Integrate the IBM Cloud - Speech to Text API with the Python API

Setup the IBM Cloud - Speech to Text API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate IBM Cloud - Speech to Text and Python remarkably fast. Free for developers.

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Popular IBM Cloud - Speech to Text and Python Actions#

Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

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Overview of IBM Cloud - Speech to Text#

The IBM Cloud - Speech to Text API transforms spoken language into written text, offering a powerful tool for creating transcriptions, enabling voice control and command features, and feeding speech into analytics platforms. With Pipedream, you can build automated workflows that leverage this capability, such as transcribing meetings in real-time, analyzing customer service calls for sentiment and keywords, or even creating subtitles for videos. The ability to connect with other apps on Pipedream allows for complex workflows that can turn spoken data into actionable insights or accessible content.

Connect IBM Cloud - Speech to Text#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    ibm_cloud_speech_to_text: {
      type: "app",
      app: "ibm_cloud_speech_to_text",
    }
  },
  async run({steps, $}) {
    const data = {
      "text": `hello world`,
    }
    return await axios($, {
      method: "post",
      url: `${this.ibm_cloud_speech_to_text.$auth.instance_url}/v1/synthesize`,
      headers: {
        "Content-Type": `application/json`,
        "Accept": `audio/wav`,
      },
      auth: {
        username: `apikey`,
        password: `${this.ibm_cloud_speech_to_text.$auth.api_key}`,
      },
      data,
    })
  },
})

Overview of Python#

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Connect Python#

def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}

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