Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Chinese
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use seiching/whisper-large-seiching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seiching/whisper-large-seiching with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="seiching/whisper-large-seiching")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("seiching/whisper-large-seiching") model = AutoModelForSpeechSeq2Seq.from_pretrained("seiching/whisper-large-seiching", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 3500
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 6173655480
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c29c03f6c489ce50410b865dff8c3290b3d6f0c82031197d899db285835020d2
|
| 3 |
size 6173655480
|
runs/Jan29_16-17-18_jupyter-207331-40cpc-2ecom-2etw/events.out.tfevents.1706516251.jupyter-207331-40cpc-2ecom-2etw.537.1
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 sha256:8c94822665172b147bd17cd0457de77a1679771f615ae298eff9bbeaf6c81a03
|
| 3 |
+
size 30345
|