How to Convert Voice to Text with Timestamps: Complete Guide

Introduction

• Precise video editing
• Searchable transcripts
• Subtitle generation
• Meeting notes with time references
• Content repurposing

Problem: Why Timestamps Matter

The Challenge Without Timestamps

Speaker 1: Welcome everyone to today's meeting.
Speaker 2: Thanks for joining us.
Speaker 1: Let's start with the quarterly review.

• ❌ Can't find specific moments in audio/video
• ❌ Difficult to create subtitles
• ❌ Hard to reference exact quotes
• ❌ No way to jump to specific sections
• ❌ Limited editing capabilities

What Timestamps Solve

[00:00:05] Speaker 1: Welcome everyone to today's meeting.
[00:00:12] Speaker 2: Thanks for joining us.
[00:00:18] Speaker 1: Let's start with the quarterly review.

• ✅ Jump directly to any moment in audio/video
• ✅ Generate accurate subtitles (SRT, VTT)
• ✅ Reference exact quotes with time codes
• ✅ Edit videos with precision
• ✅ Create searchable, navigable transcripts

Solution: How to Get Timestamps

Method 1: Using SayToWords (Recommended)

1. Upload your audio/video file
   • Supports MP3, WAV, M4A, MP4, MOV, and more
   • Drag & drop or click to upload
2. Select language and model
   • Choose the spoken language
   • Select transcription model (Fastest, Balanced, or Accurate)
3. Enable speaker recognition (optional)
   • For multi-speaker audio
   • Automatically labels speakers
4. Transcribe
   • Click "Transcribe" and wait for processing
   • Timestamps are generated automatically
5. Export with timestamps
   • SRT: Subtitle format with timestamps
   • VTT: Web video text tracks
   • TXT: Plain text with time markers
   • DOCX: Word document with timestamps
   • PDF: Formatted document with time codes

Method 2: Using OpenAI Whisper (Technical)

import whisper

# Load model
model = whisper.load_model("base")

# Transcribe with timestamps
result = model.transcribe(
    "audio.mp3",
    word_timestamps=True  # Enable word-level timestamps
)

# Access timestamps
for segment in result["segments"]:
    start = segment["start"]  # Start time in seconds
    end = segment["end"]      # End time in seconds
    text = segment["text"]    # Transcribed text
    
    print(f"[{start:.2f}s - {end:.2f}s] {text}")
    
    # Word-level timestamps
    if "words" in segment:
        for word_info in segment["words"]:
            word = word_info["word"]
            word_start = word_info["start"]
            word_end = word_info["end"]
            print(f"  {word}: {word_start:.2f}s - {word_end:.2f}s")

Method 3: Using Google Speech-to-Text API

from google.cloud import speech_v1
from google.cloud.speech_v1 import enums

client = speech_v1.SpeechClient()

config = {
    "encoding": enums.RecognitionConfig.AudioEncoding.MP3,
    "sample_rate_hertz": 16000,
    "language_code": "en-US",
    "enable_word_time_offsets": True,  # Enable timestamps
}

with open("audio.mp3", "rb") as audio_file:
    content = audio_file.read()

audio = {"content": content}
response = client.recognize(config, audio)

for result in response.results:
    for alternative in result.alternatives:
        print(f"Transcript: {alternative.transcript}")
        for word_info in alternative.words:
            start_time = word_info.start_time.seconds + word_info.start_time.nanos / 1e9
            end_time = word_info.end_time.seconds + word_info.end_time.nanos / 1e9
            print(f"  {word_info.word}: {start_time:.2f}s - {end_time:.2f}s")

Why SayToWords

Advantages for Timestamped Transcription

• ✅ No coding required
• ✅ Timestamps included by default
• ✅ Word-level and segment-level precision

• ✅ SRT: Industry-standard subtitle format
• ✅ VTT: Web-compatible video text tracks
• ✅ TXT: Plain text with time markers
• ✅ DOCX: Editable Word documents
• ✅ PDF: Professional formatted output

• ✅ Visual editor to adjust timestamps
• ✅ Easy editing of transcribed text
• ✅ Speaker labeling with timestamps
• ✅ No technical knowledge needed

• ✅ Powered by advanced AI models
• ✅ Handles multiple languages
• ✅ Works with noisy audio
• ✅ Supports long-form content

• ✅ Free tier available
• ✅ Transparent pricing
• ✅ No per-minute API costs
• ✅ Unlimited file processing

Use Cases Where SayToWords Excels

• Generate subtitles for YouTube videos
• Create searchable transcripts for podcasts
• Repurpose content with precise time references

• Transcribe interviews with time markers
• Analyze focus groups with timestamped quotes
• Document research sessions accurately

• Meeting notes with exact time references
• Conference transcription with timestamps
• Training session documentation

• Create captions for video content
• Generate accessible transcripts
• Support hearing-impaired audiences

Example: Complete Workflow

Example: Transcribing a Podcast Episode

• File: podcast-episode-42.mp3 (30 minutes)
• Format: MP3, 44.1kHz, stereo

• Language: English
• Model: Balanced (good accuracy and speed)
• Speaker Recognition: Enabled (2 speakers detected)

• Processing time: ~3 minutes
• Result: Full transcript with timestamps

[00:00:00] Host: Welcome to Tech Talk, I'm your host Sarah.
[00:00:05] Host: Today we're discussing AI transcription.
[00:00:12] Guest: Thanks for having me, Sarah. It's great to be here.
[00:00:18] Host: Let's start with the basics. What is speech-to-text?
[00:00:25] Guest: Speech-to-text converts spoken words into written text...

1
00:00:00,000 --> 00:00:05,000
Welcome to Tech Talk, I'm your host Sarah.

2
00:00:05,000 --> 00:00:12,000
Today we're discussing AI transcription.

3
00:00:12,000 --> 00:00:18,000
Thanks for having me, Sarah. It's great to be here.

WEBVTT

00:00:00.000 --> 00:00:05.000
Welcome to Tech Talk, I'm your host Sarah.

00:00:05.000 --> 00:00:12.000
Today we're discussing AI transcription.

[00:00:00] Host: Welcome to Tech Talk, I'm your host Sarah.
[00:00:05] Host: Today we're discussing AI transcription.
[00:00:12] Guest: Thanks for having me, Sarah. It's great to be here.

• YouTube Upload: Use SRT file for automatic captions
• Blog Post: Extract quotes with timestamps for references
• Show Notes: Create searchable episode notes
• Social Media: Share timestamped highlights

Comparison: Solutions for Timestamped Transcription

SayToWords vs. Other Solutions

Detailed Comparison

SayToWords

• ✅ No coding required
• ✅ Visual editor for timestamp adjustment
• ✅ Multiple export formats out of the box
• ✅ Free tier available
• ✅ Handles long audio automatically
• ✅ Speaker recognition built-in

• ⚠️ Requires internet connection
• ⚠️ File size limits on free tier

• Content creators
• Non-technical users
• Quick transcription needs
• Multiple format exports

OpenAI Whisper

• ✅ Free and open-source
• ✅ Runs locally (privacy)
• ✅ Highly accurate
• ✅ Supports many languages
• ✅ Word-level timestamps

• ❌ Requires Python knowledge
• ❌ No built-in UI
• ❌ Manual format conversion needed
• ❌ GPU recommended for speed

• Developers
• Privacy-conscious users
• Custom integrations
• Batch processing

Google Speech-to-Text

• ✅ High accuracy
• ✅ Real-time streaming support
• ✅ Enterprise features
• ✅ Word-level timestamps

• ❌ Requires API setup
• ❌ Pay-per-use pricing
• ❌ No user interface
• ❌ Complex for beginners

• Enterprise applications
• Real-time transcription
• Integrated applications
• High-volume processing

AssemblyAI

• ✅ Good accuracy
• ✅ Speaker diarization
• ✅ Sentiment analysis
• ✅ Word-level timestamps

• ❌ Requires API setup
• ❌ Pay-per-use pricing
• ❌ No user interface
• ❌ More expensive

• Enterprise use cases
• Advanced features needed
• Integrated workflows

Best Practices for Timestamped Transcription

1. Choose the Right Tool

• For quick, one-off transcriptions: Use SayToWords
• For privacy-sensitive content: Use Whisper locally
• For enterprise integration: Use Google STT or AssemblyAI API

2. Optimize Audio Quality

• Record in quiet environments
• Use good microphones
• Minimize background noise
• Ensure clear speech

3. Select Appropriate Model

• Fastest: Quick previews, low accuracy needs
• Balanced: Most use cases (recommended)
• Accurate: High-stakes content, maximum precision

4. Review and Edit Timestamps

• Check timestamp accuracy
• Adjust segment boundaries if needed
• Verify speaker labels
• Correct transcription errors

5. Export in Multiple Formats

• SRT: For video platforms (YouTube, Vimeo)
• VTT: For web players
• TXT: For reading and editing
• DOCX: For professional documents
• PDF: For sharing and archiving

6. Use Timestamps Effectively

• Create clickable transcripts
• Generate highlight reels
• Build searchable content libraries
• Reference specific moments accurately

Common Questions

Q: How accurate are timestamps?

Q: Can I adjust timestamps manually?

• Adjust segment start/end times
• Merge or split segments
• Fine-tune timestamp accuracy

Q: Do timestamps work for all languages?

Q: What's the difference between SRT and VTT?

• SRT: Traditional subtitle format, widely supported
• VTT: Web Video Text Tracks, HTML5 standard, supports styling

Q: Can I get timestamps for live/streaming audio?

• SayToWords: Basic support for uploaded files
• Google STT: Full streaming support with timestamps
• AssemblyAI: Real-time transcription with timestamps

Q: How do timestamps help with video editing?

• Jump directly to specific moments
• Create highlight reels
• Add captions automatically
• Reference exact quotes
• Build searchable video libraries

Conclusion

1. Timestamps are essential for professional transcription workflows
2. SayToWords offers the easiest solution with automatic timestamp generation
3. Multiple export formats (SRT, VTT, TXT) serve different use cases
4. Word-level timestamps provide maximum precision
5. Visual editors make timestamp adjustment simple

• Try SayToWords with a sample audio file
• Export in different formats to see the options
• Use timestamps to create subtitles for your videos
• Build a searchable transcript library

Related Resources

• Getting Started with SayToWords
• How to Create Subtitles for YouTube Videos
• Speech-to-Text Accuracy Guide
• Best Speech-to-Text Tools in 2026