iscr | Spoken Content Retrieval

by tzuhsial Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | iscr Summary

iscr is a Python library. iscr has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Spoken Content Retrieval

Support

Quality

Security

License

Reuse

Support

iscr has a low active ecosystem.

It has 0 star(s) with 1 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

iscr has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of iscr is current.

Quality

iscr has no bugs reported.

Security

iscr has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

iscr is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

iscr releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of iscr

Get all kandi verified functions for this library.

iscr Key Features

No Key Features are available at this moment for iscr.

iscr Examples and Code Snippets

No Code Snippets are available at this moment for iscr.

Community Discussions

No Community Discussions are available at this moment for iscr.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install iscr

Run baseline result on dnn recognition results with jieba segmentation. Now we have our jieba segmented query at './data/PTV.query.jieba.txt' and transcripts at './transcripts/PTV.dnn.onebest.jieba'. The indexing script is located at './scripts/run_indexing_for_segmented_transcripts.py'. Indexing will create 3 pickle files in the collection directory. The scripts is located at './data/build_queries.py'. Now we have our query_and_answer pickle at ./queries/PTV.dnn.onebest.jieba.query.pickle. Ranking script is located at './scripts/run_klranker_baseline.py'. Script is located at './scripts.run_pseudo_relevant_feedback.py'. This script assumes top 10 ranked documents are relevant and performs query regularized mixture expansion for second pass ranking. Please see './iscr/dialoguemanager.py' for details.
./data/PTV.query.txt (query file, an unsegmented query on each line)
./data/PTV_transcription_charSeg (directory of dnn recognition transcripts)
Segment query and transcripts with jieba
Run indexing with our indexer
lex.pickle
document.pickle
indices.pickle
Create query & answer pickle file for ranking evaluation
Run ranking script
Psuedo relevance feedback
Code is written in python35. To use it in another project, first execute.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: