Instructions to use rav2040/wav2vec2-base-timit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rav2040/wav2vec2-base-timit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rav2040/wav2vec2-base-timit")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rav2040/wav2vec2-base-timit") model = AutoModelForCTC.from_pretrained("rav2040/wav2vec2-base-timit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload training_args.bin
Browse files- training_args.bin +3 -0
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f0f4bd209d5d6f4bca225536fb772f0a8143dc9fd09772fdcbc5dd2552f988a5
|
| 3 |
+
size 2991
|