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Call center transcription software: what enterprises should look for in 2026

TL;DR: Most contact centers evaluate transcription software using clean-audio lab benchmarks, then watch QA automation break down when BPO (Business Process Outsourcing) agents switch languages mid-call or phone-line noise degrades the signal. In 2026, the criteria that matter are real-world multilingual WER, all-inclusive per-hour pricing, and data sovereignty that holds up under GDPR and HIPAA audit. For enterprise teams, the highest-ROI evaluation step is testing on real BPO call samples rather than vendor demo audio, and asking every shortlisted provider for an all-in per-hour price with diarization, sentiment, and entity extraction enabled.

Speech-To-Text

PII redaction for call recordings: how ingestion-level redaction keeps calls PCI compliant

TL;DR: Legacy pause-and-resume systems don't remove agents, local desktops, or telephony infrastructure from PCI DSS audit scope. Automated, ingestion-level PII redaction scrubs sensitive data before it reaches any database. By removing cardholder data at the ingestion layer, contact center platforms using automated redaction can potentially reduce audit complexity, cut agent handle time (AHT), and protect downstream CRM and LLM pipelines from corrupt data. The accuracy floor for reliable entity detection in PCI audits is significantly higher than for standard QA transcription, making STT model selection a compliance decision as much as a product one.

Speech-To-Text

GDPR, SOC 2, and ISO 27001 speech-to-text: the contact center compliance and certification guide

TL;DR: When your contact center routes voice data through a transcription vendor, every certification gap in that vendor's stack becomes your compliance liability. Voice recordings qualify as personal data under GDPR Article 4, and processing them through uncertified APIs creates direct financial exposure. This guide breaks down what GDPR, SOC 2 Type II, ISO 27001, HIPAA, and PCI DSS each require of your audio infrastructure vendor and maps those requirements to the QA coverage rates and cost-per-contact metrics you manage daily. We hold GDPR, SOC 2 Type II, ISO 27001, HIPAA, and PCI DSS certifications, and never use customer audio for model training on Growth or Enterprise plan.

Building a Whisper YouTube transcription generator for automated captioning

Published on Nov 15, 2023

With over 500 hours of video uploaded to YouTube every minute, providing accurate captions and transcripts is essential for creators to make their content engaging and accessible. However, manually transcribing long videos is tedious and time-consuming.

YouTube does automatically generate captions for uploaded videos. However, it can take hours for new videos to get captions – and the quality tends to disappoint. For creators needing high-quality captions immediately, an API-based solution may be a better alternative.

In this step-by-step guide, we’ll show you how to easily build your own Whisper YouTube transcription generator using Gladia's optimized Whisper API. With just a few lines of code, you can leverage the power of cutting-edge ASR and large language models to automatically generate captions and transcripts for your YouTube videos.

This guide will walk you through how to tap into Gladia’s Whisper-based AI transcription API to easily generate captions for any video. Let's get started!

Overview

Tools like yt_dlp allow you to download video and audio content from YouTube and other sites. Bringing these pieces together, you can automatically generate subtitles for any video. First, we’ll use a package like yt_dlp to download the video file. Next, we’ll send it to the Gladia API to generate the transcription. We’ll then take this text and format it into a subtitle file like SRT. Finally, we’ll utilize ffmpeg to insert the subtitles back into the original video.

Prerequisites

Python 3.7+
- Python Packages
- requests
- Yt_dlp

Downloading a Youtube video

Step 1: Install dependencies

To get started, we first need to download the YouTube video we want to transcribe. We'll use the popular `yt_dlp` library to handle this.

First, install `yt_dlp` if you don't already have it:


pip install yt_dlp

With the dependencies installed, we can now use the following Python code to download a video.‍

Step 2: Download a video


import yt_dlp

# Code to download video...

def download_video(url):
  ydl_opts = {
    'outtmpl': '%(id)s.%(ext)s',
    'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4'
  }

  with yt_dlp.YoutubeDL(ydl_opts) as ydl:
    ydl.download([url]) 

download_video('https://www.youtube.com/watch?v=VIDEO_ID')

This will save the video to your current directory. We can now use this file as input for the Whisper transcription.

When we call the `download_video()` function using the the Rick Astley's 'Never Gonna Make You Cry' YouTube URL:


download_video('https://www.youtube.com/watch?v=dQw4w9WgXcQ')

So when the download completes, yt-dlp will save the video with the name:`dQw4w9WgXcQ.mp4`

The `.mp4` extension is automatically added because we set the format to download the best available MP4 file.

This results in the rickroll video being saved as `dQw4w9WgXcQ.mp4` in our working directory, which we can then pass to Gladia's API to transcribe.

Transcribing videos with the Gladia API

Step 1: Retrieve your API key

Gladia provides an AI-powered API for transcribing and analyzing audio and video files. To get started using the Gladia API, you first need to create an account at app.gladia.io. You can register with an email and password or using your Google account. After signing up, you will be provided with an API key that is required to authenticate when making API requests.‍

Step 2: Import Python modules

First, we import the requests library to make HTTP requests, and the os module to interact with the file system:If you don't have requests installed run:


pip install requests


import requests
import os

Step 3: Code Integration

The easiest way to use the Gladia's API is by providing an URL to a video:


apiKey = os.getenv("GLADIA_API_KEY")

headers = {
  'x-gladia-key': apiKey
}

We can specify the YouTube URL directly in the `files` dictionary:


 API_URL=”https://api.gladia.io/audio/text/audio-transcription/”
 response = requests.post(API_URL, headers=headers, files=files)

This can be useful if you don't want to manage downloading and storing the audio files yourself. The tradeoff is the transcription may take slightly longer as the audio has to be downloaded first.

Next, we will explore uploading a file downloaded directly from YouTube.


apiKey = os.getenv("GLADIA_API_KEY")

headers = {
  'x-gladia-key': apiKey, 
}

We specify the file path to the video file we want to transcribe:


file_path = "dQw4w9WgXcQ.mp4"

Then we open the file in binary read mode:


with open(file_path, 'rb') as f:

We create a dictionary called `files` to hold the data we want to send in the request:


  filename= “'dQw4w9WgXcQ.mp4”
  files = {
    'audio_file': (mp4_file , f, 'audio/mp4'),
    'toggle_diarization': (None, 'true')
  }

This includes the audio file itself, and a parameter to enable speaker diarization in the transcription output.

We make the API request, passing the headers and files:


API_URL=”https://api.gladia.io/audio/text/audio-transcription/”
response = requests.post(API_URL, headers=headers, files=files)
data = response.json()

This allows us to send the video file and process the transcription results from Gladia's API.

Generating subtitles

Step 1: Understanding subtitle formats

A .srt file is a subtitle file that contains the timing and text for subtitles in a video. It consists of sequential blocks like this:


1
00:00:01,000 --> 00:00:05,000
This is the subtitle text for the first segment

2 
00:00:05,001 --> 00:00:10,000
Subtitle text for the second segment

We can use the following Python code to take the JSON response from Gladia's API and convert it into .srt format.

Step 2: Converting the API response to .srt format


segments = data['prediction']

lines = []
for i, segment in enumerate(segments):

  start = seconds_to_timestamp(segment['time_begin'])
  end = seconds_to_timestamp(segment['time_end'])

  text = segment['transcription']

  lines.append(str(i + 1))
  lines.append(f"{start} --> {end}")
  lines.append(text)
  lines.append('')
  
srt = '\n'.join(lines)

with open('subtitles.srt', 'w') as f:
  f.write(srt)

This generates a standard .srt file that can be added to video editing software like Premiere Pro or uploaded to YouTube to add captions.‍

Step 3: Inserting Subtitles

This ffmpeg command inserts subtitles from a SRT file into a video using the subtitles filter.


ffmpeg -i dQw4w9WgXcQ.mp4 -vf subtitles=video_transcription.srt dQw4w9WgXcQ.mp4

The subtitles filter overlays the subtitles from the SRT file on top of the input video.

This provides a convenient one-step process to overlay subtitles without encoding them separately. The subtitles filter handles overlaying the SRT on the video as needed.

Conclusion

Transcribing and subtitling video content opens up a world of possibilities. The Gladia API powered by optimized Whisper ASR makes it simple to transcribe an audio file to text. This transcription can then be formatted as subtitles and added to the video.

The end result is a subtitled video with minimal effort. While the technical details may seem complex at first, the overall workflow is straightforward. Automated transcription paves the way for increased accessibility and discoverability online, and with the right tools and knowledge, anyone can now easily add subtitles for their videos using Gladia.

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2026 Meeting Assistant Report

Read more

Call center transcription software: what enterprises should look for in 2026

PII redaction for call recordings: how ingestion-level redaction keeps calls PCI compliant

GDPR, SOC 2, and ISO 27001 speech-to-text: the contact center compliance and certification guide

Building a Whisper YouTube transcription generator for automated captioning

Overview

Prerequisites

Downloading a Youtube video

Step 1: Install dependencies

Step 2: Download a video

Transcribing videos with the Gladia API

Step 1: Retrieve your API key

Step 2: Import Python modules

Step 3: Code Integration

Generating subtitles

Step 1: Understanding subtitle formats

Step 2: Converting the API response to .srt format

Step 3: Inserting Subtitles

Conclusion

Contact us

Read more

From audio to knowledge

Subscribe to receive latest news, product updates and curated AI content.

Gladia

Newsletter

From audio to knowledge

Subscribe to receive latest news, product updates and curated AI content.