Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-tiny.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny.en") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -26,7 +26,7 @@ model-index:
|
|
| 26 |
metrics:
|
| 27 |
- name: Test WER
|
| 28 |
type: wer
|
| 29 |
-
value:
|
| 30 |
- task:
|
| 31 |
name: Automatic Speech Recognition
|
| 32 |
type: automatic-speech-recognition
|
|
|
|
| 26 |
metrics:
|
| 27 |
- name: Test WER
|
| 28 |
type: wer
|
| 29 |
+
value: 8.4372112320138
|
| 30 |
- task:
|
| 31 |
name: Automatic Speech Recognition
|
| 32 |
type: automatic-speech-recognition
|