Making automatic speech recognition work on large files with Wav2Vec2 in 🤗 Transformers

Summary

Transformer-based automatic speech recognition models like Wav2Vec2 crash on long audio inputs because the O(n²) attention mechanism rapidly exhausts GPU memory. Simple audio chunking avoids out-of-memory errors but causes poor transcription accuracy at chunk boundaries due to a lack of surrounding context. To resolve this, Hugging Face Transformers leverages the Connectionist Temporal Classification architecture to process overlapping audio chunks with configurable strides. The pipeline discards low-quality logits at chunk edges and chains the remaining central predictions together to reconstruct a seamless transcript. This striding technique operates out of the box with language model-augmented pipelines and adapts directly to low-latency live audio streaming.

Takeaways

1. 1

   Transformer attention complexity scales quadratically with sequence length, causing unchunked Wav2Vec2 speech recognition inference to crash with out-of-memory errors on long audio files.

2. 2

   Connectionist Temporal Classification (CTC) maps each audio frame to a letter prediction logit, allowing pipelines to drop edge logits from overlapping chunks and concatenate the high-quality central outputs.

3. 3

   The chunking and striding method applies directly to language model-augmented Wav2Vec2 checkpoints and enables live streaming inference by processing overlapping incoming audio frames.