mirror of https://github.com/huggingface/transformers.git synced 2025-07-04 21:30:07 +06:00

* toctree

* not-doctested.txt

* collapse sections

* feedback

* update

* rewrite get started sections

* fixes

* fix

* loading models

* fix

* customize models

* share

* fix link

* contribute part 1

* contribute pt 2

* fix toctree

* tokenization pt 1

* Add new model (#32615)

* v1 - working version

* fix

* fix

* fix

* fix

* rename to correct name

* fix title

* fixup

* rename files

* fix

* add copied from on tests

* rename to `FalconMamba` everywhere and fix bugs

* fix quantization + accelerate

* fix copies

* add `torch.compile` support

* fix tests

* fix tests and add slow tests

* copies on config

* merge the latest changes

* fix tests

* add few lines about instruct

* Apply suggestions from code review

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fix

* fix tests

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* "to be not" -> "not to be" (#32636)

* "to be not" -> "not to be"

* Update sam.md

* Update trainer.py

* Update modeling_utils.py

* Update test_modeling_utils.py

* Update test_modeling_utils.py

* fix hfoption tag

* tokenization pt. 2

* image processor

* fix toctree

* backbones

* feature extractor

* fix file name

* processor

* update not-doctested

* update

* make style

* fix toctree

* revision

* make fixup

* fix toctree

* fix

* make style

* fix hfoption tag

* pipeline

* pipeline gradio

* pipeline web server

* add pipeline

* fix toctree

* not-doctested

* prompting

* llm optims

* fix toctree

* fixes

* cache

* text generation

* fix

* chat pipeline

* chat stuff

* xla

* torch.compile

* cpu inference

* toctree

* gpu inference

* agents and tools

* gguf/tiktoken

* finetune

* toctree

* trainer

* trainer pt 2

* optims

* optimizers

* accelerate

* parallelism

* fsdp

* update

* distributed cpu

* hardware training

* gpu training

* gpu training 2

* peft

* distrib debug

* deepspeed 1

* deepspeed 2

* chat toctree

* quant pt 1

* quant pt 2

* fix toctree

* fix

* fix

* quant pt 3

* quant pt 4

* serialization

* torchscript

* scripts

* tpu

* review

* model addition timeline

* modular

* more reviews

* reviews

* fix toctree

* reviews reviews

* continue reviews

* more reviews

* modular transformers

* more review

* zamba2

* fix

* all frameworks

* pytorch

* supported model frameworks

* flashattention

* rm check_table

* not-doctested.txt

* rm check_support_list.py

* feedback

* updates/feedback

* review

* feedback

* fix

* update

* feedback

* updates

* update

---------

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>

2025-03-03 10:33:46 -08:00

3.0 KiB

Raw Blame History

Moonshine

Overview

The Moonshine model was proposed in Moonshine: Speech Recognition for Live Transcription and Voice Commands by Nat Jeffries, Evan King, Manjunath Kudlur, Guy Nicholson, James Wang, Pete Warden.

The abstract from the paper is the following:

This paper introduces Moonshine, a family of speech recognition models optimized for live transcription and voice command processing. Moonshine is based on an encoder-decoder transformer architecture and employs Rotary Position Embedding (RoPE) instead of traditional absolute position embeddings. The model is trained on speech segments of various lengths, but without using zero-padding, leading to greater efficiency for the encoder during inference time. When benchmarked against OpenAI's Whisper tiny-en, Moonshine Tiny demonstrates a 5x reduction in compute requirements for transcribing a 10-second speech segment while incurring no increase in word error rates across standard evaluation datasets. These results highlight Moonshine's potential for real-time and resource-constrained applications.

Tips:

Moonshine improves upon Whisper's architecture:
1. It uses SwiGLU activation instead of GELU in the decoder layers
2. Most importantly, it replaces absolute position embeddings with Rotary Position Embeddings (RoPE). This allows Moonshine to handle audio inputs of any length, unlike Whisper which is restricted to fixed 30-second windows.

This model was contributed by Eustache Le Bihan (eustlb). The original code can be found here.

Resources

Automatic speech recognition task guide

MoonshineConfig

autodoc MoonshineConfig

MoonshineModel

autodoc MoonshineModel - forward - _mask_input_features

MoonshineForConditionalGeneration

autodoc MoonshineForConditionalGeneration - forward - generate

3.0 KiB Raw Blame History