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Fangjun Kuang 2022-04-29 14:01:24 +08:00
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@ -85,7 +85,6 @@ Decoding commands are:
--max-sym-per-frame 1
# fast beam search
for epoch in 27; do
for avg in 10 12; do
./pruned_transducer_stateless3/decode.py \
@ -100,6 +99,34 @@ for epoch in 27; do
done
```
The following table shows the
[Nbest oracle WER](http://kaldi-asr.org/doc/lattices.html#lattices_operations_oracle)
for fast beam search.
| epoch | avg | num_paths | nbest_scale | test-clean | test-other |
|-------|-----|-----------|-------------|------------|------------|
| 27 | 10 | 50 | 0.5 | 0.91 | 2.74 |
| 27 | 10 | 50 | 0.8 | 0.94 | 2.82 |
| 27 | 10 | 50 | 1.0 | 1.06 | 2.88 |
| 27 | 10 | 100 | 0.5 | 0.82 | 2.58 |
| 27 | 10 | 100 | 0.8 | 0.92 | 2.65 |
| 27 | 10 | 100 | 1.0 | 0.95 | 2.77 |
| 27 | 10 | 200 | 0.5 | 0.81 | 2.50 |
| 27 | 10 | 200 | 0.8 | 0.85 | 2.56 |
| 27 | 10 | 200 | 1.0 | 0.91 | 2.64 |
| 27 | 10 | 400 | 0.5 | N/A | N/A |
| 27 | 10 | 400 | 0.8 | 0.81 | 2.49 |
| 27 | 10 | 400 | 1.0 | 0.85 | 2.54 |
The Nbest oracle WER is computed using the following steps:
- 1. Use `fast_beam_search` to produce a lattice.
- 2. Extract `N` paths from the lattice using [k2.random_path](https://k2-fsa.github.io/k2/python_api/api.html#random-paths)
- 3. [Unique](https://k2-fsa.github.io/k2/python_api/api.html#unique) paths so that each path
has a distinct sequence of tokens
- 4. Compute the edit distance of each path with the ground truth
- 5. The path with the lowest edit distance is the final output and is used to
compute the WER
### LibriSpeech BPE training results (Pruned Transducer 2)
[pruned_transducer_stateless2](./pruned_transducer_stateless2)