Beam search decoder

By default validation is carried out using a greedy decoder. To instead use a beam decoder, run

./scripts/val.sh --decoder=beam

which runs with a default beam width of 4. To change the beam width, run, for example

./scripts/val.sh --decoder=beam --beam_width=8

Note

All of the beam decoder options described in this page are available in train.sh as well as val.sh.

The time synchronous beam decoder utilises an optimised version of beam search - adaptive beam search - which reduces decoding compute and latency by reducing the number of beam expansions to consider, without degrading WER. Three hypothesis pruning methods are employed:

  1. Hypotheses with a score less than beam_prune_score_thresh (default 0.4) below the best hypothesis’ score are pruned.
  2. Tokens with a logprob score less than beam_prune_topk_thresh (default 1.5) below the most likely token are ignored.
  3. Hypotheses are depth-pruned when their most recent common ancestor is further than beam_final_emission_thresh seconds older than the best hypothesis. This has the effect of forcing finals at at-least this interval, which reduces tail emission latencies.

Reducing beam_prune_score_thresh, beam_prune_topk_thresh, and beam_final_emission_thresh increases pruning aggressiveness; setting them < 0 disables pruning.

Adaptive beam search dynamically adjusts computation based on model confidence, using more compute when uncertain and behaving almost greedily when confident.

Softmax temperature

The beam decoder applies a softmax temperature to the logits. By default the --temperature=1.4 as this was found to improve WER across a range of configurations. Increasing the temperature will increase the beam diversity and make the greedy path less likely.

Fuzzy top-k logits

When using --decoder=beam, the model first calculates the logits for all classes (or tokens), applies the log-softmax function to get log probabilities, and then selects the top beam-width tokens to expand the beam.

However, in the hardware-accelerated solution, the I/O of sending the full vocab-size logits tensor from the FPGA to the CPU is a bottleneck. To address this, the hardware-accelerated solution sends a reduced set of logits to the CPU. Specifically, it sends the highest-value logits within some local block of the logits tensor. This enables a ‘fuzzy top-k’ operation which is approximate to the full top-k operation with some small difference.

Our experiments show that using the reduced logits tensor (fuzzy top-k logits) does not impact the model’s WER performance.

Using fuzzy top-k logits

To use a reduced tensor implementation similar to the accelerated version, run the following command:

./scripts/val.sh --num_gpus=2 --decoder=beam --fuzzy_topk_logits

Please note that the evaluation time will increase by ~30% compared to standard beam search, so it is disabled by default.

N-gram language models

N-gram language models are used with beam decoding to improve WER. This is on by default and described in more detail in the N-gram language model documentation.

Keyword boosting

Keyword boosting is available with the beam decoder and described in more detail in the keyword boosting documentation.