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models.py
---------
Single source of truth for every model used in the pipeline.
Component Model Params Device
----------- --------------------------------------- ------- ------
STT openai/whisper-base.en 74 M GPU/CPU (fp16/fp32)
LLM HuggingFaceTB/SmolLM2-1.7B-Instruct 1.7 B GPU/CPU (fp16/fp32)
TTS facebook/mms-tts-eng (VITS) ~430 M CPU (fp32)
VAD silero-vad 2 MB CPU
All models are loaded once at startup. Inference methods are synchronous
(blocking) — call them from a thread-pool executor inside async handlers.
"""
from __future__ import annotations
import logging
import time
from typing import Optional
import numpy as np
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
VitsModel,
WhisperForConditionalGeneration,
WhisperProcessor,
)
logger = logging.getLogger(__name__)
# --------------------------------------------------------------------------- #
# Model IDs (swap here for lighter / heavier variants) #
# --------------------------------------------------------------------------- #
STT_MODEL_ID = "openai/whisper-base.en" # 74 M — swap tiny.en for CPU-only
LLM_MODEL_ID = "HuggingFaceTB/SmolLM2-1.7B-Instruct" # 1.7 B — swap 360M for CPU-only
TTS_MODEL_ID = "facebook/mms-tts-eng" # VITS, 16 kHz output
STT_SR = 16_000 # Whisper expects 16 kHz
TTS_SR = 16_000 # MMS-TTS-ENG native output rate
MAX_NEW_TOKENS = 256 # Cap LLM response length for voice interaction
# --------------------------------------------------------------------------- #
# ModelManager #
# --------------------------------------------------------------------------- #
class ModelManager:
"""Load and expose all model inference in one place."""
def __init__(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.compute_dtype = (
torch.float16 if self.device == "cuda" else torch.float32
)
logger.info("Device: %s | dtype: %s", self.device, self.compute_dtype)
# TTS always runs on CPU (saves GPU VRAM for STT + LLM)
self.tts_device = "cpu"
self._load_vad()
self._load_stt()
self._load_llm()
self._load_tts()
logger.info("✅ All models ready.")
# ------------------------------------------------------------------ #
# VAD #
# ------------------------------------------------------------------ #
def _load_vad(self):
logger.info("Loading VAD (silero-vad) …")
from silero_vad import VADIterator, load_silero_vad # type: ignore
vad_model = load_silero_vad()
# silence_ms=700 → 700 ms of quiet ends an utterance
self.vad_iter = VADIterator(
vad_model,
sampling_rate=STT_SR,
threshold=0.50,
min_silence_duration_ms=700,
speech_pad_ms=50,
)
logger.info("VAD ready.")
def vad_reset(self):
"""Reset VAD state between conversations."""
self.vad_iter.reset_states()
# ------------------------------------------------------------------ #
# STT — Whisper #
# ------------------------------------------------------------------ #
def _load_stt(self):
logger.info("Loading STT: %s …", STT_MODEL_ID)
self.stt_processor = WhisperProcessor.from_pretrained(STT_MODEL_ID)
self.stt_model = WhisperForConditionalGeneration.from_pretrained(
STT_MODEL_ID,
torch_dtype=self.compute_dtype,
).to(self.device)
self.stt_model.eval()
logger.info("STT ready on %s.", self.device)
def transcribe(self, audio_float32: np.ndarray) -> str:
"""
Transcribe a 16 kHz float32 numpy array to text.
Parameters
----------
audio_float32 : np.ndarray shape (N,), dtype float32, range [-1, 1]
"""
t0 = time.perf_counter()
inputs = self.stt_processor(
audio_float32,
sampling_rate=STT_SR,
return_tensors="pt",
)
features = inputs.input_features.to(
device=self.device, dtype=self.compute_dtype
)
with torch.no_grad():
ids = self.stt_model.generate(features)
text = self.stt_processor.batch_decode(ids, skip_special_tokens=True)[0].strip()
logger.info("STT (%.0f ms): '%s'", (time.perf_counter() - t0) * 1000, text)
return text
# ------------------------------------------------------------------ #
# LLM — SmolLM2 #
# ------------------------------------------------------------------ #
def _load_llm(self):
logger.info("Loading LLM: %s …", LLM_MODEL_ID)
self.llm_tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_ID)
self.llm = AutoModelForCausalLM.from_pretrained(
LLM_MODEL_ID,
torch_dtype=self.compute_dtype,
).to(self.device)
self.llm.eval()
logger.info("LLM ready on %s.", self.device)
def build_prompt(self, messages: list[dict]) -> str:
"""Apply the model's chat template and return a formatted string."""
return self.llm_tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
def tokenize(self, prompt: str) -> dict:
"""Tokenize a prompt string → dict of tensors on self.device."""
enc = self.llm_tokenizer(prompt, return_tensors="pt")
return {k: v.to(self.device) for k, v in enc.items()}
# ------------------------------------------------------------------ #
# TTS — MMS-TTS-ENG (VITS) #
# ------------------------------------------------------------------ #
def _load_tts(self):
logger.info("Loading TTS: %s …", TTS_MODEL_ID)
self.tts_tokenizer = AutoTokenizer.from_pretrained(TTS_MODEL_ID)
self.tts_model = VitsModel.from_pretrained(TTS_MODEL_ID) # fp32 on CPU
self.tts_model.eval()
self.tts_sample_rate: int = self.tts_model.config.sampling_rate # 16 000
logger.info("TTS ready on CPU (sample_rate=%d Hz).", self.tts_sample_rate)
def synthesize(self, text: str) -> Optional[bytes]:
"""
Synthesize text → raw PCM bytes (int16, mono, 16 kHz).
Returns None if text is empty or synthesis fails.
"""
text = text.strip()
if not text:
return None
t0 = time.perf_counter()
try:
inputs = self.tts_tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = self.tts_model(**inputs)
# waveform: (1, T) float32 in roughly [-1, 1]
wav: np.ndarray = output.waveform.squeeze().cpu().numpy()
# Convert float32 → int16 PCM
wav_int16 = np.clip(wav * 32_767.0, -32_768, 32_767).astype(np.int16)
pcm_bytes = wav_int16.tobytes()
logger.debug(
"TTS (%.0f ms): %d chars → %d bytes",
(time.perf_counter() - t0) * 1000,
len(text),
len(pcm_bytes),
)
return pcm_bytes
except Exception as exc:
logger.error("TTS synthesis failed for %r: %s", text[:60], exc)
return None
# ------------------------------------------------------------------ #
# Convenience #
# ------------------------------------------------------------------ #
def warm_up(self):
"""Run a tiny inference on each model to pre-JIT all kernels."""
logger.info("Warming up models …")
silence = np.zeros(STT_SR, dtype=np.float32)
self.transcribe(silence)
dummy_msgs = [
{"role": "system", "content": "You are helpful."},
{"role": "user", "content": "hi"},
]
# Just tokenize, don't run full generation during warm-up
self.tokenize(self.build_prompt(dummy_msgs))
self.synthesize("Hello.")
logger.info("Warm-up complete.")
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