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Performance Factorsįactors relevant to Speech-to-Text performance include but are not limited to speaker demographics, recording quality, and background noise.

Speech-to-Text for the Persian Language on 16kHz, mono-channel audio.

Where to send questions or comments about the model: You can leave an issue on STT issues, open a new discussion on STT discussions, or chat with us on Gitter.

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License: GNU Lesser General Public License v3.0.

Model language: Persian / Farsi / fa, fa-IR.

Person or organization developing model: Maintained by oct4pie.

Model card for Persian STT v0.1.0 Model details

The acoustic and language models are available in releases.

Thankfully, the scorer is able to handle most of these cases, according to the context.

For example, the model is unable to distinguish between the following words by themselves:

It is worth noting that many of the errors are due to the language syntax.

Then, the dropout rate was decreased as the learning rate was decreasing due to the plateau to reach the minimum loss values.

Scorer_path = '/content/kenlm-persian.scorer',

The model was trained with the following configuration:.

Tranfer learning from the STT English model was enabled (with drop_source_layers=2).

Training was stopped at 1896648 steps after optimizing the model.

Learning_rate was increased for the second run from 0.00012 to 0.0004. The model was trained 2 separate times with force_initialize_learning_rate.Then, the corpus was normalized, tokenized, and cleaned using the persianify function and _HTMLStripper in the notebook.The raw text corpus was obtained from here.The generated validated.csv was passed to auto_input_dataset=metadata in the configuation step.Using STT's bin/import_cv2.py script, the data was matched to the alphabet converted to CSV files.The Common-Voice dataset was obtained from here.

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persian_stt.ipynb contains a notebook that demonstrates how to preprocess the data.Approaches to uncertainty and variability.To learn React, check out the React documentation.This is a Persian DeepSpeech model that is currently in development. You can learn more in the Create React App documentation. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. All of the commands except eject will still work, but they will point to the copied scripts so you can tweak them.

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Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. This command will remove the single build dependency from your project. If you aren't satisfied with the build tool and configuration choices, you can eject at any time. See the section about deployment for more information. The build is minified and the filenames include the hashes. It correctly bundles React in production mode and optimizes the build for the best performance. npm run buildīuilds the app for production to the build folder. See the section about running tests for more information. Launches the test runner in the interactive watch mode.

You may also see any lint errors in the console. The page will reload when you make changes. In the project directory, you can run: npm start This project was bootstrapped with Create React App.