Complete Workflow
Step 1: Environmental preparation
- Choose PyTorch/MLX (Runtime) or Rust (Production Server)
- Install the corresponding version of the model package (moshi-mlx or moshi-server)
- downloading
stt-2.6b-enHigh Precision English Modeling
Step 2: Audio Input Configuration
- Real-time microphone input: add
--micparameters - File Input: Specify the path of WAV/MP3 file.
- Network Streaming Input: Transferring audio data blocks via WebSocket
Key Parameter Settings
| parameters | clarification | recommended value |
|---|---|---|
| -temp | sampling temperature | 0 (deterministic output) |
| -vad-thresh | speech activity threshold | 0.3 (adjusted upwards for noisy environments) |
| -max-delay | Maximum Allowable Delay | 500 (milliseconds) |
pass (a bill or inspection etc)--output-jsonStructured results can be obtained to contain:
- transcript: complete transcription of the text
- word_timings: array of word-level timestamps
- confidence: confidence score
Output Post-Processing Recommendations
Subtitle file generation:
- Convert timestamps to SRT/VTT format
- utilization
ffmpegEmbedded video - Adjust the length of each subtitle line (3-5 seconds recommended)
Real-time display optimization:
- Push to front-end via WebSocket
- Add 0.2 second buffer to avoid jitter
- Enhance readability by highlighting the word currently being read aloud.
This answer comes from the articleKyutai: Speech to text real-time conversion toolThe
May not be reproduced without permission:AI productivity tools " What are the exact steps for real-time subtitle generation using delayed-streams-modeling?
