baari 0.1.1

End-of-turn detection for Urdu voice agents.
At every pause, it decides whether the speaker has finished (reply now) or is only pausing mid-sentence (keep listening).

baari (باری) means "turn" in Urdu. The table below shows that today's turn detectors often cut Urdu speakers off mid-sentence, or wait after they have finished. baari is a small audio model trained to get this right. It follows the Smart Turn recipe and takes the same input and output, so it can stand in for Smart Turn.

Status: the first model is out, on Hugging Face and as the baari package on PyPI. On the Urdu test set below, it beats every existing turn detector.

🎧 Results on Urdu

1,588 clips of real Urdu phone conversations, cut from the official held-out split of IndicVoices: 1,470 where the speaker had finished and 118 where they were pausing mid-sentence. Because most clips are "finished", a model that always says "finished" gets 92.6% plain accuracy, so the fair score is the average of the last two columns (balanced accuracy).

Scored on a MacBook Pro (M3 Pro), with the same audio preparation Pipecat uses. On the same clips, baari fp32 beats Indic Smart Turn tiny int8, the best existing model, by 3.3 points (paired bootstrap, 95% CI +0.3 to +6.4). int8 models are sensitive: their scores shift by a few points between Arm and x86 CPUs (baari int8 gets 77.0% on Arm and 74.9% on x86), and even tiny changes in how the audio is prepared flip about 1 answer in 100. fp32 results stay the same.

Use it

pip install baari

from baari import TurnDetector

detector = TurnDetector(threads=4)  # downloads baari's 9 MB int8 model once
detector.is_finished(audio, sample_rate=16000)  # mono audio (float or int16) that ends at the pause
detector.probability(audio, sample_rate=16000)  # the same decision as a number from 0 to 1

It needs only numpy and onnxruntime. With the default int8 file, one call takes about 12 ms on 4 CPU threads and 36 ms on one; TurnDetector(precision="fp32") takes 23 and 77 ms (MacBook Pro, M3 Pro). Use threads=1 on a server that handles many calls at once. baari needs 16 kHz audio; for 8 kHz phone audio, resample first, for example with soxr.resample(audio, 8000, 16000).

In Pipecat, baari replaces Smart Turn with one line, because it takes the same input and gives the same output:

from baari import model_path
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3

turn_analyzer = LocalSmartTurnAnalyzerV3(smart_turn_model_path=model_path())

TurnDetector(path=...) runs a local ONNX file instead of downloading one.

🛠️ Run it

uv sync --extra pipeline
echo "HF_TOKEN=..." > .env          # accept the IndicVoices terms on Hugging Face first
uv run baari scan --split valid     # index the conversation segments (text only)
uv run baari batches                # label batches for the LLM agents
# the agents write data/labels/valid/results/{a,b}/batch-*.jsonl
uv run baari build                  # cut the 8 s clips
uv run baari evaluate               # score the existing models

uv run baari scan --split train
uv run baari batches --split train
# the agents write data/labels/train/results/a/batch-*.jsonl
uv run baari build --split train --labelers a --delete-shards
uv run baari train                  # fine-tune Indic Smart Turn tiny, export fp32 and int8 ONNX

Credits · Contributing · BSD-2-Clause license
Made by Abdul Samad (abdulsamadsid1@gmail.com)