Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
hf-asr-leaderboard
whisper-event
Eval Results (legacy)
Instructions to use softcatala/whisper-base-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softcatala/whisper-base-ca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="softcatala/whisper-base-ca")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("softcatala/whisper-base-ca") model = AutoModelForSpeechSeq2Seq.from_pretrained("softcatala/whisper-base-ca") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 40000
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 290456599
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7f437f61f4bceac6b0941879b65ba55e39c220355f39da029ae4d305e9510a25
|
| 3 |
size 290456599
|
runs/Dec23_22-06-41_ip-172-31-28-50/events.out.tfevents.1671833219.ip-172-31-28-50.2290.0
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:b390845eb7f264a6ffd7948d374364face6592540ecf4add352c3e4b3c761ceb
|
| 3 |
+
size 261403
|