🗣️ Speech STT/TTS & Barge-In

SLM Voice Agent

Conversational voice assistant powered by local ONNX Speech-to-Text (STT) and Text-to-Speech (TTS) models with real-time barge-in capability.

🚀 Overview & Capabilities

Conversational voice assistant powered by local ONNX Speech-to-Text (STT) and Text-to-Speech (TTS) models with real-time barge-in capability.

Key Features

Offline Speech-to-Text (STT) transcription
Multilingual Text-to-Speech (TTS) synthesis (English, Hindi, Tamil, Telugu)
Real-time audio stream barge-in interrupt detection
Sub-50ms CPU synthesis latency

💻 Installation

Install the local package using pip:

Terminal

              $pip install slm-voice

🐙 Checkout from GitHub

Clone only this agent's folder from the monorepo using Git sparse-checkout — no need to download the full repository:

Option 1 — Sparse Checkout (Recommended)

Terminal — Git Sparse Checkout

              # 1. Create and enter a new directory
$ mkdir slm_voice && cd slm_voice

# 2. Initialise empty git repo and add remote
$ git init
$ git remote add origin https://github.com/t00114218-stack/SLMAgents.git

# 3. Enable sparse-checkout and set target folder
$ git sparse-checkout init --cone
$ git sparse-checkout set slm_voice

# 4. Pull only that agent's source
$ git pull origin main
            

Option 2 — Full Repository Clone

Terminal — Full Clone

              $ git clone https://github.com/t00114218-stack/SLMAgents.git
$ cd SLMAgents/slm_voice
            

💡 Tip: After checkout, install the package locally with pip install -e ./slm_voice to run in editable mode without publishing to PyPI.

⚙️ Configuration API

Constructor Parameters

Instantiate SLMVoiceAgent with performance and runtime options:

Parameter	Type / Default	Description
`stt_model_path`	str \| None	Path to local STT ONNX weights. Default: None.
`tts_model_path`	str \| None	Path to local TTS ONNX weights. Default: None.
`temperature`	float \| 0.7	Sampling temperature for speech synthesis. Default: 0.7.
`top_p`	float \| 0.9	Nucleus sampling cutoff threshold. Default: 0.9.
`max_tokens`	int \| 256	Maximum output token generation limit per response. Default: 256.
`cache_dir`	str \| None	Local directory for caching model weights. Default: None.
`n_threads`	int \| 4	Number of CPU threads for inference. Default: 4.

Methods

Method Signature	Return Type	Description
`process_speech_text(speech_transcript, ...)`	dict	Transcribes audio input or processes text transcript and returns synthesized speech payload.

Execution Parameters

Complete list of execution parameters accepted by the primary agent method:

Parameter	Type / Default	Description
`speech_transcript`	str	Input raw audio transcription string or speech input text.
`language`	str \| English	Target output synthesis language (English, Hindi, Tamil, Telugu). Default: English.
`system_prompt`	str \| None	Custom system prompt instruction. Default: None.
`user_input`	str \| None	Optional additional contextual text keys. Default: None.
`temperature`	float \| 0.7	Sampling temperature. Default: 0.7.
`top_p`	float \| 0.9	Nucleus sampling probability. Default: 0.9.
`max_tokens`	int \| 256	Maximum generation token limit. Default: 256.

Quick Start

Python Example

              from slm_voice import SLMVoiceAgent

voice = SLMVoiceAgent(temperature=0.7, top_p=0.9, max_tokens=256)
res = voice.process_speech_text(
    speech_transcript="Schedule a team sync meeting for tomorrow at 3 PM",
    language="English",
    system_prompt="Conversational voice assistant",
    user_input="Remind about Q3 project deadline",
    temperature=0.7,
    top_p=0.9,
    max_tokens=256
)
print(res)
            

🔍 Verified Output Logs

Diagnostic execution console output running locally on CPU:

Output Console

              → INPUT:
Speech Transcript: Schedule a team sync meeting for tomorrow at 3 PM
Language: English

← OUTPUT:
{
  'status': '200 OK',
  'transcript': 'Schedule a team sync meeting for tomorrow at 3 PM',
  'response': 'Scheduled team sync meeting for tomorrow at 3 PM.',
  'audio_synthesized': True,
  'barge_in_enabled': True
}
            