speech_to_text example github

MelNet - Audio Samples

Audio samples accompanying the paper MelNet: A Generative Model for Audio in the Frequency Domain .

Single-Speaker Speech Generation

Multi-speaker speech generation, music generation, single-speaker text-to-speech, multi-speaker text-to-speech, wavenet baseline, ablation - multiscale modelling.

• Due to the large number of audio samples on this page, all samples have been compressed (96 kb/s mp3). The uncompressed files are available for download at this repository .
• Audio clips which correspond to ground-truth data are generated by inverting ground-truth spectrograms.
• Samples shown here were selected based on diversity and quality. Samples used for quantitative experiments in the paper were randomly drawn.

Samples generated by MelNet trained on the task of unconditional single-speaker speech generation using professionally recorded audiobook data from the Blizzard 2013 dataset.

Samples from the model without biasing or priming.

Biased Samples

Samples from the model using a bias of 1.0.

Primed Samples

The first 5 seconds of each audio clip are from the dataset and the remaining 5 seconds are generated by the model.

Samples generated by MelNet trained on the task of unconditional multi-speaker speech generation using noisy, multispeaker, multilingual speech data from the VoxCeleb2 dataset.

Samples generated by MelNet trained on the task of unconditional music generation using recorded piano performances from the MAESTRO dataset.

Samples generated by MelNet trained on the task of single-speaker TTS using professionally recorded audiobook data from the Blizzard 2013 dataset.

The first audio clip for each text is taken from the dataset and the remaining 3 are samples generated by the model.

“My dear Fanny, you feel these things a great deal too much. I am most happy that you like the chain,”

Looking with a half fantastic curiosity to see whether the tender grass of early spring,

“I like them round,” said Mary. “And they are exactly the color of the sky over the moor.”

Lydia was Lydia still; untamed, unabashed, wild, noisy, and fearless.

“Oh, he has been away from New York—he has been all round the world. He doesn't know many people here, but he's very sociable, and he wants to know every one.”

Each unlabelled audio clip is taken from the dataset and the audio clip that directly follows is a sample generated by the model primed with that sequence.

Write a fond note to the friend you cherish.

Pluck the bright rose without leaves.

Two plus seven is less than ten.

He said the same phrase thirty times.

We frown when events take a bad turn.

Samples generated by MelNet trained on the task of multi-speaker TTS using noisy speech recognition data from the TED-LIUM 3 dataset.

Samples generated by the model conditioned on text and speaker ID. The conditioning text and speaker IDs are taken directly from the validation set (text in the dataset is unnormalized and unpunctuated).

it wasn't like i was asking for the code to a nuclear bunker or anything like that but the amount of resistance i got from this

and what that form is modeling and shaping is not cement

that every person here every decision that you've made today every decision you've made in your life you've not really made that decision but in fact

syria was largely a place of tolerance historically accustomed

and no matter what the rest of the world tells them they should be

the years went by and the princess grew up into a beautiful young woman

i spent so much time learning this language why do i only

and we were down to eating one meal a day running from place to place but wherever we could help we did at a certain point in time in

phrases and words even if you have a phd of chinese language you can't understand them

and when they came back and told us about it we really started thinking about the ways in which we see styrofoam every day

is only a very recent religious enthusiasm it surfaced only in the west

chances are that they are rooted in the productivity crisis

i cannot face your fears or chase your dreams and you can't do that for me but we can be supportive of eachother

the first law of travel and therefore of life you're only as strong

Selected Speakers

Samples generated by the model for selected speakers. Reference audio for each of the speakers can be found on the TED website .

A cramp is no small danger on a swim.

The glow deepened in the eyes of the sweet girl.

Bring your problems to the wise chief.

Clothes and lodging are free to new men.

Port is a strong wine with a smoky taste.

For comparison, we train WaveNet on the same three unconditional audio generation tasks used to evaluate MelNet (single-speaker speech generation, multi-speaker speech generation, and music generation).

Samples without biasing or priming.

Samples with priming: 5 seconds from the dataset followed by 5 seconds generated by WaveNet.

Samples from a two-stage model which separately models MIDI notes and then uses WaveNet to synthesize audio conditioned on the generated MIDI notes.

The following models were trained on the same data, with each model using a different number of tiers.

5-Tier Model

4-tier model, 3-tier model, 2-tier model.

View Github Repository .

Say (Text-to-Speech)

Speak synthetically using this simple SDK.

Get list of Voices

Open your DEV CONSOLE . Voices load asynchronously from the OS.

Listen (Speech-to-Text)

Get voice commands and transcribe your voice to text.

Prompt User Voice Command Example

You want to ask the user for information and record their response.

Capture Partial Transcripts

Capture live "real-time" transcription as you speak.

• Português – Brasil

Using the Speech-to-Text API with Node.js

1. overview.

Google Cloud Speech-to-Text API enables developers to convert audio to text in 120 languages and variants, by applying powerful neural network models in an easy to use API.

In this codelab, you will focus on using the Speech-to-Text API with Node.js. You will learn how to send an audio file in English and other languages to the Cloud Speech-to-Text API for transcription.

What you'll learn

• How to enable the Speech-to-Text API
• How to Authenticate API requests
• How to install the Google Cloud client library for Node.js
• How to transcribe audio files in English
• How to transcribe audio files with word timestamps
• How to transcribe audio files in different languages

What you'll need

• A Google Cloud Platform Project
• A Browser, such Chrome or Firefox
• Familiarity using Javascript/Node.js

How will you use this tutorial?

How would you rate your experience with node.js, how would you rate your experience with using google cloud platform services, 2. setup and requirements, self-paced environment setup.

• Sign in to Cloud Console and create a new project or reuse an existing one. (If you don't already have a Gmail or G Suite account, you must create one .)

Remember the project ID, a unique name across all Google Cloud projects (the name above has already been taken and will not work for you, sorry!). It will be referred to later in this codelab as PROJECT_ID .

• Next, you'll need to enable billing in Cloud Console in order to use Google Cloud resources.

Running through this codelab shouldn't cost much, if anything at all. Be sure to to follow any instructions in the "Cleaning up" section which advises you how to shut down resources so you don't incur billing beyond this tutorial. New users of Google Cloud are eligible for the $300USD Free Trial program.

Start Cloud Shell

While Google Cloud can be operated remotely from your laptop, in this codelab you will be using Google Cloud Shell , a command line environment running in the Cloud.

Activate Cloud Shell

If you've never started Cloud Shell before, you'll be presented with an intermediate screen (below the fold) describing what it is. If that's the case, click Continue (and you won't ever see it again). Here's what that one-time screen looks like:

It should only take a few moments to provision and connect to Cloud Shell.

This virtual machine is loaded with all the development tools you'll need. It offers a persistent 5GB home directory and runs in Google Cloud, greatly enhancing network performance and authentication. Much, if not all, of your work in this codelab can be done with simply a browser or your Chromebook.

Once connected to Cloud Shell, you should see that you are already authenticated and that the project is already set to your project ID.

• Run the following command in Cloud Shell to confirm that you are authenticated:

Command output

If it is not, you can set it with this command:

3. Enable the Speech-to-Text API

Before you can begin using the Speech-to-Text API, you must enable the API. You can enable the API by using the following command in the Cloud Shell:

4. Authenticate API requests

In order to make requests to the Speech-to-Text API, you need to use a Service Account . A Service Account belongs to your project and it is used by the Google Client Node.js library to make Speech-to-Text API requests. Like any other user account, a service account is represented by an email address. In this section, you will use the Cloud SDK to create a service account and then create credentials you will need to authenticate as the service account.

First, set an environment variable with your PROJECT_ID which you will use throughout this codelab, if you are using Cloud Shell this will be set for you:

Next, create a new service account to access the Speech-to-Text API by using:

Next, create credentials that your Node.js code will use to login as your new service account. Create these credentials and save it as a JSON file ~/key.json by using the following command:

Finally, set the GOOGLE_APPLICATION_CREDENTIALS environment variable, which is used by the Speech-to-Text API Node.js library, covered in the next step, to find your credentials. The environment variable should be set to the full path of the credentials JSON file you created, by using:

You can read more about authenticating the Speech-to-Text API .

5. Install the Google Cloud Speech-to-Text API client library for Node.js

First, create a project that you will use to run this Speech-to-Text API lab, initialize a new Node.js package in a folder of your choice:

NPM asks several questions about the project configuration, such as name and version. For each question, press ENTER to accept the default values. The default entry point is a file named index.js .

Next, install the Google Cloud Speech library to the project:

For more instructions on how to set up a Node.js development for Google Cloud please see the Setup Guide .

Now, you're ready to use Speech-to-Text API!

6. Transcribe Audio Files

In this section, you will transcribe a pre-recorded audio file in English. The audio file is available on Google Cloud Storage.

Navigate to the index.js file inside the and replace the code with the following:

Take a minute or two to study the code and see it is used to transcribe an audio file*.*

The Encoding parameter tells the API which type of audio encoding you're using for the audio file. Flac is the encoding type for .raw files (see the doc for encoding type for more details).

In the RecognitionAudio object, you can pass the API either the uri of our audio file in Cloud Storage or the local file path for the audio file. Here, we're using a Cloud Storage uri.

Run the program:

You should see the following output:

7. Transcribe with word timestamps

Speech-to-Text can detect time offset (timestamp) for the transcribed audio. Time offsets show the beginning and end of each spoken word in the supplied audio. A time offset value represents the amount of time that has elapsed from the beginning of the audio, in increments of 100ms.

Take a minute or two to study the code and see it is used to transcribe an audio file with word timestamps*.* The EnableWordTimeOffsets parameter tells the API to enable time offsets (see the doc for more details).

Run your program again:

8. Transcribe different languages

Speech-to-Text API supports transcription in over 100 languages! You can find a list of supported languages here .

In this section, you will transcribe a pre-recorded audio file in French. The audio file is available on Google Cloud Storage.

Run your program again and you should see the following output:

This is a sentence from a popular French children's tale .

For the full list of supported languages and language codes, see the documentation here .

9. Congratulations!

You learned how to use the Speech-to-Text API using Node.js to perform different kinds of transcription on audio files!

To avoid incurring charges to your Google Cloud Platform account for the resources used in this quickstart:

• Go to the Cloud Platform Console .
• Select the project you want to shut down, then click ‘Delete' at the top: this schedules the project for deletion.
• Google Cloud Speech-to-Text API: https://cloud.google.com/speech-to-text/docs
• Node.js on Google Cloud Platform: https://cloud.google.com/nodejs/
• Google Cloud Node.js client: https://googlecloudplatform.github.io/google-cloud-node/

This work is licensed under a Creative Commons Attribution 2.0 Generic License.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Video of Eric Schmidt blaming remote work for Google’s woes mysteriously vanishes

Former Google CEO Eric Schmidt, who has since moved on to greener and perhaps more dangerous pastures, told an audience of Stanford students recently that “Google decided that work-life balance and going home early and working from home was more important than winning.” Evidently this hot take was not for wider consumption, as Stanford — which posted the video this week on YouTube — today made the video of the event private.

It would not be surprising if it was at the behest of the talent. “I misspoke about Google and their work hours,” said Schmidt in an emailed statement to TechCrunch. “I regret my error.”

Several news publications have already run headlines about the video, and 1.4 million people viewed a clip of Schmidt trashing Google’s remote work practices on X.

At another point in the video, Schmidt tells the audience to keep what he’s telling them private, then acts somewhat surprised when his interviewer points out cameras in the room. At another point, he tells the audience that AI startups can steal IP and hire smart lawyers to clean it up later, once they’ve created a successful product. Presumably, the former Google CEO did not know the entire interview would be posted online.

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Accessing contextual information about workflow runs

You can access context information in workflows and actions.

In this article

About contexts.

Contexts are a way to access information about workflow runs, variables, runner environments, jobs, and steps. Each context is an object that contains properties, which can be strings or other objects.

Contexts, objects, and properties will vary significantly under different workflow run conditions. For example, the matrix context is only populated for jobs in a matrix .

You can access contexts using the expression syntax. For more information, see " Evaluate expressions in workflows and actions ."

${{ <context> }}

Warning: When creating workflows and actions, you should always consider whether your code might execute untrusted input from possible attackers. Certain contexts should be treated as untrusted input, as an attacker could insert their own malicious content. For more information, see " Security hardening for GitHub Actions ."

As part of an expression, you can access context information using one of two syntaxes.

• Index syntax: github['sha']
• Property dereference syntax: github.sha

In order to use property dereference syntax, the property name must start with a letter or _ and contain only alphanumeric characters, - , or _ .

If you attempt to dereference a nonexistent property, it will evaluate to an empty string.

Determining when to use contexts

GitHub Actions includes a collection of variables called contexts and a similar collection of variables called default variables . These variables are intended for use at different points in the workflow:

• Default environment variables: These environment variables exist only on the runner that is executing your job. For more information, see " Store information in variables ."
• Contexts: You can use most contexts at any point in your workflow, including when default variables would be unavailable. For example, you can use contexts with expressions to perform initial processing before the job is routed to a runner for execution; this allows you to use a context with the conditional if keyword to determine whether a step should run. Once the job is running, you can also retrieve context variables from the runner that is executing the job, such as runner.os . For details of where you can use various contexts within a workflow, see " Context availability ."

The following example demonstrates how these different types of variables can be used together in a job:

In this example, the if statement checks the github.ref context to determine the current branch name; if the name is refs/heads/main , then the subsequent steps are executed. The if check is processed by GitHub Actions, and the job is only sent to the runner if the result is true . Once the job is sent to the runner, the step is executed and refers to the $GITHUB_REF variable from the runner.

Context availability

Different contexts are available throughout a workflow run. For example, the secrets context may only be used at certain places within a job.

In addition, some functions may only be used in certain places. For example, the hashFiles function is not available everywhere.

The following table indicates where each context and special function can be used within a workflow. Unless listed below, a function can be used anywhere.

Example: printing context information to the log

You can print the contents of contexts to the log for debugging. The toJSON function is required to pretty-print JSON objects to the log.

Warning: When using the whole github context, be mindful that it includes sensitive information such as github.token . GitHub masks secrets when they are printed to the console, but you should be cautious when exporting or printing the context.

github context

The github context contains information about the workflow run and the event that triggered the run. You can also read most of the github context data in environment variables. For more information about environment variables, see " Store information in variables ."

Example contents of the github context

The following example context is from a workflow run triggered by the push event. The event object in this example has been truncated because it is identical to the contents of the push webhook payload .

Note: This context is an example only. The contents of a context depends on the workflow that you are running. Contexts, objects, and properties will vary significantly under different workflow run conditions.

Example usage of the github context

This example workflow uses the github.event_name context to run a job only if the workflow run was triggered by the pull_request event.

env context

The env context contains variables that have been set in a workflow, job, or step. It does not contain variables inherited by the runner process. For more information about setting variables in your workflow, see " Workflow syntax for GitHub Actions ."

You can retrieve the values of variables stored in env context and use these values in your workflow file. You can use the env context in any key in a workflow step except for the id and uses keys. For more information on the step syntax, see " Workflow syntax for GitHub Actions ."

If you want to use the value of a variable inside a runner, use the runner operating system's normal method for reading environment variables.

Example contents of the env context

The contents of the env context is a mapping of variable names to their values. The context's contents can change depending on where it is used in the workflow run. In this example, the env context contains two variables.

Example usage of the env context

This example workflow shows variables being set in the env context at the workflow, job, and step levels. The ${{ env.VARIABLE-NAME }} syntax is then used to retrieve variable values within individual steps in the workflow.

When more than one environment variable is defined with the same name, GitHub uses the most specific variable. For example, an environment variable defined in a step will override job and workflow environment variables with the same name, while the step executes. An environment variable defined for a job will override a workflow variable with the same name, while the job executes.

vars context

Note: Configuration variables for GitHub Actions are in beta and subject to change.

The vars context contains custom configuration variables set at the organization, repository, and environment levels. For more information about defining configuration variables for use in multiple workflows, see " Store information in variables ".

Example contents of the vars context

The contents of the vars context is a mapping of configuration variable names to their values.

Example usage of the vars context

This example workflow shows how configuration variables set at the repository, environment, or organization levels are automatically available using the vars context.

If a configuration variable has not been set, the return value of a context referencing the variable will be an empty string.

The following example shows using configuration variables with the vars context across a workflow. Each of the following configuration variables have been defined at the repository, organization, or environment levels.

job context

The job context contains information about the currently running job.

Example contents of the job context

This example job context uses a PostgreSQL service container with mapped ports. If there are no containers or service containers used in a job, the job context only contains the status property.

Example usage of the job context

This example workflow configures a PostgreSQL service container, and automatically maps port 5432 in the service container to a randomly chosen available port on the host. The job context is used to access the number of the port that was assigned on the host.

jobs context

The jobs context is only available in reusable workflows, and can only be used to set outputs for a reusable workflow. For more information, see " Reusing workflows ."

Example contents of the jobs context

This example jobs context contains the result and outputs of a job from a reusable workflow run.

Example usage of the jobs context

This example reusable workflow uses the jobs context to set outputs for the reusable workflow. Note how the outputs flow up from the steps, to the job, then to the workflow_call trigger. For more information, see " Reusing workflows ."

steps context

The steps context contains information about the steps in the current job that have an id specified and have already run.

Example contents of the steps context

This example steps context shows two previous steps that had an id specified. The first step had the id named checkout , the second generate_number . The generate_number step had an output named random_number .

Example usage of the steps context

This example workflow generates a random number as an output in one step, and a later step uses the steps context to read the value of that output.

runner context

The runner context contains information about the runner that is executing the current job.

Example contents of the runner context

The following example context is from a Linux GitHub-hosted runner.

Example usage of the runner context

This example workflow uses the runner context to set the path to the temporary directory to write logs, and if the workflow fails, it uploads those logs as artifact.

secrets context

The secrets context contains the names and values of secrets that are available to a workflow run. The secrets context is not available for composite actions due to security reasons. If you want to pass a secret to a composite action, you need to do it explicitly as an input. For more information about secrets, see " Using secrets in GitHub Actions ."

GITHUB_TOKEN is a secret that is automatically created for every workflow run, and is always included in the secrets context. For more information, see " Automatic token authentication ."

Warning: If a secret was used in the job, GitHub automatically redacts secrets printed to the log. You should avoid printing secrets to the log intentionally.

Example contents of the secrets context

The following example contents of the secrets context shows the automatic GITHUB_TOKEN , as well as two other secrets available to the workflow run.

Example usage of the secrets context

This example workflow uses the GitHub CLI , which requires the GITHUB_TOKEN as the value for the GH_TOKEN input parameter:

strategy context

For workflows with a matrix, the strategy context contains information about the matrix execution strategy for the current job.

Example contents of the strategy context

The following example contents of the strategy context is from a matrix with four jobs, and is taken from the final job. Note the difference between the zero-based job-index number, and job-total which is not zero-based.

Example usage of the strategy context

This example workflow uses the strategy.job-index property to set a unique name for a log file for each job in a matrix.

matrix context

For workflows with a matrix, the matrix context contains the matrix properties defined in the workflow file that apply to the current job. For example, if you configure a matrix with the os and node keys, the matrix context object includes the os and node properties with the values that are being used for the current job.

There are no standard properties in the matrix context, only those which are defined in the workflow file.

Example contents of the matrix context

The following example contents of the matrix context is from a job in a matrix that has the os and node matrix properties defined in the workflow. The job is executing the matrix combination of an ubuntu-latest OS and Node.js version 16 .

Example usage of the matrix context

This example workflow creates a matrix with os and node keys. It uses the matrix.os property to set the runner type for each job, and uses the matrix.node property to set the Node.js version for each job.

needs context

The needs context contains outputs from all jobs that are defined as a direct dependency of the current job. Note that this doesn't include implicitly dependent jobs (for example, dependent jobs of a dependent job). For more information on defining job dependencies, see " Workflow syntax for GitHub Actions ."

Example contents of the needs context

The following example contents of the needs context shows information for two jobs that the current job depends on.

Example usage of the needs context

This example workflow has three jobs: a build job that does a build, a deploy job that requires the build job, and a debug job that requires both the build and deploy jobs and runs only if there is a failure in the workflow. The deploy job also uses the needs context to access an output from the build job.

inputs context

The inputs context contains input properties passed to an action, to a reusable workflow, or to a manually triggered workflow. For reusable workflows, the input names and types are defined in the workflow_call event configuration of a reusable workflow, and the input values are passed from jobs.<job_id>.with in an external workflow that calls the reusable workflow. For manually triggered workflows, the inputs are defined in the workflow_dispatch event configuration of a workflow.

The properties in the inputs context are defined in the workflow file. They are only available in a reusable workflow or in a workflow triggered by the workflow_dispatch event

Example contents of the inputs context

The following example contents of the inputs context is from a workflow that has defined the build_id , deploy_target , and perform_deploy inputs.

Example usage of the inputs context in a reusable workflow

This example reusable workflow uses the inputs context to get the values of the build_id , deploy_target , and perform_deploy inputs that were passed to the reusable workflow from the caller workflow.

Example usage of the inputs context in a manually triggered workflow

This example workflow triggered by a workflow_dispatch event uses the inputs context to get the values of the build_id , deploy_target , and perform_deploy inputs that were passed to the workflow.

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Harris Plans to Ban Grocery ‘Price Gouging.’ What Does the Evidence Say?

Price increases when demand exceeds supply are textbook economics. The question is whether, and how much, the pandemic yielded an excess take.

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By Jim Tankersley and Jeanna Smialek

Reporting from Washington

Vice President Kamala Harris’s economic agenda for her presidential campaign features an argument that blames corporate price gouging for high grocery prices.

That message polls well with swing voters. It has been embraced by progressive groups , which regularly point to price gouging as a driver of rapid inflation, or at least something that contributes to rapid price increases. Those groups cheered the announcement late Wednesday that Ms. Harris would call for a federal ban on corporate price gouging on groceries in an economic policy speech on Friday.

But the economic argument over the issue is complicated.

Economists have cited a range of forces for pushing up prices in the recovery from the pandemic recession, including snarled supply chains, a sudden shift in consumer buying patterns , and the increased customer demand fueled by stimulus from the government and low rates from the Federal Reserve. Most economists say those forces are far more responsible than corporate behavior for the rise in prices in that period.

Biden administration economists have found that corporate behavior has played a role in pushing up grocery costs in recent years — but that other factors have played a much larger one.

The Harris campaign announcement on Wednesday cited meat industry consolidation as a driver of excessive grocery prices, but officials did not respond on Thursday to questions about the evidence Ms. Harris would cite or how her proposal would work.

There are examples of companies telling investors in recent years that they have been able to raise prices to increase profits. But even the term “price gouging” means different things to different people.

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