Steveeeeeeen
HF Staff
Update library tag for better download tracking and code snippets!
5d317d3
verified
| license: other | |
| license_name: seallms | |
| license_link: LICENSE | |
| language: | |
| - en | |
| - zh | |
| - id | |
| - vi | |
| - th | |
| pipeline_tag: audio-text-to-text | |
| tags: | |
| - seallms-audio | |
| - speech | |
| - audio | |
| - SEA | |
| library_name: transformers | |
| <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 | |
| <p align="center"> | |
| <a href="https://DAMO-NLP-SG.github.io/SeaLLMs-Audio/" target="_blank" rel="noopener">Website</a> | |
| | |
| <a href="https://huggingface.co/spaces/SeaLLMs/SeaLLMs-Audio-Demo" target="_blank" rel="noopener"> 🤗 DEMO</a> | |
| | |
| <a href="https://github.com/DAMO-NLP-SG/SeaLLMs-Audio" target="_blank" rel="noopener">Github</a> | |
| | |
| <a href="https://huggingface.co/SeaLLMs/SeaLLMs-Audio-7B" target="_blank" rel="noopener">🤗 Model</a> | |
| | |
| <!-- <a href="https://arxiv.org/pdf/2407.19672" target="_blank" rel="noopener">[NEW] Technical Report</a> --> | |
| </p> | |
| 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)**. | |
| 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. | |
| ### 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** | |
|  | |
| </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) | |
| ``` | |
| ## 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 ([wxzhang@sutd.edu.sg](mailto:wxzhang@sutd.edu.sg)) | |
| ``` | |
| @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}}, | |
| } | |
| ``` | |