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SentenceConfig is a dataclass that controls how transcribed text is split into sentences based on punctuation, silence gaps, word count, or duration.

​
Class Definition

from parakeet_mlx import SentenceConfig

config = SentenceConfig(
    max_words=None,
    silence_gap=None,
    max_duration=None
)

​
Fields

​
max_words
int | None
default:"None"
Maximum number of words allowed in a single sentence. When the next token would exceed this limit, a sentence break is created.Set to None to disable word-based splitting.Example: max_words=30
​
silence_gap
float | None
default:"None"
Minimum silence duration (in seconds) that triggers a sentence split. When the gap between tokens exceeds this threshold, a new sentence is created.Set to None to disable silence-based splitting.Example: silence_gap=5.0
​
max_duration
float | None
default:"None"
Maximum duration (in seconds) allowed for a single sentence. When a sentence reaches this duration, it is split even if no other conditions are met.Set to None to disable duration-based splitting.Example: max_duration=40.0

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Splitting Behavior

Sentences are automatically split at punctuation marks (., !, ?, 。, ？, ！). Additional splits occur when any of the following conditions are met:
  1. Punctuation: Token contains sentence-ending punctuation
  2. Word limit: Next token would exceed max_words (if set)
  3. Silence gap: Gap between current and next token exceeds silence_gap (if set)
  4. Duration limit: Sentence duration exceeds max_duration (if set)

​
Examples

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Default Configuration

By default, sentences are only split at punctuation: 
from parakeet_mlx import from_pretrained, DecodingConfig, SentenceConfig

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

# Default: only split on punctuation
config = DecodingConfig(sentence=SentenceConfig())
result = model.transcribe("audio.wav", decoding_config=config)

for sentence in result.sentences:
    print(sentence.text)

​
Limit Words per Sentence

Split long sentences to keep them under 30 words: 
from parakeet_mlx import from_pretrained, DecodingConfig, SentenceConfig

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

config = DecodingConfig(
    sentence=SentenceConfig(max_words=30)
)

result = model.transcribe("audio.wav", decoding_config=config)

for sentence in result.sentences:
    word_count = len([t for t in sentence.tokens if " " in t.text])
    print(f"{word_count} words: {sentence.text}")

​
Split on Long Silences

Create sentence breaks when silence exceeds 5 seconds: 
from parakeet_mlx import from_pretrained, DecodingConfig, SentenceConfig

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

config = DecodingConfig(
    sentence=SentenceConfig(silence_gap=5.0)
)

result = model.transcribe("audio.wav", decoding_config=config)

for sentence in result.sentences:
    print(f"[{sentence.start:.1f}s - {sentence.end:.1f}s] {sentence.text}")

​
Limit Sentence Duration

Keep sentences under 40 seconds: 
from parakeet_mlx import from_pretrained, DecodingConfig, SentenceConfig

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

config = DecodingConfig(
    sentence=SentenceConfig(max_duration=40.0)
)

result = model.transcribe("audio.wav", decoding_config=config)

for sentence in result.sentences:
    print(f"Duration: {sentence.duration:.1f}s - {sentence.text}")

​
Combine Multiple Constraints

Use all splitting criteria together: 
from parakeet_mlx import from_pretrained, DecodingConfig, SentenceConfig

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

config = DecodingConfig(
    sentence=SentenceConfig(
        max_words=30,       # Max 30 words
        silence_gap=5.0,    # Split on 5+ second silences
        max_duration=40.0   # Max 40 second duration
    )
)

result = model.transcribe("audio.wav", decoding_config=config)

for i, sentence in enumerate(result.sentences, 1):
    print(f"Sentence {i}: [{sentence.start:.1f}s - {sentence.end:.1f}s]")
    print(f"  {sentence.text}")
    print()

​
Use Cases

​
Subtitle Generation

For video subtitles, limit words and duration to fit on screen: 
config = DecodingConfig(
    sentence=SentenceConfig(
        max_words=15,       # Short lines for readability
        max_duration=5.0    # Quick screen changes
    )
)

​
Meeting Transcription

For meeting notes, split on natural pauses: 
config = DecodingConfig(
    sentence=SentenceConfig(
        silence_gap=3.0,    # Split on speaker pauses
        max_duration=30.0   # Keep paragraphs manageable
    )
)

​
Podcast Transcription

For long-form content with natural flow: 
config = DecodingConfig(
    sentence=SentenceConfig(
        max_words=40,       # Allow longer sentences
        silence_gap=8.0     # Only split on significant pauses
    )
)

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