Automatic Speech Recognition
Transformers
PyTorch
Estonian
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use TalTechNLP/whisper-medium-et with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TalTechNLP/whisper-medium-et with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TalTechNLP/whisper-medium-et")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("TalTechNLP/whisper-medium-et") model = AutoModelForSpeechSeq2Seq.from_pretrained("TalTechNLP/whisper-medium-et") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -30,6 +30,7 @@ model-index:
|
|
| 30 |
name: Common Voice 8
|
| 31 |
type: mozilla-foundation/common_voice_8_0
|
| 32 |
config: et
|
|
|
|
| 33 |
metrics:
|
| 34 |
- name: Test WER
|
| 35 |
type: wer
|
|
|
|
| 30 |
name: Common Voice 8
|
| 31 |
type: mozilla-foundation/common_voice_8_0
|
| 32 |
config: et
|
| 33 |
+
split: test
|
| 34 |
metrics:
|
| 35 |
- name: Test WER
|
| 36 |
type: wer
|