Instructions to use garnagar/whisper-tiny-libirClean-vs-commonNative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use garnagar/whisper-tiny-libirClean-vs-commonNative with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="garnagar/whisper-tiny-libirClean-vs-commonNative")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("garnagar/whisper-tiny-libirClean-vs-commonNative") model = AutoModelForSpeechSeq2Seq.from_pretrained("garnagar/whisper-tiny-libirClean-vs-commonNative") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 1000
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 151097331
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5fb74a47d25aaeb0cafc05a4f1a6ab628abe874182b10f7d8a0bb5742d46bea6
|
| 3 |
size 151097331
|
runs/Dec07_09-46-42_73abc972721c/events.out.tfevents.1670406681.73abc972721c.76.0
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:942fa508e1e62790a746a828aac3c5b9ecd054a52011f067caca698397189e28
|
| 3 |
+
size 13811
|