Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
language:
|
3 |
+
- bn
|
4 |
+
metrics:
|
5 |
+
- accuracy
|
6 |
+
pipeline_tag: audio-classification
|
7 |
+
---
|
8 |
+
# whisper-tiny-bn-emo
|
9 |
+
This model is a fine-tuned version of shhossain/whisper-tiny-bn on the Unknown dataset. It achieves the following results on the evaluation set:
|
10 |
+
|
11 |
+
Loss: 0.1842
|
12 |
+
Accuracy: 0.9357
|
13 |
+
|
14 |
+
## Model Info
|
15 |
+
It detects 7 basic human emotions on `Bengali Language`.
|
16 |
+
- `ANGRY`
|
17 |
+
- `DISGUST`
|
18 |
+
- `FEAR`
|
19 |
+
- `HAPPY`
|
20 |
+
- `NEUTRAL`
|
21 |
+
- `SAD`
|
22 |
+
- `SURPRISE`
|
23 |
+
|
24 |
+
## Usage
|
25 |
+
```python
|
26 |
+
from transformers import pipeline
|
27 |
+
|
28 |
+
pipe = pipeline("audio-classification", model="shhossain/whisper-tiny-bn-emo")
|
29 |
+
|
30 |
+
pipe("audio_file.wav")
|
31 |
+
```
|