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Moonshine Streaming

[Paper]

This is the model card for the Moonshine Streaming automatic speech recognition (ASR) models trained and released by Useful Sensors. Moonshine Streaming pairs a lightweight 50~Hz audio frontend with a sliding-window Transformer encoder to deliver low-latency streaming ASR on edge-class hardware. The encoder uses bounded local attention and no positional embeddings (an "ergodic" encoder), while an adapter injects positional information before a standard autoregressive decoder.

This model card follows the recommendations from Model Cards for Model Reporting (Mitchell et al.). See the paper draft in this repository for full details.

Usage

Moonshine Streaming is supported in Hugging Face Transformers. The following example matches the standard seq2seq ASR API and uses the streaming model checkpoint:

pip install --upgrade pip
pip install --upgrade git+https://github.com/huggingface/transformers.git#egg=transformers datasets[audio]

from transformers import MoonshineStreamingForConditionalGeneration, AutoProcessor
from datasets import load_dataset, Audio
import torch

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model = MoonshineStreamingForConditionalGeneration.from_pretrained(
    "usefulsensors/moonshine-streaming-small"
).to(device).to(torch_dtype)
processor = AutoProcessor.from_pretrained("usefulsensors/moonshine-streaming-small")

dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
sample = dataset[0]["audio"]

inputs = processor(
    sample["array"],
    return_tensors="pt",
    sampling_rate=processor.feature_extractor.sampling_rate,
)
inputs = inputs.to(device, torch_dtype)

# Limit max output length to avoid halluci

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