Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers

Summary

Fine-tuning the multilingual XLS-R speech model adapts pretrained cross-lingual audio representations to automatic speech recognition tasks with limited labeled data. The demonstration configures a Wav2Vec2-XLS-R-300M checkpoint using Connectionist Temporal Classification on roughly four hours of validated Turkish audio from Common Voice. Building the pipeline requires pairing a Wav2Vec2FeatureExtractor for audio signals with a custom Wav2Vec2CTCTokenizer derived from dataset transcriptions. Training with Hugging Face Trainer over 3,200 steps decreases the validation word error rate from 0.7000 down to 0.3195. While the resulting transcription demonstrates recognizable phonetic alignment, output quality can be further improved by extending training schedules, refining preprocessing, and adding language model decoding.

Takeaways

1. 1

   XLS-R learns contextualized speech representations across 128 languages using self-supervised masked feature prediction, with pretrained checkpoints scaling from 300M to 2B parameters.

2. 2

   For ASR downstream tasks, a linear layer mapping context representations to vocabulary tokens is added atop the pretrained transformer and trained with Connectionist Temporal Classification.

3. 3

   To handle GPU out-of-memory errors during training, reduce per_device_train_batch_size to 8 or less and increase gradient_accumulation.