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Model: UsefulSensors/moonshine-tiny-ko Source: Original Platform
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LICENSE.txt
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LICENSE.txt
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MOONSHINE AI COMMUNITY LICENSE AGREEMENT
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Last Updated: June 15th, 2025
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I. INTRODUCTION
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This Agreement applies to any individual person or entity ("You", "Your" or
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This Agreement is intended to allow research, non-commercial, and limited
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Agreement will be governed by and constructed in accordance with the laws of the
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United States and the State of California without regard to choice of law
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principles, and the UN Convention on Contracts for International Sale of Goods
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does not apply to this Agreement.
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|
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V. DEFINITIONS
|
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|
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"Affiliate(s)" means any entity that directly or indirectly controls, is
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controlled by, or is under common control with the subject entity; for purposes
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of this definition, "control" means direct or indirect ownership or control of
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more than 50% of the voting interests of the subject entity. "Agreement" means
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this Moonshine AI Community License Agreement. "AUP" means the Moonshine AI
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Acceptable Use Policy available at https://moonshine.ai/use-policy, as may be
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updated from time to time. "Derivative Work(s)" means (a) any derivative work of
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the Moonshine AI Materials as recognized by U.S. copyright laws and (b) any
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modifications to a Model, and any other model created which is based on or
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derived from the Model or the Model's output, including"fine tune" and "low-rank
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adaptation" models derived from a Model or a Model's output, but do not include
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the output of any Model. "Documentation" means any specifications, manuals,
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documentation, and other written information provided by Moonshine AI related to
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proprietary models and algorithms, including machine-learning models, trained
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model weights and other elements of the foregoing. "Moonshine AI" or "we" means
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Moonshine AI Ltd. and its Affiliates. "Software" means Moonshine AI's
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proprietary software made available under this Agreement now or in the future. "
|
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Moonshine AI Materials" means, collectively, Moonshine's proprietary Models,
|
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Software and Documentation (and any portion or combination thereof) made
|
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available under this Agreement. "Trade Control Laws" means any applicable U.S.
|
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and non-U.S. export control and trade sanctions laws and regulations.
|
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120
README.md
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---
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language:
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- ko
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library_name: transformers
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license: other
|
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pipeline_tag: automatic-speech-recognition
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arxiv: https://arxiv.org/abs/2509.02523
|
||||
---
|
||||
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# Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices
|
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|
||||
[[Paper]](https://huggingface.co/papers/2509.02523) [[Code]](https://github.com/moonshine-ai/moonshine) [[Installation]](https://github.com/usefulsensors/moonshine/blob/main/README.md)
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|
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This is the model card for running the automatic speech recognition (ASR) models (Moonshine models) trained and released by Moonshine AI (f.k.a Useful Sensors.) This model is part of the **Flavors of Moonshine** suite, tiny automatic speech recognition (ASR) models specialized for a range of underrepresented languages. Moonshine models are optimized for fast and accurate ASR on resource-constrained devices, outperforming comparably sized Whisper Tiny models.
|
||||
|
||||
Following [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993), we're providing some information about the automatic speech recognition model. More information on how these models were trained and evaluated can be found [in the paper](https://huggingface.co/papers/2509.02523). Note, a lot of the text has been copied verbatim from the [model card](https://github.com/openai/whisper/blob/main/model-card.md) for the Whisper model developed by OpenAI, because both models serve identical purposes, and carry identical risks.
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## Usage
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Moonshine is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub, and 🤗 Accelerate to reduce the model loading time:
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```bash
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pip install --upgrade pip
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pip install --upgrade transformers datasets[audio]
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```
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```python
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from transformers import MoonshineForConditionalGeneration, AutoProcessor
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from datasets import load_dataset, Audio
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import torch
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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model = MoonshineForConditionalGeneration.from_pretrained('UsefulSensors/moonshine-tiny-ko').to(device).to(torch_dtype)
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processor = AutoProcessor.from_pretrained('UsefulSensors/moonshine-tiny-ko')
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dataset = load_dataset('UsefulSensors/multilingual_examples', split='ko')
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dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
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sample = dataset[0]["audio"]
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inputs = processor(
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sample["array"],
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return_tensors="pt",
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sampling_rate=processor.feature_extractor.sampling_rate
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)
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inputs = inputs.to(device, torch_dtype)
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# to avoid hallucination loops, we limit the maximum length of the generated text based expected number of tokens per second
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token_limit_factor = 13 / processor.feature_extractor.sampling_rate
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seq_lens = inputs.attention_mask.sum(dim=-1)
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max_length = int((seq_lens * token_limit_factor).max().item())
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generated_ids = model.generate(**inputs, max_length=max_length)
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print(processor.decode(generated_ids[0], skip_special_tokens=True))
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```
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## Model Details
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This Moonshine model is trained for the speech recognition task, capable of transcribing Korean speech audio into Korean text. Moonshine AI developed the models to support their business direction of developing real time speech transcription products based on low cost hardware. The following table shows comparisons of common ASR evaluations sets. For more information about evaluation, please refer to the paper.
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| Size | Parameters | Fleurs (CER) ↓ | Common Voice 17 (CER) ↓ |
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|:----:|:----------:|:------------------:|:------------------:|
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| whisper tiny | 39 M | 15.83 | 37.27 |
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| whisper medium | 769 M | 6.99 | 9.38 |
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| moonshine tiny | 27 M | 8.9 | 14.94 |
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### Release date
|
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|
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September 2025
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### Model type
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Sequence-to-sequence ASR (automatic speech recognition) and speech translation model
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## Model Use
|
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|
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### Evaluated Use
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|
||||
The primary intended users of these models are AI developers that want to deploy Korean speech recognition systems in platforms that are severely constrained in memory capacity and computational resources. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not safe use.
|
||||
|
||||
The models are primarily trained and evaluated on Arabic ASR task. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.
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||||
|
||||
In particular, we caution against using Moonshine models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe Korean speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.
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|
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## Training Data
|
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|
||||
The models are trained on 72,000 hours of audio and the corresponding transcripts collected from the internet, as well as datasets openly available and accessible on HuggingFace. The open datasets used are listed in [the accompanying paper](https://huggingface.co/papers/2509.02523).
|
||||
|
||||
## Performance and Limitations
|
||||
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||||
Our evaluations show that, the models exhibit greater accuracy on standard datasets over existing ASR systems of both similar and larger sizes.
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However, like any machine learning model, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.
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In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. It is likely that this behavior and hallucinations may be worse for short audio segments, or segments where parts of words are cut off at the beginning or at the end of the segment.
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## Broader Implications
|
||||
|
||||
We anticipate that Moonshine models’ transcription capabilities may be used for improving accessibility tools, especially for real-time transcription. The real value of beneficial applications built on top of Moonshine models suggests that the disparate performance of these models may have real economic implications.
|
||||
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||||
There are also potential dual-use concerns that come with releasing Moonshine. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.
|
||||
|
||||
## Project Page
|
||||
Check out the blog post for more details: https://petewarden.com/2024/10/21/introducing-moonshine-the-new-state-of-the-art-for-speech-to-text/
|
||||
|
||||
## Citation
|
||||
If you benefit from our work, please cite us:
|
||||
|
||||
```
|
||||
@misc{king2025flavorsmoonshinetinyspecialized,
|
||||
title={Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices},
|
||||
author={Evan King and Adam Sabra and Manjunath Kudlur and James Wang and Pete Warden},
|
||||
year={2025},
|
||||
eprint={2509.02523},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CL},
|
||||
url={https://huggingface.co/papers/2509.02523},
|
||||
}
|
||||
```
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33
config.json
Normal file
33
config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"architectures": [
|
||||
"MoonshineForConditionalGeneration"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 1,
|
||||
"pad_token_id": 2,
|
||||
"decoder_hidden_act": "silu",
|
||||
"decoder_num_attention_heads": 8,
|
||||
"decoder_num_hidden_layers": 6,
|
||||
"decoder_num_key_value_heads": 8,
|
||||
"decoder_start_token_id": 1,
|
||||
"encoder_hidden_act": "gelu",
|
||||
"encoder_num_attention_heads": 8,
|
||||
"encoder_num_hidden_layers": 6,
|
||||
"encoder_num_key_value_heads": 8,
|
||||
"eos_token_id": 2,
|
||||
"hidden_size": 288,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 1152,
|
||||
"is_encoder_decoder": true,
|
||||
"max_position_embeddings": 194,
|
||||
"model_type": "moonshine",
|
||||
"partial_rotary_factor": 0.9,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 10000.0,
|
||||
"torch_dtype": "float32",
|
||||
"transformers_version": "4.48.0.dev0",
|
||||
"use_cache": true,
|
||||
"vocab_size": 32768,
|
||||
"pad_head_dim_to_multiple_of": 8
|
||||
}
|
||||
9
generation_config.json
Normal file
9
generation_config.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"pad_token_id": 2,
|
||||
"decoder_start_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"transformers_version": "4.48.0.dev0",
|
||||
"max_length": 194
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3040b2317956691c7440997b38aef4f96e4c4a8c6a3c581487c9b8363d8099c6
|
||||
size 108389160
|
||||
9
preprocessor_config.json
Normal file
9
preprocessor_config.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"do_normalize": false,
|
||||
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
|
||||
"feature_size": 1,
|
||||
"padding_side": "right",
|
||||
"padding_value": 0.0,
|
||||
"return_attention_mask": true,
|
||||
"sampling_rate": 16000
|
||||
}
|
||||
100304
tokenizer.json
Normal file
100304
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user