Utility to compare output transcript to reference

Uses Ukkonen algorithm to efficiently compute Leveshtein distance and character error rate (CER).

Additionally it can output alignment information.

Usage

Usage: transcribe-compare [OPTIONS]
  Transcription compare tool provided by VoiceGain
Options:
  -r, --reference TEXT            source string
  -o, --output TEXT               target string
  -R, --reference_file FILENAME   source file path
  -O, --output_file FILENAME      target file path
  -a, --alignment                 Do you want to see the alignment result?
                                  True/False
  -e, --error_type [CER|WER]
  -j, --output_format [JSON|TABLE]
  -l, --to_lower                  Do you want to lower all the words?
                                  True/False
  -p, --remove_punctuation        Do you want to remove all the punctuation?
                                  True/False
  -P, --to_save_plot              Do you want to see the windows? True/False
  -s, --to_edit_step INTEGER      Please enter the step
  -w, --to_edit_width INTEGER     Please enter the width
  --help                          Show this message and exit.

Dependencies

click
inflect
re
nltk
metaphone
matplotlib

Sample Commands

python transcribe-compare -R sample_data/The_Princess_and_the_Pea-reference.txt -O sample_data/The_Princess_and_the_Pea-output-1.txt -e CER

HTML Output

HTML Output: Single Compare - Stats

There is a script available that using transcribe-compare to compare results from Voicegaing and Google recognizers. You can find it here: https://github.com/voicegain/platform/tree/master/utility-scripts/test-transcribe

Acknowledgements

Contributed by VoiceGain.

VoiceGain provides Deep-Neural-Network-based Speech-to-Text (ASR) available in Cloud and also as an Edge Deployment.
Accessible via RESTful Web API or MRCP v2 interface.
Is suitable both for continuous large-vocabulary transcription (real-time or off-line) and for recognition using context-free grammars (e.g. GRXML).
In addition to this VoiceGain platform provides API-driven method to modify models used in speech-to-text. It is possible to modify language model, pronunciation model, and the acoustic DNN model.