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bartelds/
whisper-dro-set4-dro

Name: whisper-dro-set4-dro
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Whisper CTC-DRO ASR model - set 4

This repository contains an automatic speech recognition (ASR) model fine-tuned from openai/whisper-large-v3 using the principles of CTC-DRO applied to Whisper's seq2seq architecture. The model was trained on balanced training data from set 4 (slv, snd, spa, urd).

DRO hyperparameters: eta=1e-3, alpha=0.1, aggregation: mean

Intended Use

This model is intended for multilingual ASR. Users can run inference using the HuggingFace Transformers library:

python

import torch
import librosa
from transformers import WhisperForConditionalGeneration, WhisperProcessor

model = WhisperForConditionalGeneration.from_pretrained("bartelds/whisper-dro-set4-dro")
processor = WhisperProcessor.from_pretrained("bartelds/whisper-dro-set4-dro")
model.eval()

audio, sr = librosa.load("input.wav", sr=16000)
inputs = processor.feature_extractor(audio, sampling_rate=16000, return_tensors="pt")

with torch.no_grad():
    generated = model.generate(input_features=inputs.input_features)

text = processor.tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
print("Recognized text:", text)

How to Use

Install dependencies: pip install transformers torch librosa
Load the model and processor using from_pretrained() as shown above.
The model supports multilingual transcription -- see the training repository for evaluation details.

Training

Base model: openai/whisper-large-v3
Training code: whisper-dro
Paper: CTC-DRO