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MiFID II and FCA call recording: compliance for voice transcription in finance

TL;DR: Financial firms operating under MiFID II and FCA jurisdictions must maintain searchable, high-accuracy records of all client-related voice communications, including remote and mobile calls under FCA Market Watch 66. Engineering and product teams building for these obligations typically implement dedicated cloud infrastructure with defined data residency, controls that prevent customer audio from being used to retrain models on Growth and Enterprise plans, and transcription accurate enough that the resulting records hold up under regulatory review. Transcription and speaker attribution errors are not product quality issues in this context. They are audit trail failures, and regulators treat them as such.

Speech-To-Text

Why speech-to-text accuracy matters upstream of your LLM

TL;DR: Downstream LLM performance is ceiling-bounded by upstream transcription accuracy. A transcript with a meaningful error rate doesn't produce proportionally degraded summaries or CRM entries. It produces outputs where hallucinated names, inverted logic, and misattributed speaker turns compound silently into every downstream system that reads them. Prompt engineering cannot recover information the STT layer never captured. Gravite cut call quality review time by 93%, from 15 minutes to 1 minute per call, once transcript accuracy was high enough to trust the output without manual verification.

Speech-To-Text

Telephony-audio robustness: why 8kHz narrowband calls break generic STT

TL;DR: Telephony audio is constrained to 8kHz narrowband frequencies, stripping away the high-frequency spectral energy that generic 16kHz STT models require. Standard upsampling cannot recover phonemes that were never captured at the source, leading to transcription errors that compound silently into downstream NLU failures. Solaria-3 ranks #1 on Switchboard, the most challenging conversational telephony dataset, ahead of AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics, ensuring your downstream LLM pipelines receive clean, structured data from real-world noisy call audio.

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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New model: Solaria-3

Test our real-time and async transcription

2026 Meeting Assistant Report

Read more

MiFID II and FCA call recording: compliance for voice transcription in finance

Why speech-to-text accuracy matters upstream of your LLM

Telephony-audio robustness: why 8kHz narrowband calls break generic STT

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.

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From audio to knowledge

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