | 0.3 (3x real-time) |
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| Size | ~460 MB |
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**Dispatch AI (FZE)** — Sharjah, UAE | License 10818
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