AI BriefingImplementing ASR for Long Audio Files with Wav2Vec2
Key point
This explains how to leverage the characteristics of the CTC architecture to perform high-quality speech recognition on long audio files without memory issues.
Details
Transformer-based models like Wav2Vec2 have memory complexity that increases as $O(n^2)$ with sequence length, which can cause out-of-memory (OOM) errors when processing very long audio files.
The Simple Chunking approach, which simply splits the audio into short segments for processing, has the drawback that context is lost at the chunk boundaries, causing recognition quality to drop sharply.
To solve this, you can use the Chunking with stride approach, which leverages Wav2Vec2's CTC (Connectionist Temporal Classification) architecture.
- Overlapping chunks are used so that the model retains sufficient context.
- By excluding the logits generated at the chunk boundaries and concatenating only the central portions, results of quality similar to processing the entire file at once can be obtained.
This feature can be easily applied simply by setting the chunk_length_s parameter in the pipeline of the Hugging Face transformers library.
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