Automatic Punctuation and Truecasing with Python Speech-to-Text

TLDR: Automatic punctuation and truecasing transform raw speech-to-text into readable transcripts. This tutorial shows how to enable automatic punctuation and truecasing in Python for batch transcription and real-time speech-to-text.

What Are Automatic Punctuation and Truecasing in Speech-to-Text?

Automatic punctuation inserts punctuation marks such as periods, commas, and question marks into transcripts based on speech patterns and pauses. Truecasing (automatic capitalization) restores proper casing for sentence starts, proper nouns, and acronyms. Together, they transform raw speech-to-text output into readable, properly formatted text without manual editing. For example:

Without automatic formatting:

how are you doing today i am fine thanks for asking

With automatic formatting:

How are you doing today? I am fine, thanks for asking.

Punctuation and Capitalization for Batch & Streaming STT

This tutorial covers automatic punctuation and truecasing for both batch and real-time speech-to-text in Python:

• Batch Audio File Transcription with Leopard Speech-to-Text - For pre-recorded audio files (meeting recordings, documentation, interviews, podcasts)
• Real-Time Transcription with Cheetah Streaming Speech-to-Text - For live transcription (dictation software, live captions, real-time meeting transcription)

Both speech-to-text models enable automatic formatting with a single parameter: enable_automatic_punctuation=True and process audio on-device for low-latency transcription.

Prerequisites

• Python 3.9+
• Audio file (WAV, MP3, FLAC, or other common formats) for batch transcription or microphone access for real-time transcription
• Picovoice AccessKey from Picovoice Console

Batch Transcription with Punctuation and Capitalization

Leopard Speech-to-Text transcribes pre-recorded audio files with automatic punctuation and truecasing.

Install the Speech-to-Text SDK

Install Leopard Speech-to-Text SDK using pip:

pip install pvleopard

Full Code: Audio File Transcription with Automatic Punctuation and Truecasing

Here's the complete working code that transcribes an audio file with automatic formatting:

import argparse

from pvleopard import create


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--access_key',
        required=True,
        help='AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)')
    parser.add_argument(
        '--audio_path',
        required=True,
        help='Absolute path to audio file')

    args = parser.parse_args()

    leopard = create(
        access_key=args.access_key,
        enable_automatic_punctuation=True)

    transcript, words = leopard.process_file(args.audio_path)
    print(transcript)

    leopard.delete()


if __name__ == '__main__':
    main()

Setting enable_automatic_punctuation=True enables automatic punctuation insertion and truecasing.

Run the Batch Transcription Script

Replace ${ACCESS_KEY} with your AccessKey from Picovoice Console and path/to/your/audio.wav with your audio file path to run the script:

python leopard_formatted.py \
    --access_key ${ACCESS_KEY} \
    --audio_path path/to/your/audio.wav

Real-Time Streaming Transcription with Punctuation and Capitalization

Cheetah Streaming Speech-to-Text transcribes audio streams in real-time with automatic punctuation and truecasing.

Install the required Python Libraries

Install Python Cheetah Streaming Speech-to-Text SDK pvcheetah and Picovoice Python recorder library pvrecorder, using pip:

pip install pvcheetah pvrecorder

Full Code: Real-Time Transcription with Automatic Punctuation and Truecasing

Here's the complete code for real-time streaming transcription with automatic formatting:

import argparse

from pvcheetah import create
from pvrecorder import PvRecorder


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--access_key',
        required=True,
        help='AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)')
    parser.add_argument(
        '--audio_device_index',
        type=int,
        default=-1,
        help='Index of input audio device')

    args = parser.parse_args()

    cheetah = create(
        access_key=args.access_key,
        enable_automatic_punctuation=True)

    recorder = PvRecorder(
        frame_length=cheetah.frame_length,
        device_index=args.audio_device_index)
    recorder.start()

    print('Listening... (press Ctrl+C to stop)')

    try:
        while True:
            partial_transcript, is_endpoint = cheetah.process(recorder.read())
            print(partial_transcript, end='', flush=True)
            if is_endpoint:
                print(cheetah.flush())
    except KeyboardInterrupt:
        pass
    finally:
        recorder.stop()
        recorder.delete()
        cheetah.delete()


if __name__ == '__main__':
    main()

Setting enable_automatic_punctuation=True enables automatic punctuation insertion and truecasing while the is_endpoint flag detects natural pauses in speech to structure output into readable segments.

Run the Real-Time Transcription Script

Replace ${ACCESS_KEY} with your AccessKey from Picovoice Console to run the script:

python cheetah_formatted.py \
     --access_key ${ACCESS_KEY}

Real-Time Punctuation Accuracy and Performance

In batch transcription, speech-to-text models can use the full audio context to generate transcripts, which often leads to higher overall accuracy for word recognition, truecasing and punctuation. In contrast, real-time transcription models must emit text as audio arrives, making accurate punctuation and capitalization decisions more challenging without seeing future speech.

A common way to evaluate punctuation quality in streaming speech-to-text is Punctuation Error Rate (PER), which measures how closely predicted punctuation aligns with reference transcripts. Lower PER reflects more reliable sentence boundaries and capitalization while preserving real-time responsiveness — a key requirement for live captions, meeting notes, and interactive voice applications.

Cheetah Streaming Speech-to-Text delivers a lower Punctuation Error Rate than major cloud-based streaming STT services, with less than half the PER of Google Streaming Speech-to-Text.

In practice, even small improvements in PER can significantly reduce manual cleanup and improve readability in live transcription workflows.

Start building speech recognition applications with automatic punctuation and truecasing for professional-quality transcripts today!