tiantiaf / voxlect-german-dialect-whisper-small

Whisper-small for German Dialect Classification

Model Description

This model includes the implementation of German dialect classification described in Voxlect: A Speech Foundation Model Benchmark for Modeling Dialect and Regional Languages Around the Globe

Github repository: https://github.com/tiantiaf0627/voxlect

The included German dialects are with speakers from:

[
  "Austria", 
  "German-Non-NRW Area", 
  "German-NRW",
  "Other", 
  "Swiss"
]

How to use this model

Download repo

git clone [email protected]:tiantiaf0627/voxlect

Install the package

conda create -n voxlect python=3.8
cd voxlect
pip install -e .

Load the model

# Load libraries
import torch
import torch.nn.functional as F
from src.model.dialect.whisper_dialect import WhisperWrapper

# Find device
device = torch.device("cuda") if torch.cuda.is_available() else "cpu"

# Load model from Huggingface
model = WhisperWrapper.from_pretrained("tiantiaf/voxlect-german-dialect-whisper-small").to(device)
model.eval()

Prediction

# Label List
dialect_list = [
  "Austria", 
  "German-Non-NRW Area", 
  "German-NRW",
  "Other", 
  "Swiss"
]
    
# Load data, here just zeros as an example
# Our training data filters output audio shorter than 3 seconds (unreliable predictions) and longer than 15 seconds (computation limitation)
# So you need to prepare your audio to a maximum of 15 seconds, 16kHz, and mono channel
max_audio_length = 15 * 16000
data = torch.zeros([1, 16000]).float().to(device)[:, :max_audio_length]
logits, embeddings = model(data, return_feature=True)
    
# Probability and output
dialect_prob = F.softmax(logits, dim=1)
print(dialect_list[torch.argmax(dialect_prob).detach().cpu().item()])

Responsible Use: Users should respect the privacy and consent of the data subjects, and adhere to the relevant laws and regulations in their jurisdictions when using Voxlect.

If you have any questions, please contact: Tiantian Feng ( [email protected] )

❌ Out-of-Scope Use

• Clinical or diagnostic applications
• Surveillance
• Privacy-invasive applications