--- title: "AI Notebooks - Tutorial - Use Speech-to-Text powers on audio and video" description: "How to convert Speech to Text using AI Notebooks" url: https://docs.ovhcloud.com/en/guides/public-cloud/ai-machine-learning/ai-notebooks-speech-to-text-recognition lang: en lastUpdated: 2022-09-01 --- # AI Notebooks - Tutorial - Use Speech-to-Text powers on audio and video ## Objective The purpose of this tutorial is to show you how it is possible to **convert speech into text** and **generate transcripts** thanks to AI Notebooks. ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-08-speech-to-text/speech-to-text.jpeg) In **Natural Language Processing** (NLP), speech-to-text is a Deep Learning task that enables machines to understand and read human language. There are many applications: transcription, summaries, diarization, subtitle generation, ... This documentation allows you to test and launch **3 AI Notebooks** allowing you to get to grips with and use various speech-to-text features. 1. The first one will teach you the **basics of audio transcript**. You will be able to transcribe long local or YouTube audio files, measure the quality of a transcription, add punctuation and summarize them. 2. The second tutorial is intended to discover more advanced steps such as the **detection of speaker changes (diarization)** and the **generation of video subtitles**. 3. The last tutorial is a **comparison of different Speech-to-Text models** to find the best one among those available. :::info The following instructions correspond to **each** of these 3 tutorials. ::: ## Requirements - An AI Notebooks project created inside a [Public Cloud project](https://www.ovhcloud.com/en-gb/public-cloud/) in your OVHcloud account - A user for AI Notebooks *** ### OVHcloud Control Panel Access - **Direct link:** Public Cloud Projects - **Navigation path:** Public Cloud > Select your project *** ## Instructions You can launch your notebook from the OVHcloud Control Panel or via the ovhai [CLI](/en/guides/public-cloud/ai-machine-learning/ai-cli-run-notebook.md) . Direct link to the full code can be found [here](https://github.com/ovh/ai-training-examples/tree/main/notebooks/natural-language-processing/speech-to-text/miniconda). ### Launching a Jupyter notebook with "Miniconda" via UI To launch your notebook from the OVHcloud Control Panel , refer to the following steps. #### Code editor Choose the `Jupyterlab` code editor. #### Framework In this tutorial, the `Miniconda` framework is used. :::warning With **Miniconda**, you will be able to set up your environment by installing the Python libraries you need. ::: You can choose the `conda` version. :::info The default version of **conda** is functional for this tutorial: `conda-py39-cuda11.2-v22-4`. ::: #### Resources GPU is recommended since audio transcription is resource intensive. :::info Here, using `1 GPU` is sufficient. ::: ### Launching a Jupyter notebook with "Miniconda" via CLI If you want to launch it with the CLI, choose the `jupyterlab` editor and the `conda` framework. To access the different versions of `conda` available, run the following command. ```bash ovhai capabilities framework list -o yaml ``` _This tutorial has been launched with the `conda-py39-cuda11.2-v22-4` version._ :::info If you do not specify a version, your notebook starts with the default version of `conda`. ::: Choose the number of CPUs/GPUs (`` or ``) to use in your notebook and use the following command. :::info Here we recommend using `1 GPU`. ::: ```bash ovhai notebook run conda jupyterlab \ --name \ --framework-version \ --gpu ``` You can then reach your notebook’s URL once the notebook is running. ### Accessing the notebooks Once the repository has been cloned, find your notebook by following this path: `ai-training-examples` > `notebooks` > `natural-language-processing` > `speech-to-text`. 1. You can find the first tutorial in the `basics` folder. A preview of this notebook can be found on GitHub [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/natural-language-processing/speech-to-text/miniconda/basics/speech-to-text-basics.ipynb). 2. The second tutorial corresponds to the `advanced` folder. A preview of this notebook can be found on GitHub [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/natural-language-processing/speech-to-text/miniconda/advanced/speech-to-text-advanced.ipynb). 3. The last folder, named `compare-models`, contains the third tutorial. A preview of this notebook can be found on GitHub [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/natural-language-processing/speech-to-text/miniconda/compare-models/speech-to-text-compare-models.ipynb). ## Go further - With NLP, you can do **sentiment analysis**. For more information, click [here](/en/guides/public-cloud/ai-machine-learning/ai-notebooks-hugging-face-sentiment-analysis.md). If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/en-gb/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project. ## Feedback Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud)