Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny") - Notebooks
- Google Colab
- Kaggle
QORA-STT - Pure Rust Speech-to-Text Based on openai/whisper-tiny
#47
by drdraq - opened
Pure Rust inference engine for OpenAI's Whisper Tiny. No Python, no CUDA, no external dependencies. Single executable + binary weights = portable speech-to-text on any machine.
Based on openai/whisper-tiny (MIT License).
Try: https://huggingface.co/qoranet/QORA-STT
Transcribe an audio file (English)
qora-stt.exe --model-path . --load model.qora-stt --audio recording.wav
Specify language
qora-stt.exe --model-path . --load model.qora-stt --audio recording.wav --language french
Save transcription to file
qora-stt.exe --model-path . --load model.qora-stt --audio recording.wav --output transcript.txt