Update README.md

README.md

@@ -14,17 +14,228 @@ tags:
 - chat,
 - audio
 ---
+<p align="center">
+    <img src="https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/images/seallm-audio-logo.png" alt="SeaLLMs-Audio" width="20%">
+</p>
 
-# SeaLLMs-Audio: Large Audio Language Models for Southeast Asia
+# SeaLLMs-Audio: Large Audio-Language Models for Southeast Asia
 
+<p align="center">
+<a href="https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/" target="_blank" rel="noopener">Website</a>
+&nbsp;&nbsp;
+<a href="https://huggingface.co/spaces/SeaLLMs/SeaLLMs-Audio-Demo" target="_blank" rel="noopener"> 🤗 DEMO</a>
+&nbsp;&nbsp;
+<a href="https://github.com/DAMO-NLP-SG/SeaLLMs-Audio" target="_blank" rel="noopener">Github</a>
+&nbsp;&nbsp;
+<a href="https://huggingface.co/SeaLLMs/SeaLLMs-Audio-7B" target="_blank" rel="noopener">🤗 Model</a>
+&nbsp;&nbsp;
+<!-- <a href="https://arxiv.org/pdf/2407.19672" target="_blank" rel="noopener">[NEW] Technical Report</a> -->
+</p>
 
-## Introduction
+We introduce **SeaLLMs-Audio**, the multimodal (audio) extension of the [SeaLLMs](https://damo-nlp-sg.github.io/DAMO-SeaLLMs/) (Large Language Models for Southeast Asian languages) family. It is the first large audio-language model (LALM) designed to support multiple Southeast Asian languages, including **Indonesian (id), Thai (th), and Vietnamese (vi), alongside English (en) and Chinese (zh)**. 
 
-## Quickstart
+Trained on a large-scale audio dataset, SeaLLMs-Audio demonstrates strong performance across various audio-related tasks, such as audio analysis tasks and voice-based interactions. As a significant step toward advancing audio LLMs in Southeast Asia, we hope SeaLLMs-Audio will benefit both the research community and industry in the region.
 
-### Voice Chat Inference
+### Key Features of SeaLLMs-Audio:
+
+- **Multilingual**: The model mainly supports 5 languages, including 🇮🇩 Indonesian, 🇹🇭 Thai, 🇻🇳 Vietnamese, 🇬🇧 English, and 🇨🇳 Chinese.
+- **Multimodal**: The model supports flexible input formats, such as **audio only, text only, and audio with text**.
+- **Multi-task**: The model supports a variety of tasks, including audio analysis tasks such as audio captioning, automatic speech recognition, speech-to-text translation, speech emotion recognition, speech question answering, and speech summarization. Additionally, it handles voice chat tasks, including answering factual, mathematical, and other general questions.
+
+We open-weight [SeaLLMs-Audio](https://huggingface.co/SeaLLMs/SeaLLMs-Audio-7B) on Hugging Face, and we have built a [demo](https://huggingface.co/spaces/SeaLLMs/SeaLLMs-Audio-Demo) for users to interact with.
+
+
+# Training information: 
+SeaLLMs-Audio builts upon [Qwen2-Audio-7B](https://huggingface.co/Qwen/Qwen2-Audio-7B) and [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). We replaced the LLM module in Qwen2-Audio-7B by Qwen2.5-7B-Instruct. After that, we do full-parameter fine-tuning on a large-scale audio dataset. This dataset contains 1.58M conversations for multiple tasks, in which 93% are single turn. The tasks can be roughly classified as following task categories:  automatic speech recognition (ASR), audio captioning (AC), speech-to-text translation (S2TT), question answering (QA),  speech summarization (SS), speech question answering (SQA), chat, math, and fact and mixed tasks (mixed).
+
+The distribution of data across languages and tasks are:
+
+<p align="center">
+    <strong>Distribution of SeaLLMs-Audio training data across languages and tasks</strong>
+</p>
+
+<p align="center">
+    <img src="https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/data_distribution/dist_lang.png" alt="Distribution of SeaLLMs-Audio training data across languages" width="70%">
+    <img src="https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/data_distribution/dist_task.png" alt="Distribution of SeaLLMs-Audio training data across tasks" width="70%">
+</p>
+
+The training dataset was curated from multiple data sources, including public datasets and in-house data. Public datasets includes: [gigaspeech](https://huggingface.co/datasets/speechcolab/gigaspeech), [gigaspeech2](https://huggingface.co/datasets/speechcolab/gigaspeech2), [common voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0), [AudioCaps](https://huggingface.co/datasets/OpenSound/AudioCaps), [VoiceAssistant-400K](https://huggingface.co/datasets/gpt-omni/VoiceAssistant-400K), [YODAS2](https://huggingface.co/datasets/espnet/yodas2), and [Multitask-National-Speech-Corpus](https://huggingface.co/datasets/MERaLiON/Multitask-National-Speech-Corpus-v1). We would like to thank the authors of these datasets for their contributions to the community!
+
+We train the model on the dataset for 1 epoch, which took ~6 days to complete on 32 A800 GPUs.
+
+
+# Performance
+Due to the absence of standard audio benchmarks for evaluating audio LLMs in Southeast Asia, we have manually created a benchmark called **SeaBench-Audio**. It comprises nine tasks:
+
+- **Tasks with both audio and text inputs:** Audio Captioning (AC), Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Recognition (SER), Speech Question Answering (SQA), and Speech Summarization (SS).
+- **Tasks with only audio inputs:** Factuality, Math, and General.
+
+We manually annotated 15 questions per task per language. For evaluation, qualified native speakers rated each response on a scale of 1 to 5, with 5 representing the highest quality.
+
+Due to the lack of LALMs for all the three Southeast Asian languages, we compare the performance of SeaLLMs-Audio with relevant LALMs with similar sizes, including: [Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct) (Qwen2-Audio), [MERaLiON-AudioLLM-Whisper-SEA-LION](https://huggingface.co/MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION) (MERaLiON), [llama3.1-typhoon2-audio-8b-instruct](https://huggingface.co/scb10x/llama3.1-typhoon2-audio-8b-instruct) (typhoon2-audio), and [DiVA-llama-3-v0-8b](https://huggingface.co/WillHeld/DiVA-llama-3-v0-8b) (DiVA).
+All the LALMs can accept audio with text as input. The results are shown in the figure below.
+
+<center>
+
+**Average scores of SeaLLMs-Audio vs. Other LALMs on SeaBench-Audio**
+
+![Performance of SeaLLMs-Audio vs. Other Audio LLMs](https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/images/scores_lang.png)
+
+</center>
+
+The results shows that SeaLLMs-Audio achieve state-of-the-art performance in all the five langauges, demonstrating its effectiveness in supporting audio-related tasks in Southeast Asia.
+
+
+# Quickstart
+Our model is available on Hugging Face, and you can easily use it with the `transformers` library or `vllm` library. Below are some examples to get you started.
+
+## Get started with `transformers`
+
+```python
+from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
+import librosa
+import os
+
+model = Qwen2AudioForConditionalGeneration.from_pretrained("SeaLLMs/SeaLLMs-Audio-7B", device_map="auto")
+processor = AutoProcessor.from_pretrained("SeaLLMs/SeaLLMs-Audio-7B")
+
+def response_to_audio(conversation, model=None, processor=None):
+    text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+    audios = []
+    for message in conversation:
+        if isinstance(message["content"], list):
+            for ele in message["content"]:
+                if ele["type"] == "audio":
+                    if ele['audio_url'] != None:
+                        audios.append(librosa.load(
+                            ele['audio_url'], 
+                            sr=processor.feature_extractor.sampling_rate)[0]
+                        )
+    if audios != []:
+        inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True,sampling_rate=16000)
+    else: 
+        inputs = processor(text=text, return_tensors="pt", padding=True)
+    inputs.input_ids = inputs.input_ids.to("cuda")
+    inputs = {k: v.to("cuda") for k, v in inputs.items() if v is not None}
+    generate_ids = model.generate(**inputs, max_new_tokens=2048, temperature = 0, do_sample=False)
+    generate_ids = generate_ids[:, inputs["input_ids"].size(1):]
+    response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    return response
+
+# Voice Chat
+os.system(f"wget -O fact_en.wav https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/audios/fact_en.wav")
+os.system(f"wget -O general_en.wav https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/audios/general_en.wav")
+conversation = [
+    {"role": "user", "content": [
+        {"type": "audio", "audio_url": "fact_en.wav"},
+    ]},
+    {"role": "assistant", "content": "The most abundant gas in Earth's atmosphere is nitrogen. It makes up about 78 percent of the atmosphere by volume."},
+    {"role": "user", "content": [
+        {"type": "audio", "audio_url": "general_en.wav"},
+    ]},
+]
+
+response = response_to_audio(conversation, model=model, processor=processor)
+print(response)
+
+# Audio Analysis
+os.system(f"wget -O ASR_en.wav https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/audios/ASR_en.wav")
+conversation = [
+    {"role": "user", "content": [
+        {"type": "audio", "audio_url": "ASR_en.wav"},
+        {"type": "text", "text": "Please write down what is spoken in the audio file."},
+    ]},
+]
+
+response = response_to_audio(conversation, model=model, processor=processor)
+print(response)
+```
+
+## Inference with `vllm`
+
+```python
+from vllm import LLM, SamplingParams
+import librosa, os
+from transformers import AutoProcessor
+
+processor = AutoProcessor.from_pretrained("SeaLLMs/SeaLLMs-Audio-7B")
+llm = LLM(
+    model="SeaLLMs/SeaLLMs-Audio-7B", trust_remote_code=True, gpu_memory_utilization=0.5,  
+    enforce_eager=True,  device = "cuda",
+    limit_mm_per_prompt={"audio": 5},
+)
+
+def response_to_audio(conversation, model=None, processor=None, temperature = 0.1,repetition_penalty=1.1, top_p = 0.9,max_new_tokens = 4096):
+    text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+    audios = []
+    for message in conversation:
+        if isinstance(message["content"], list):
+            for ele in message["content"]:
+                if ele["type"] == "audio":
+                    if ele['audio_url'] != None:
+                        audios.append(librosa.load(
+                            ele['audio_url'], 
+                            sr=processor.feature_extractor.sampling_rate)[0]
+                        )
+
+    sampling_params = SamplingParams(
+        temperature=temperature, max_tokens=max_new_tokens, repetition_penalty=repetition_penalty, top_p=top_p, top_k=20,
+        stop_token_ids=[],
+    )
+
+    input = {
+            'prompt': text,
+            'multi_modal_data': {
+                'audio': [(audio, 16000) for audio in audios]
+            }
+            }
+
+    output = model.generate([input], sampling_params=sampling_params)[0]
+    response = output.outputs[0].text
+    return response
+
+# Voice Chat
+os.system(f"wget -O fact_en.wav https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/audios/fact_en.wav")
+os.system(f"wget -O general_en.wav https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/audios/general_en.wav")
+conversation = [
+    {"role": "user", "content": [
+        {"type": "audio", "audio_url": "fact_en.wav"},
+    ]},
+    {"role": "assistant", "content": "The most abundant gas in Earth's atmosphere is nitrogen. It makes up about 78 percent of the atmosphere by volume."},
+    {"role": "user", "content": [
+        {"type": "audio", "audio_url": "general_en.wav"},
+    ]},
+]
+
+response = response_to_audio(conversation, model=llm, processor=processor)
+print(response)
+
+# Audio Analysis
+os.system(f"wget -O ASR_en.wav https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/static/audios/ASR_en.wav")
+conversation = [
+    {"role": "user", "content": [
+        {"type": "audio", "audio_url": "ASR_en.wav"},
+        {"type": "text", "text": "Please write down what is spoken in the audio file."},
+    ]},
+]
+
+response = response_to_audio(conversation, model=llm, processor=processor)
+print(response)
+```
 
-### Audio Analysis Inference
 
 ## Citation
+If you find our project useful, we hope you would kindly star our [repo](https://github.com/DAMO-NLP-SG/SeaLLMs-Audio) and cite our work as follows.
+Corresponding Author: Wenxuan Zhang ([[email protected]](mailto:[email protected]))
+```
+@misc{SeaLLMs-Audio,
+    author = {Chaoqun Liu and Mahani Aljunied and Guizhen Chen and Hou Pong Chan and Weiwen Xu and Yu Rong and Wenxuan Zhang},
+    title = {SeaLLMs-Audio: Large Audio-Language Models for Southeast Asia},
+    year = {2025},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+    howpublished = {\url{https://github.com/DAMO-NLP-SG/SeaLLMs-Audio}},
+}
+```
+