Automatic Speech Recognition
Safetensors
MLX
English
mlx-audio
whisper
audio
hf-asr-leaderboard
speech-to-text
speech-to-speech
speech
speech generation
stt
Eval Results (legacy)
8-bit precision
Instructions to use mlx-community/whisper-tiny.en-asr-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/whisper-tiny.en-asr-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir whisper-tiny.en-asr-8bit mlx-community/whisper-tiny.en-asr-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
mlx-community/whisper-tiny.en-asr-8bit
This model was converted to MLX format from openai/whisper-tiny.en using mlx-audio version 0.2.10.
Refer to the original model card for more details on the model.
Use with mlx-audio
pip install -U mlx-audio
CLI Example:
python -m mlx_audio.stt.generate --model mlx-community/whisper-tiny.en-asr-8bit --audio "audio.wav"
Python Example:
from mlx_audio.stt.utils import load_model
from mlx_audio.stt.generate import generate_transcription
model = load_model("mlx-community/whisper-tiny.en-asr-8bit")
transcription = generate_transcription(
model=model,
audio_path="path_to_audio.wav",
output_path="path_to_output.txt",
format="txt",
verbose=True,
)
print(transcription.text)
- Downloads last month
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Model size
11M params
Tensor type
F16
·
U32 ·
Hardware compatibility
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8-bit
Evaluation results
- Test WER on LibriSpeech (clean)test set self-reported8.437
- Test WER on LibriSpeech (other)test set self-reported14.858