Uses Ukkonen algorithm to efficiently compute Leveshtein distance and character error rate (CER).
Additionally it can output alignment information.
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.
- click
- inflect
- re
- nltk
- metaphone
- matplotlib
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
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
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.