Instructions to use juierror/whisper-tiny-thai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juierror/whisper-tiny-thai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="juierror/whisper-tiny-thai")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("juierror/whisper-tiny-thai") model = AutoModelForSpeechSeq2Seq.from_pretrained("juierror/whisper-tiny-thai") - Notebooks
- Google Colab
- Kaggle
Upload WhisperForConditionalGeneration
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "./whisper-tiny-thai/checkpoint-
|
| 3 |
"activation_dropout": 0.0,
|
| 4 |
"activation_function": "gelu",
|
| 5 |
"apply_spec_augment": false,
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "./whisper-tiny-thai/checkpoint-20000",
|
| 3 |
"activation_dropout": 0.0,
|
| 4 |
"activation_function": "gelu",
|
| 5 |
"apply_spec_augment": false,
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 151098921
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:569221aec453d6f43726e15a933916fa1cdcb6f060990d44c31e29db72aa9c21
|
| 3 |
size 151098921
|