Automatic Speech Recognition
Transformers
PyTorch
whisper
audio
speech
wav2vec2
Eval Results (legacy)
Instructions to use devasheeshG/whisper_medium_fp16_transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devasheeshG/whisper_medium_fp16_transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devasheeshG/whisper_medium_fp16_transformers")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devasheeshG/whisper_medium_fp16_transformers") model = AutoModelForSpeechSeq2Seq.from_pretrained("devasheeshG/whisper_medium_fp16_transformers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
cfaec9e
1
Parent(s): b656fa6
README.md
CHANGED
|
@@ -13,11 +13,15 @@ model-index:
|
|
| 13 |
- name: whisper_medium_fp16_transformers
|
| 14 |
results:
|
| 15 |
- task:
|
| 16 |
-
|
| 17 |
-
|
| 18 |
dataset:
|
| 19 |
type: common_voice
|
| 20 |
-
name: Common Voice (14.0) (Hindi)
|
|
|
|
|
|
|
|
|
|
|
|
|
| 21 |
metrics:
|
| 22 |
- type: wer
|
| 23 |
value: 54.97
|
|
|
|
| 13 |
- name: whisper_medium_fp16_transformers
|
| 14 |
results:
|
| 15 |
- task:
|
| 16 |
+
type: automatic-speech-recognition
|
| 17 |
+
name: Automatic Speech Recognition
|
| 18 |
dataset:
|
| 19 |
type: common_voice
|
| 20 |
+
name: Common Voice (14.0) (Hindi)
|
| 21 |
+
config: hi
|
| 22 |
+
split: test
|
| 23 |
+
args:
|
| 24 |
+
language: hi
|
| 25 |
metrics:
|
| 26 |
- type: wer
|
| 27 |
value: 54.97
|