Instructions to use GlowLED/whisper-tiny-minds14-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GlowLED/whisper-tiny-minds14-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GlowLED/whisper-tiny-minds14-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("GlowLED/whisper-tiny-minds14-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("GlowLED/whisper-tiny-minds14-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from GlowLED/whisper-tiny-minds14-en: direct link, hf CLI and curl.
- Browser
- Download file 447 Bytes
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https://huggingface.co/GlowLED/whisper-tiny-minds14-en/resolve/main/README.md
- Command line
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hf download hf://GlowLED/whisper-tiny-minds14-en/README.md
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curl -L -o README.md https://huggingface.co/GlowLED/whisper-tiny-minds14-en/resolve/main/README.md
447 Bytes
metadata
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- automatic-speech-recognition
- whisper
- minds14
- fine-tuned
whisper-tiny-minds14-en
Fine-tuned openai/whisper-tiny on PolyAI/minds14 en-US (first 450 examples as train). Normalised WER 0.2341 (< 0.37). Use with WhisperForConditionalGeneration + WhisperProcessor.
Metrics
- wer: 0.2341
Usage
# automatic-speech-recognition