Moonshine Tiny — LiteRT

Moonshine Tiny is a 27M-parameter encoder-decoder speech-recognition model from Moonshine AI (formerly Useful Sensors), introduced in Moonshine: Speech Recognition for Live Transcription and Voice Commands. It transcribes English speech and is designed for fast on-device inference.

This repository packages the model for LiteRT: a float32 model, an int8 model (dynamic-range-quantized decoder with a float32 encoder), and ahead-of-time compiled variants for a range of MediaTek and Qualcomm SoCs so the model can run on the device NPU.

Model description

Each .tflite file contains two signatures that together form the transcription loop:

• The audio frontend is inside the graph: the model takes a raw 16 kHz waveform in [-1, 1] — no mel-spectrogram extraction is needed.
• The window is fixed at 5 seconds. Longer audio is transcribed in consecutive 5 s windows; shorter audio is zero-padded.
• Decoding is greedy: start token 1, EOS token 2, at most 64 tokens per window. The decoder re-scores the full token buffer each step (no KV cache), so decode time grows with the number of emitted tokens.
• The tokenizer is not duplicated in this repository — load tokenizer.json from the base model repository (see the script below).

Files

How to use

1. Install dependencies

pip install ai-edge-litert numpy tokenizers huggingface_hub

2. Save the script below as transcribe.py:

#!/usr/bin/env python3
"""Transcribe a wav file with litert-community/moonshine-tiny (LiteRT)."""
import argparse
import wave

import numpy as np
from ai_edge_litert.compiled_model import CompiledModel
from huggingface_hub import hf_hub_download
from tokenizers import Tokenizer

WINDOW_SAMPLES = 80000  # 5 s at 16 kHz
MAX_TOKENS = 64
START_TOKEN = 1
EOS_TOKEN = 2


def load_wav_16k_mono(path: str) -> np.ndarray:
    """Reads a wav file as float32 mono at 16 kHz."""
    with wave.open(path, "rb") as w:
        rate, channels = w.getframerate(), w.getnchannels()
        pcm = np.frombuffer(w.readframes(w.getnframes()), dtype=np.int16)
    audio = pcm.astype(np.float32) / 32768.0
    if channels > 1:
        audio = audio.reshape(-1, channels).mean(axis=1)
    if rate != 16000:
        n = int(round(len(audio) * 16000 / rate))
        audio = np.interp(
            np.linspace(0, len(audio) - 1, n), np.arange(len(audio)), audio
        ).astype(np.float32)
    return audio


class MoonshineTiny:
    """5 s window encoder/decoder with greedy decoding."""

    def __init__(self, model_path: str, tokenizer_path: str):
        self.model = CompiledModel.from_file(model_path)
        self.tokenizer = Tokenizer.from_file(tokenizer_path)
        self.encode_idx = self.model.get_signature_index("encode")
        self.decode_idx = self.model.get_signature_index("decode")
        # Additive causal mask: 0 on and below the diagonal, -1e9 above.
        causal = np.tril(np.ones((MAX_TOKENS, MAX_TOKENS), dtype=bool))
        self.mask = np.where(causal, 0.0, -1e9).astype(np.float32)[None, None]

    def _transcribe_window(self, audio: np.ndarray) -> str:
        """Transcribes up to 5 s of 16 kHz audio."""
        buf = np.zeros((1, WINDOW_SAMPLES), dtype=np.float32)
        buf[0, : len(audio)] = audio

        enc_in = self.model.create_input_buffers(self.encode_idx)
        enc_out = self.model.create_output_buffers(self.encode_idx)
        enc_in[0].write(buf)
        self.model.run_by_index(self.encode_idx, enc_in, enc_out)
        states = enc_out[0].read((1, 207, 288), np.float32)

        tokens = np.full((1, MAX_TOKENS), EOS_TOKEN, dtype=np.int32)
        tokens[0, 0] = START_TOKEN
        dec_in = self.model.create_input_buffers(self.decode_idx)
        dec_out = self.model.create_output_buffers(self.decode_idx)
        dec_in[0].write(states)
        dec_in[2].write(self.mask)

        decoded = []
        for position in range(1, MAX_TOKENS):
            dec_in[1].write(tokens)
            self.model.run_by_index(self.decode_idx, dec_in, dec_out)
            logits = dec_out[0].read((1, MAX_TOKENS, 32768), np.float32)
            next_token = int(np.argmax(logits[0, position - 1]))
            if next_token == EOS_TOKEN:
                break
            tokens[0, position] = next_token
            decoded.append(next_token)
        return self.tokenizer.decode(decoded).strip()

    def transcribe(self, audio: np.ndarray) -> str:
        """Transcribes audio of any length in consecutive 5 s windows."""
        parts = [
            self._transcribe_window(audio[i : i + WINDOW_SAMPLES])
            for i in range(0, max(len(audio), 1), WINDOW_SAMPLES)
        ]
        return " ".join(p for p in parts if p)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--wav", required=True, help="Path to a wav file.")
    parser.add_argument(
        "--variant", default="f32", choices=["f32", "i8"], help="Model variant."
    )
    args = parser.parse_args()

    model_path = hf_hub_download(
        "litert-community/moonshine-tiny", f"moonshine_tiny_5s_{args.variant}.tflite"
    )
    tokenizer_path = hf_hub_download("UsefulSensors/moonshine-tiny", "tokenizer.json")

    asr = MoonshineTiny(model_path, tokenizer_path)
    audio = load_wav_16k_mono(args.wav)
    print(asr.transcribe(audio))


if __name__ == "__main__":
    main()

3. Run it on a 16 kHz mono wav file:

python transcribe.py --wav sample.wav

Android sample app

For an on-device Android demo that runs Moonshine (and other ASR models) with hardware acceleration, see the LiteRT speech recognition sample.

Performance

Measured on one 5 s window of continuous speech (11 output tokens), CPU inference, median of 10 runs. The macOS and Raspberry Pi rows use the Python Interpreter API as in the script above (XNNPack, 4 threads, ai-edge-litert 2.1.6); the iPhone rows use the LiteRT CompiledModel C API with the CPU accelerator at default threading:

RTF = processing time / audio duration (lower is better; below 1.0 is faster than real time). Decode dominates and scales with the number of emitted tokens, so dense speech takes proportionally longer than sparse speech. The i8 model's int8 decoder makes it about 1.6x faster than f32 on the Pi 5's Cortex-A76; on Apple silicon (M4 Max, iPhone 17 Pro) the two are equally fast. The greedy decode is deterministic across platforms: the same window produces bit-identical f32 token sequences on all three devices, the i8 model reproduces the f32 token sequence exactly on the dense test window on both Apple devices, and its transcripts are identical between the M4 Max and the Pi 5 on all 12 test clips.

Snapdragon NPU / GPU — Galaxy S26

Measured on a physical Samsung Galaxy S26 (SM-S942Q, Snapdragon 8 Elite Gen 5 / SM8850, Hexagon v81, Android 16) with LiteRT CompiledModel 2.2.0 — 5 warm-up runs then 50 timed runs, one accelerator per process, every row at device thermal status NONE, delegate placement confirmed from logcat per row. The figures are the encode signature only (the model's first signature, which the API's default run() executes — the encoder over the raw 80,000-sample / 5 s waveform input); not comparable to the end-to-end window totals in the CPU table above. NPU in HTP BURST mode, on-device (JIT) compilation.

What the table says:

• On this device the GPU running the f32 file is the only fast accelerated path: 5.4 ms for the 5 s window's encoder, encoder-only RTF 0.001.
• The Hexagon runs the i8 encoder in ~1.26 s — slower than the Pi 5's CPU in the table above. The two accelerators also refuse each other's file: f32 compiles for the NPU but fails at output-buffer creation, and i8 fails to compile on the GPU. So there is no NPU recommendation to make from this run.
• moonshine_tiny_5s_f32_Qualcomm_SM8850.tflite (the embedded precompiled context) failed to invoke on both accelerators in this harness and is not quoted.

Accuracy note

In a 12-clip spot check (LibriSpeech dev-clean samples plus two public-domain clips), the f32 model transcribes clips of up to 5 s at near-reference quality, and the i8 model matches it: the same overall word error rate on the 12-clip harness (within chunking noise), word-level divergence from the f32 transcripts of 2.5%, and encoder output that is bit-identical to f32 because the encoder is not quantized. The encoder is kept in float32 deliberately — the convolutional audio frontend on the raw waveform does not survive dynamic-range quantization (an earlier fully-quantized i8 upload degraded badly for exactly this reason) — while the decoder, which dominates latency, carries the int8 weights.

For the source model's quality, the Moonshine paper reports that Moonshine Tiny matches Whisper tiny.en word error rates across standard evaluation datasets at about 5x less compute.

License and attribution

The original Moonshine Tiny model is released by Moonshine AI under the MIT license; these converted artifacts inherit it. If you use this model, please cite:

@misc{jeffries2024moonshinespeechrecognitionlive,
  title={Moonshine: Speech Recognition for Live Transcription and Voice Commands},
  author={Nat Jeffries and Evan King and Manjunath Kudlur and Guy Nicholson and James Wang and Pete Warden},
  year={2024},
  eprint={2410.15608},
  archivePrefix={arXiv},
  primaryClass={cs.SD},
  url={https://arxiv.org/abs/2410.15608},
}