Upload README.md

README.md

@@ -1,13 +1,121 @@
 ---
+language:
+- zh
+- en
 tags:
-- music
-- text-generation-inference
+- qwen
+pipeline_tag: text-generation
+inference: false
 ---
-# Environment:
-## step 1:
-conda create -name SpectPrompt python=3.9
-## step 2:
-pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118  
-pip install transformers datasets librosa einops_exts einops mmcls peft ipdb torchlibrosa  
-pip install -U openmim  
-mim install mmcv==1.7.1   
+
+# Qwen-Audio
+
+<br>
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/audio_logo.jpg" width="400"/>
+<p>
+<br>
+
+<p align="center">
+        Qwen-Audio <a href="https://www.modelscope.cn/models/qwen/QWen-Audio/summary">🤖 <a> | <a href="https://huggingface.co/Qwen/Qwen-Audio">🤗</a>&nbsp ｜ Qwen-Audio-Chat <a href="https://www.modelscope.cn/models/qwen/QWen-Audio-Chat/summary">🤖 <a>| <a href="https://huggingface.co/Qwen/Qwen-Audio-Chat">🤗</a>&nbsp | &nbsp&nbsp Demo<a href="https://modelscope.cn/studios/qwen/Qwen-Audio-Chat-Demo/summary"> 🤖</a> | <a href="https://huggingface.co/spaces/Qwen/Qwen-Audio">🤗</a>&nbsp
+<br>
+&nbsp&nbsp<a href="https://qwen-audio.github.io/Qwen-Audio/">Homepage</a>&nbsp ｜ &nbsp<a href="http://arxiv.org/abs/2311.07919">Paper</a> | &nbsp<a href="https://huggingface.co/papers/2311.07919">🤗</a> 
+</p>
+<br><br>
+
+**Qwen-Audio** (Qwen Large Audio Language Model) is the multimodal version of the large model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-Audio accepts diverse audio (human speech, natural sound, music and song) and text as inputs, outputs text. The contribution of Qwen-Audio include:
+
+- **Fundamental audio models**: Qwen-Audio is a fundamental multi-task audio-language model that supports various tasks, languages, and audio types, serving as a universal audio understanding model. Building upon Qwen-Audio, we develop Qwen-Audio-Chat through instruction fine-tuning, enabling multi-turn dialogues and supporting diverse audio-oriented scenarios.
+- **Multi-task learning framework for all types of audios**: To scale up audio-language pre-training, we address the challenge of variation in textual labels associated with different datasets by proposing a multi-task training framework, enabling knowledge sharing and avoiding one-to-many interference. Our model incorporates more than 30 tasks and extensive experiments show the model achieves strong performance.
+- **Strong Performance**: Experimental results show that Qwen-Audio achieves impressive performance across diverse benchmark tasks without requiring any task-specific fine-tuning, surpassing its counterparts. Specifically, Qwen-Audio achieves state-of-the-art results on the test set of Aishell1, cochlscene, ClothoAQA, and VocalSound.
+- **Flexible multi-run chat from audio and text input**: Qwen-Audio supports multiple-audio analysis, sound understading and reasoning, music appreciation, and tool usage for speech editing.
+
+**Qwen-Audio** 是阿里云研发的大规模音频语言模型（Large Audio Language Model）。Qwen-Audio 可以以多种音频 (包括说话人语音、自然音、音乐、歌声）和文本作为输入，并以文本作为输出。Qwen-Audio 系列模型的特点包括：
+
+- **音频基石模型**：Qwen-Audio是一个性能卓越的通用的音频理解模型，支持各种任务、语言和音频类型。在Qwen-Audio的基础上，我们通过指令微调开发了Qwen-Audio-Chat，支持多轮、多语言、多语言对话。Qwen-Audio和Qwen-Audio-Chat模型均已开源。
+- **兼容多种复杂音频的多任务学习框架**：为了避免由于数据收集来源不同以及任务类型不同，带来的音频到文本的一对多的干扰问题，我们提出了一种多任务训练框架，实现相似任务的知识共享，并尽可能减少不同任务之间的干扰。通过提出的框架，Qwen-Audio可以容纳训练超过30多种不同的音频任务；
+- **出色的性能**：Qwen-Audio在不需要任何任务特定的微调的情况下，在各种基准任务上取得了领先的结果。具体得，Qwen-Audio在Aishell1、cochlscene、ClothoAQA和VocalSound的测试集上都达到了SOTA；
+- **支持多轮音频和文本对话，支持各种语音场景**：Qwen-Audio-Chat支持声音理解和推理、音乐欣赏、多音频分析、多轮音频-文本交错对话以及外部语音工具的使用(如语音编辑)。
+
+
+We release Qwen-Audio and Qwen-Audio-Chat, which are pretrained model and Chat model respectively. For more details about Qwen-Audio, please refer to our [Github Repo](https://github.com/QwenLM/Qwen-Audio/tree/main). This repo is the one for Qwen-Audio.
+<br>
+
+目前，我们提供了Qwen-Audio和Qwen-Audio-Chat两个模型，分别为预训练模型和Chat模型。如果想了解更多关于信息，请点击[链接](https://github.com/QwenLM/Qwen-Audio/tree/main)查看Github仓库。本仓库为Qwen-Audio仓库。
+
+
+## Requirements
+* python 3.8 and above
+* pytorch 1.12 and above, 2.0 and above are recommended
+* CUDA 11.4 and above are recommended (this is for GPU users)
+* FFmpeg
+  <br>
+
+## Quickstart
+Below, we provide simple examples to show how to use Qwen-Audio with 🤗 Transformers.
+
+Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.
+
+```bash
+pip install -r requirements.txt
+```
+For more details, please refer to [tutorial](https://github.com/QwenLM/Qwen-Audio).
+
+#### 🤗 Transformers
+
+To use Qwen-Audio for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, **please make sure that you are using the latest code.**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import torch
+torch.manual_seed(1234)
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-Audio", trust_remote_code=True)
+
+# use bf16
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="auto", trust_remote_code=True, bf16=True).eval()
+# use fp16
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="auto", trust_remote_code=True, fp16=True).eval()
+# use cpu only
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="cpu", trust_remote_code=True).eval()
+# use cuda device
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="cuda", trust_remote_code=True).eval()
+
+# Specify hyperparameters for generation (No need to do this if you are using transformers>4.32.0)
+# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-Audio", trust_remote_code=True)
+audio_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/1272-128104-0000.flac"
+sp_prompt = "<|startoftranscription|><|en|><|transcribe|><|en|><|notimestamps|><|wo_itn|>"
+query = f"<audio>{audio_url}</audio>{sp_prompt}"
+audio_info = tokenizer.process_audio(query)
+inputs = tokenizer(query, return_tensors='pt', audio_info=audio_info)
+inputs = inputs.to(model.device)
+pred = model.generate(**inputs, audio_info=audio_info)
+response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False,audio_info=audio_info)
+print(response)
+# <audio>https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/1272-128104-0000.flac</audio><|startoftranscription|><|en|><|transcribe|><|en|><|notimestamps|><|wo_itn|>mister quilting is the apostle of the middle classes and we are glad to welcome his gospel<|endoftext|>
+```
+
+
+## License Agreement
+Researchers and developers are free to use the codes and model weights of Qwen-Audio. We also allow its commercial use. Check our license at [LICENSE](https://github.com/QwenLM/Qwen-Audio/blob/main/LICENSE.txt) for more details.
+<br>
+
+## Citation
+If you find our paper and code useful in your research, please consider giving a star and citation
+
+```BibTeX
+@article{Qwen-Audio,
+  title={Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models},
+  author={Chu, Yunfei and Xu, Jin and Zhou, Xiaohuan and Yang, Qian and Zhang, Shiliang and Yan, Zhijie  and Zhou, Chang and Zhou, Jingren},
+  journal={arXiv preprint arXiv:2311.07919},
+  year={2023}
+}
+```
+<br>
+
+## Contact Us
+
+If you are interested to leave a message to either our research team or product team, feel free to send an email to [email protected].
+