Principle:Togethercomputer Together python Speech To Text
| Knowledge Sources | |
|---|---|
| Domains | Audio, Speech_To_Text |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
Principle for converting audio recordings into text transcriptions using automatic speech recognition models.
Description
Speech-to-text (automatic speech recognition) converts audio input into textual transcriptions. Advanced features include word-level and segment-level timestamps, speaker diarization (identifying who said what), and language detection. The process involves acoustic modeling, language modeling, and decoding to produce text from audio waveforms.
Usage
Apply this principle when you need to convert audio recordings to text for applications such as meeting transcription, subtitle generation, voice search, or accessibility tools. Use diarization when multiple speakers are present.
Theoretical Basis
Speech-to-text follows an acoustic-to-text pipeline:
Pseudo-code Logic:
# Abstract STT pipeline
transcript = transcribe(
audio=audio_file,
model=asr_model,
language=lang_hint,
granularity=timestamp_level,
diarize=enable_speakers,
)
# Access results
text = transcript.text
segments = transcript.segments # with timestamps
speakers = transcript.speaker_segments # with speaker IDs
Key considerations:
- Language Hint: Providing ISO-639-1 code improves accuracy and latency
- Timestamp Granularity: Word-level for subtitles, segment-level for summaries
- Diarization: Identifies distinct speakers; useful for multi-party recordings
- Temperature: Lower values (0.0) produce more deterministic output