speech-to-text-wavenet | Speech-to-Text-WaveNet : End-to-end sentence level English | Speech library

 by   buriburisuri Python Version: Current License: Apache-2.0

kandi X-RAY | speech-to-text-wavenet Summary

kandi X-RAY | speech-to-text-wavenet Summary

speech-to-text-wavenet is a Python library typically used in Artificial Intelligence, Speech, Tensorflow, Neural Network applications. speech-to-text-wavenet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Speech-to-Text-WaveNet : End-to-end sentence level English speech recognition based on DeepMind's WaveNet and tensorflow

            kandi-support Support

              speech-to-text-wavenet has a medium active ecosystem.
              It has 3746 star(s) with 794 fork(s). There are 197 watchers for this library.
              It had no major release in the last 6 months.
              There are 78 open issues and 38 have been closed. On average issues are closed in 122 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of speech-to-text-wavenet is current.

            kandi-Quality Quality

              speech-to-text-wavenet has 0 bugs and 0 code smells.

            kandi-Security Security

              speech-to-text-wavenet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              speech-to-text-wavenet code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              speech-to-text-wavenet is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              speech-to-text-wavenet 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 are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed speech-to-text-wavenet and discovered the below as its top functions. This is intended to give you an instant insight into speech-to-text-wavenet implemented functionality, and help decide if they suit your requirements.
            • Process libriSpeech data files
            • Convert a string to an index
            • Process TTL data files
            • Convert a SOX file to a wav
            • Load audio file
            • Augment a random FCC
            • Print a list of indices
            • Convert an index to a string
            • Process VCTK files
            • Get logit
            • Compute CTC loss
            Get all kandi verified functions for this library.

            speech-to-text-wavenet Key Features

            No Key Features are available at this moment for speech-to-text-wavenet.

            speech-to-text-wavenet Examples and Code Snippets

            Speech-to-Text using WaveNet,Dataset
            Pythondot img1Lines of Code : 6dot img1License : Permissive (MIT)
            copy iconCopy
            find -type f -name '*.sph' | awk '{printf "sox -t sph %s -b 16 -t wav %s\n", $0, $0".wav" }' | bash
            sudo apt-get install sox
            python preprocess.py
            Speech-to-Text using WaveNet,Training
            Pythondot img2Lines of Code : 1dot img2License : Permissive (MIT)
            copy iconCopy
            python train.py

            Community Discussions


            How to build Tensorflow speech recognition integrated with language model
            Asked 2017-Apr-12 at 07:55

            How can I integrate a language model in a tensorflow speech recognition architecture?

            There are a bunch of examples out there for building character level speech recognition in Tensorflow (e.g. https://github.com/nervanasystems/neon, https://github.com/buriburisuri/speech-to-text-wavenet), which is interesting but practically useless, unless a language model is integrated. I couldn't find an example that uses a language model.

            How can I integrate a language model?



            Answered 2017-Apr-12 at 07:55

            LM scoring is just an additional rescoring step, simply a spelling correction with a language model. It can be applied on any system output. Mozilla has it spell.py for example.

            Source https://stackoverflow.com/questions/43359882

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


            No vulnerabilities reported

            Install speech-to-text-wavenet

            You can download it from GitHub.
            You can use speech-to-text-wavenet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            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 .
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            gh repo clone buriburisuri/speech-to-text-wavenet

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