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Please refer to the above information for Whisper models and paper.
All required packages for Whisper
We use LibriSpeech as an example, but this can be applied to SLURP and DSTC as well.
- Dump features
cd data/LibriSpeech
python dump_feature.py
Note that you need to change setname='train-clean-100'
to the set you want.
- Biasing lists Biasing lists are already prepared:
rareword_error.txt
: error-based biasing list for training
all_rare_words.txt
: full biasing list for inference
Use get_rarewords.py
to get JSON data files containing per-utterance biasing words, e.g. train_clean_100_error.json
which is used for training.
run training script train_large.sh
for training.
run decoding script decoding.sh
for decoding.
score with score.sh
after decoding.
Use error_analysis/get_error_word_count.py
to calculate R-WER, by passing <path_to_results.txt>
as the argument to it.
System | WER | R-WER |
---|---|---|
Whisper large unnormalised | 4.0% | 10.4% |
Whisper large + TCPGen unnormalised | 3.4% | 8.3% |
Whisper large normalised | 2.5% | 8.1% |
Whisper large + TCPGen normalised | 2.3% | 7.0% |