Instructions to use rossevine/Model_S_P_Wav2Vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_S_P_Wav2Vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_S_P_Wav2Vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_S_P_Wav2Vec2") model = AutoModelForCTC.from_pretrained("rossevine/Model_S_P_Wav2Vec2") - Notebooks
- Google Colab
- Kaggle
add tokenizer
Browse files
runs/Aug14_22-25-09_hpc-Aquarium2/events.out.tfevents.1692026717.hpc-Aquarium2.18885.2
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 sha256:5bb2fcca92802b3ad282b5332df59d4926a0bf5dda67b62a4d54e8a80667130d
|
| 3 |
+
size 5073
|