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---
license: mit
datasets:
- mozilla-foundation/common_voice_17_0
- CSTR-Edinburgh/vctk
base_model:
- openai/whisper-small
---
# Model Card for Model ID
Model name: Amirjab21/accent-classifier
Task: Accent classification (audio → accent label)
Supported input: 16 kHz mono audio waveform (float32 or int16) in NumPy array
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
- **Developed by:** Amir Jabarivasal
- **Finetuned from model [optional]:** Openai/whisper-small
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/Amirjab21/accents
- **Paper [optional]:** https://amirjab21.github.io/?blog=0
- **Demo [optional]:** Accentgame.xyz
## Uses
Classify accents
## How to Get Started with the Model
Use the code below to get started with the model.
```
ID_TO_ACCENT = {
0: "Scottish",
1: "English",
2: "Indian",
3: "Irish",
4: "Welsh",
5: "New Zealand",
6: "Australian",
7: "South African",
8: "Canadian",
9: "NorthernIrish",
10: "American",
11: "South East Asia",
12: "Eastern Europe",
13: "East Asia",
14: "Nordic",
15: "France",
16: "Southern Europe",
17: "Germany",
18: "West Indies",
19: "Western Africa",
20: "South Asia",
}
import soundfile as sf
import torch
from scipy import signal
audio_array, sr = sf.read(audio_path)
if audio_array.ndim > 1:
audio_array = audio_array.mean(axis=1)
if sr != 16000:
audio_array = signal.resample(audio_array, int(len(audio_array)*16000/sr))
input_features = processor(audio_array, sampling_rate=16000, return_tensors="pt").input_features
output, pooled_embed = model(input_features)
probabilities = torch.nn.functional.softmax(output, dim=1)
predictions = torch.argmax(probabilities, dim=1)
predicted_accent = ID_TO_ACCENT[predictions.item()]
accent_probabilities = {ID_TO_ACCENT[i]: prob.item() for i, prob in enumerate(probabilities[0])}
```
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