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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Link
Browse files
README.md
CHANGED
|
@@ -49,7 +49,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 49 |
|
| 50 |
# openai/whisper-base
|
| 51 |
|
| 52 |
-
This is an automatic speech recognition model that also does punctuation and casing.
|
| 53 |
|
| 54 |
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 ca dataset.
|
| 55 |
It achieves the following results on the evaluation set:
|
|
|
|
| 49 |
|
| 50 |
# openai/whisper-base
|
| 51 |
|
| 52 |
+
This is an automatic speech recognition model that also does punctuation and casing. This model is for research only, **we do not recommend using this model on production environments**. See our [learnings](https://huggingface.co/softcatala/whisper-small-ca/blob/main/TRAINING.md) when training these models.
|
| 53 |
|
| 54 |
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 ca dataset.
|
| 55 |
It achieves the following results on the evaluation set:
|