Troubleshooting Audio Corruption Within Buffer Limits
I've been wrestling with audio corruption issues (garbled, truncated audio) while trying to stay within the buffer limit (Max Tokens) when generating audio using the Higgs Audio model. I'm sharing my observations and seeking help in diagnosing and resolving this problem.
Initially, I experimented with different word limits, stepping down from 200 to 150, then to 100 words in my maximum buffer limit based on observation. I eventually figured out how to calculate the number of audio tokens being generated. Now, I consistently ensure that I stay within the buffer limit, using 100 words as my limit and a maximum buffer of 4096. I even implemented a method that accumulates and chunks the words into sentences while respecting the specified limit. However, despite adhering to the buffer limit, corruption still occurs, suggesting that other factors are at play. Let's dive deeper into the specifics and observations to understand this problem better.
Understanding the Buffer Limit and Observed Behavior
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Buffer Limit (Max Tokens):
4096 -
Based on this limit, the calculated maximum safe audio length (without corruption) is 81.92 seconds.
Math:
4096 (Max Tokens) / 50 (Tokens/sec) = 81.92 seconds
However, here's where things get interesting:
- I've observed chunks ranging from 90–120 seconds that did not exhibit corruption.
- This indicates that while the calculation is correct, corruption doesn't consistently occur above this threshold, which is puzzling.
- This discrepancy raises a significant question: Does the model potentially compress audio more efficiently in certain instances, allowing it to pack more audio into a tighter space? This could explain why longer chunks sometimes escape corruption, but it also highlights the complexity of the issue. To further investigate, I've been meticulously logging my calculated tokens, and I've noticed a pattern: on chunks that exceeded approximately 81 seconds, the audio is more likely to become corrupt, manifesting in various ways.
Detailed Corruption Observations
When the generated audio surpasses the calculated “safe” range, I've encountered several distinct types of corruption:
- Garbled sections within the audio chunks, where the sound becomes distorted and unintelligible.
- Truncated words, where words are cut off abruptly, making the audio sound incomplete.
- Missing words, where entire words are absent from the audio, disrupting the flow of the sentence.
- Mispronounced words, even if those same words were previously pronounced correctly in other chunks, indicating inconsistency in the audio generation.
- Sentences that gradually become softer (quieter) and slower towards the end of the chunk, creating an uneven and unnatural listening experience.
- Chunks that sound muffled, as if they were spoken inside a box, which significantly reduces the clarity and quality of the audio. These observations suggest that exceeding the buffer limit can lead to a range of audio quality issues, highlighting the need to understand and address the underlying causes. Now, let's delve into how tokens are calculated to gain a clearer picture of the technical aspects involved.
Understanding Token Calculation
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The model generates audio at a fixed architectural rate of 50 tokens per second (50 Hz). This is a fundamental characteristic of the model's design and audio processing mechanism.
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The number of tokens is calculated using a straightforward formula:
Duration (seconds) × 50 tokens/sec
For example:
Math:
85.20 seconds × 50 tokens/sec = 4,260 tokens
