Update model card for Maori

5c91565 verified 2 months ago

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pipeline_tag: automatic-speech-recognition
language: mri
license: apache-2.0
tags:
  - trimmed
library_name: transformers
base_model: openai/whisper-large-v3-turbo
base_model_relation: quantized
datasets:
  - lbourdois/fineweb-2-trimming

whisper-large-v3-turbo-mri-32768

This model is a 3.02% whisper-large-v3-turbo version of openai/whisper-large-v3-turbo optimized for Maori language via vocabulary size reduction using the trimming method.
This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.

Model Statistics

Metric	Original	Trimmed	Reduction
Vocabulary size	51,865 tokens	32,768 tokens	36.82%
Model size	808,878,080 params	784,432,640 params	3.02%

Mining Dataset Statistics

Number of texts used for mining: 158,804 texts
Dataset: lbourdois/fineweb-2-trimming

Usage

from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import librosa

# Pipeline function
processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-large-v3-turbo-mri-32768")
pipe = pipeline(
    "automatic-speech-recognition",
    model="alphaedge-ai/whisper-large-v3-turbo-mri-32768",
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    generate_kwargs={"language": "maori", "task": "transcribe"},
)

# Loading and resampling at 16 kHz (required by Whisper)
audio_array, sampling_rate = librosa.load(audio_path, sr=16000)

# Result
result = pipe(audio_array)
print("Transcription :", result["text"])

Citations

Whisper

@misc{radford2022whisper,
  doi = {10.48550/ARXIV.2212.04356},
  url = {https://arxiv.org/abs/2212.04356},
  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

Trimming blog post

@misc{hf_blogpost_trimming,
      title={Introduction to Trimming}, 
      author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
      year={2026},
      url={https://huggingface.co/blog/lbourdois/introduction-to-trimming}, 
}