README.md

---
library_name: transformers
tags: []
---

# MERaLiON

<!-- Provide a quick summary of what the model is/does. -->

MERaLiON-AudioLLM is a Speech-Text Large Language Model tailored for Singapore’s multilingual and multicultural landscape. 

MERaLiON stands for <i>Multimodal Empathetic Reasoning and Learning in One Network</i>.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** I<sup>2</sup>R, A\*STAR
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

```python

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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[More Information Needed]

### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### 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

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [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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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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