metadata

library_name: transformers
tags: []

MERaLiON

MERaLiON-AudioLLM is a Speech-Text Large Language Model tailored for Singapore’s multilingual and multicultural landscape. Integrating a localised Whisper-Large-V3 and SEA-LIONv3, MERaLiON-AudioLLM is finetuned on 260,000 hours of speech and audio data, 8 various tasks, to address the diverse linguistic nuances of Singapore local accents and dialects.

MERaLiON stands for Multimodal Empathetic Reasoning and Learning in One Network.

Developed by: I²R, A*STAR
Funded by [optional]: Singapore NRF
Model type: MultiModal LLM
Language(s) (Speech): English (general & Singapore)
Language(s) (NLP): English, Chinese, Vietnamese, Indonesian, Thai, Filipino, Tamil, Malay, Khmer, Lao, Burmese, Javanese, Sundanese
License: MIT

For more details, please refer to our report.

Model Details

Model Description

Uses

Direct Use


from datasets import load_dataset
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor

repo_id = "MERaLiON/AudioLLM"

processor = AutoProcessor.from_pretrained(
    repo_id, 
    trust_remote_code=True,
    )
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    repo_id,
    use_safetensors=True,
    trust_remote_code=True,
)

prompt = "Can you please turn this audio into text format?"
conversation = [
    {
        "role": "user",
        "content": f"Given the following audio context: <SpeechHere>\n\nText instruction: {prompt}"
    }
]
chat_prompt = processor.tokenizer.apply_chat_template(
    conversation=conversation,
    tokenize=False,
    add_generation_prompt=True
)

libri_data = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
audio_array = libri_data[0]["audio"]["array"]

inputs = processor(text=chat_prompt, audios=audio_array, time_duration_limit=20)

outputs = model.generate(**inputs, max_new_tokens=128)

print(processor.decode(outputs[0, inputs['input_ids'].size(1):], skip_special_tokens=True))

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Hours used: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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