moonshine-streaming-medium

Usage

Moonshine Streaming is supported by the Moonshine Voice framework for edge devices and 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 hallucination loops.
token_limit_factor = 6.5 / processor.feature_extractor.sampling_rate
seq_lens = inputs.attention_mask.sum(dim=-1)
max_length = int((seq_lens * token_limit_factor).max().item())

generated_ids = model.generate(**inputs, max_length=max_length)
print(processor.decode(generated_ids[0], skip_special_tokens=True))

Note: the current Transformers code path does not yet implement fully efficient streaming for these models. It uses the flash-attention backend's sliding-window attention when available.

Model Details

Model type

Sequence-to-sequence ASR model with a streaming, sliding-window Transformer encoder and an autoregressive Transformer decoder.

Supported languages

English (trained and evaluated on English datasets).

Model sizes

Architecture summary

• Audio frontend: 50~Hz features using simple time-domain operations, CMVN, and two causal stride-2 convolutions.
• Encoder: sliding-window self-attention with no positional embeddings (ergodic encoder). Windowing uses $(16,4)$ for the first two and last two layers and $(16,0)$ for intermediate layers, giving an 80~ms lookahead in the lookahead layers.
• Adapter: adds learned positional embeddings and aligns dimensions before the decoder.
• Decoder: causal Transformer with RoPE, autoregressively generating text.

Model Use

Intended use

These models are intended for low-latency, on-device English speech transcription on memory- and compute-constrained platforms (roughly 0.1--1~TOPS and sub-1~GB memory budgets). Typical applications include live captioning, voice commands, and real-time transcription.

Out-of-scope use

These models are not intended for non-consensual surveillance, speaker identification, or high-stakes decision-making contexts. They have not been robustly evaluated for tasks outside English ASR.

Training Data

Moonshine Streaming was trained on roughly 300K hours of speech data. This includes the original Moonshine training sources (about 200K hours of public web data and open datasets) plus an additional 100K hours of internally prepared speech data. See the paper for details and dataset sources.

Performance and Limitations

Open ASR benchmark results (WER %)

Known limitations

• The decoder is autoregressive, so full-output latency grows with transcript length even when TTFT is low.
• The Transformers implementation does not yet perform fully efficient streaming; it relies on the flash-attention backend for sliding-window attention.
• Like other seq2seq ASR models, Moonshine Streaming can hallucinate words that are not present in the audio, and may repeat phrases, especially on short or noisy segments.

Broader Implications

Moonshine Streaming enables low-cost, low-l