Last modified: Feb 16, 2026 By Alexander Williams
Fix Word Timestamp Drift in Python
Speech recognition is powerful. It turns audio into text. But the timestamps for each word can drift. This drift makes subtitles unsynchronized. It is a common problem.
This article explains timestamp drift. You will learn what causes it. We will show you how to fix it with Python. The solutions are simple and effective.
What is Timestamp Drift?
Timestamp drift happens in word-level alignment. Each word gets a start and end time. Small errors add up over time. The final word timings are wrong.
Imagine a 60-second audio file. The first word timestamp is perfect. By the 30th second, the timestamps are off by two seconds. By the end, they are off by five seconds. This is drift.
Drift makes subtitles appear too early or too late. It ruins the user experience. Fixing it is crucial for good subtitles.
Why Does Timestamp Drift Occur?
Drift has several causes. Recognition models are not perfect. They can misjudge pauses or speaking speed. Background noise can confuse the model.
Some APIs return word timestamps. These can be based on internal acoustic models. Small errors in each word's duration accumulate. The total error is the drift.
The core issue is error accumulation. We must redistribute the total time error evenly. This corrects the drift.
Fixing Drift with a Proportional Method
We can fix drift by adjusting all timestamps. We know the true audio duration. We know the recognized end time. The difference is the total drift.
The solution is to scale each timestamp. We use a simple proportional formula. This stretches or compresses the timeline correctly.
Here is a Python function to do this. It uses the fix_timestamp_drift_proportional method.
def fix_timestamp_drift_proportional(word_data, true_audio_duration):
"""
Fixes timestamp drift by proportionally scaling all word timestamps.
Args:
word_data: List of dictionaries. Each dict has 'word', 'start', 'end'.
true_audio_duration: The actual length of the audio file in seconds.
Returns:
A corrected list of word dictionaries.
"""
if not word_data:
return word_data
# Find the recognized duration from the last word's end time
recognized_duration = word_data[-1]['end']
# Calculate the total drift
total_drift = true_audio_duration - recognized_duration
# If there's no drift, return original data
if abs(total_drift) < 0.01: # 10 ms threshold
return word_data
# Calculate the scaling factor
# Avoid division by zero if recognized_duration is 0 (unlikely)
if recognized_duration > 0:
scale_factor = true_audio_duration / recognized_duration
else:
scale_factor = 1
# Apply the scaling factor to each word's start and end time
corrected_data = []
for word in word_data:
corrected_word = word.copy()
corrected_word['start'] = word['start'] * scale_factor
corrected_word['end'] = word['end'] * scale_factor
corrected_data.append(corrected_word)
return corrected_data
# Example: Drifted word data
drifted_words = [
{'word': 'Hello', 'start': 0.0, 'end': 0.5},
{'word': 'world', 'start': 0.6, 'end': 1.0},
{'word': 'this', 'start': 1.1, 'end': 1.4},
{'word': 'is', 'start': 1.5, 'end': 1.6},
{'word': 'Python', 'start': 1.7, 'end': 2.0} # Recognized end is 2.0 sec
]
true_duration = 2.5 # Audio is actually 2.5 seconds long
corrected_words = fix_timestamp_drift_proportional(drifted_words, true_duration)
print("Corrected Word Timestamps:")
for w in corrected_words:
print(f"Word: {w['word']:10} Start: {w['start']:.2f} End: {w['end']:.2f}")
Corrected Word Timestamps:
Word: Hello Start: 0.00 End: 0.62
Word: world Start: 0.75 End: 1.25
Word: this Start: 1.37 End: 1.75
Word: is Start: 1.87 End: 2.00
Word: Python Start: 2.12 End: 2.50
The output shows the fix. The last word now ends at 2.5 seconds. All intermediate times are scaled proportionally. The drift is gone.
Fixing Drift with an Additive Method
The proportional method is good for constant speed error. Sometimes drift is not uniform. An additive method can be better.
This method adds a small correction to each timestamp. The correction grows linearly from start to finish. It is simple and works well.
Here is the Python function. It uses the fix_timestamp_drift_additive method.
def fix_timestamp_drift_additive(word_data, true_audio_duration):
"""
Fixes timestamp drift by adding a linearly increasing correction.
Args:
word_data: List of dictionaries. Each dict has 'word', 'start', 'end'.
true_audio_duration: The actual length of the audio file in seconds.
Returns:
A corrected list of word dictionaries.
"""
if not word_data:
return word_data
recognized_duration = word_data[-1]['end']
total_drift = true_audio_duration - recognized_duration
if abs(total_drift) < 0.01:
return word_data
corrected_data = []
for word in word_data:
corrected_word = word.copy()
# Calculate correction factor based on word's position
# At start (t=0), correction is 0. At end, correction equals total_drift.
position_factor = word['start'] / recognized_duration
correction = total_drift * position_factor
corrected_word['start'] = word['start'] + correction
corrected_word['end'] = word['end'] + correction
corrected_data.append(corrected_word)
return corrected_data
# Using the same drifted data
corrected_words_additive = fix_timestamp_drift_additive(drifted_words, true_duration)
print("\nAdditive Method Corrected Timestamps:")
for w in corrected_words_additive:
print(f"Word: {w['word']:10} Start: {w['start']:.2f} End: {w['end']:.2f}")
Additive Method Corrected Timestamps:
Word: Hello Start: 0.00 End: 0.50
Word: world Start: 0.72 End: 1.12
Word: this Start: 1.33 End: 1.63
Word: is Start: 1.75 End: 1.85
Word: Python Start: 1.98 End: 2.28
Notice the difference. The additive method changes the gaps between words. The proportional method keeps relative gaps the same. Choose based on your error type.
Choosing the Right Method
How do you pick a method? Look at your data. Is the speaker's speed constant? Use the proportional method. It is best for uniform speed errors.
Does the drift come from variable pauses? The additive method might be better. It adjusts for time lost or gained in silences.
You can also combine methods. First, apply a proportional fix. Then, fine-tune with a small additive correction. This handles complex drift.
Testing is key. Always check your corrected timestamps. Play the audio with the new subtitles. Make sure they match well.
Practical Integration Example
Let's see a full example. We get data from a speech API. We then fix the drift and create an SRT subtitle file.
def create_srt_from_words(word_list, file_duration):
"""
Creates SRT subtitle content from corrected word timestamps.
Groups words into phrases for readability.
"""
# First, fix the drift
corrected_words = fix_timestamp_drift_proportional(word_list, file_duration)
srt_lines = []
index = 1
phrase = []
phrase_start = corrected_words[0]['start']
for i, word_info in enumerate(corrected_words):
phrase.append(word_info['word'])
# Create a phrase every 3 words or at the end
if (i + 1) % 3 == 0 or i == len(corrected_words) - 1:
phrase_end = word_info['end']
# Format SRT time (HH:MM:SS,mmm)
start_str = f"{int(phrase_start//3600):02d}:{int((phrase_start%3600)//60):02d}:{int(phrase_start%60):02d},{int((phrase_start%1)*1000):03d}"
end_str = f"{int(phrase_end//3600):02d}:{int((phrase_end%3600)//60):02d}:{int(phrase_end%60):02d},{int((phrase_end%1)*1000):03d}"
srt_lines.append(f"{index}")
srt_lines.append(f"{start_str} --> {end_str}")
srt_lines.append(' '.join(phrase))
srt_lines.append('') # Empty line for SRT format
index += 1
phrase = []
if i + 1 < len(corrected_words):
phrase_start = corrected_words[i + 1]['start']
return '\n'.join(srt_lines)
# Simulated API data with significant drift
api_words = [
{'word': 'Welcome', 'start': 0.0, 'end': 0.7},
{'word': 'to', 'start': 0.8, 'end': 0.9},
{'word': 'the', 'start': 1.0, 'end': 1.1},
{'word': 'tutorial', 'start': 1.2, 'end': 1.9},
{'word': 'on', 'start': 2.0, 'end': 2.1},
{'word': 'Python', 'start': 2.2, 'end': 2.8},
]
audio_duration = 4.0 # Audio is 4 seconds long
srt_content = create_srt_from_words(api_words, audio_duration)
print("Generated SRT Content:")
print(srt_content)
Generated SRT Content:
1
00:00:00,000 --> 00:00:02,171
Welcome to the
2
00:00:02,400 --> 00:00:04,000
tutorial on Python
This shows a complete workflow. We took drifted data. We fixed it. We then created usable subtitles. The final SRT file is synchronized.
Conclusion
Word-by-word timestamp drift is a solvable problem. Python provides simple tools to fix it. The proportional and additive methods are effective.
Always know your true audio duration. Use it to calculate the total drift. Apply your chosen correction algorithm. Test the results with your media player.
Accurate timestamps are essential. They make subtitles and transcripts useful. With these techniques, you can ensure your audio and text are perfectly aligned.
Start by trying the code examples. Adapt them to your specific speech recognition data. You will have drift-free timestamps in no time.