Update Readme file

README.md

@@ -1,3 +1,134 @@
 ---
 license: cc-by-nc-4.0
+language:
+- en
+library_name: transformers
+tags:
+- music
+- art
 ---
+
+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- music
+- art
+---
+# Model Card for Model ID
+## Model Details
+### Model Description
+The model consists of a music encoder ```MERT-v1-300M```, a natural language decoder ```vicuna-7b-delta-v0```, and a linear projection laer between the two.
+
+This checkpoint of MusiLingo is developed on the MusicQA and can answer instructions with music raw audio, such as querying about the tempo, emotion, genre, tags or subjective feelings etc.
+You can use the MusicQA dataset for the following demo. For the implementation of MusicQA, please refer to our [Github repo](https://github.com/zihaod/MusiLingo/blob/main/musilingo/datasets/datasets/musicqa_dataset.py).
+
+
+### Model Sources [optional]
+- **Repository:** [GitHub repo](https://github.com/zihaod/MusiLingo)
+- **Paper [optional]:** __[MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response](https://arxiv.org/abs/2309.08730)__
+<!-- - **Demo [optional]:** [More Information Needed] -->
+
+
+
+## Getting Start
+```
+from tqdm.auto import tqdm
+
+import torch
+from torch.utils.data import DataLoader
+from transformers import Wav2Vec2FeatureExtractor
+from transformers import StoppingCriteria, StoppingCriteriaList
+
+
+
+class StoppingCriteriaSub(StoppingCriteria):
+    def __init__(self, stops=[], encounters=1):
+        super().__init__()
+        self.stops = stops
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
+        for stop in self.stops:
+            if torch.all((stop == input_ids[0][-len(stop):])).item():
+                return True
+        return False
+
+def answer(self, samples, stopping, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.5,
+        repetition_penalty=1.0, length_penalty=1, temperature=0.1, max_length=2000):
+    audio = samples["audio"].cuda()
+    audio_embeds, atts_audio = self.encode_audio(audio)
+    if 'instruction_input' in samples:  # instruction dataset
+        #print('Instruction Batch')
+        instruction_prompt = []
+        for instruction in samples['instruction_input']:
+            prompt = '<Audio><AudioHere></Audio> ' + instruction
+            instruction_prompt.append(self.prompt_template.format(prompt))
+        audio_embeds, atts_audio = self.instruction_prompt_wrap(audio_embeds, atts_audio, instruction_prompt)
+    self.llama_tokenizer.padding_side = "right"
+    batch_size = audio_embeds.shape[0]
+    bos = torch.ones([batch_size, 1],
+                    dtype=torch.long,
+                    device=torch.device('cuda')) * self.llama_tokenizer.bos_token_id
+    bos_embeds = self.llama_model.model.embed_tokens(bos)
+    atts_bos = atts_audio[:, :1]
+    inputs_embeds = torch.cat([bos_embeds, audio_embeds], dim=1)
+    attention_mask = torch.cat([atts_bos, atts_audio], dim=1)
+    outputs = self.llama_model.generate(
+        inputs_embeds=inputs_embeds,
+        max_new_tokens=max_new_tokens,
+        stopping_criteria=stopping,
+        num_beams=num_beams,
+        do_sample=True,
+        min_length=min_length,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        length_penalty=length_penalty,
+        temperature=temperature,
+    )
+    output_token = outputs[0]
+    if output_token[0] == 0:  # the model might output a unknow token <unk> at the beginning. remove it
+        output_token = output_token[1:]
+    if output_token[0] == 1:  # if there is a start token <s> at the beginning. remove it
+        output_token = output_token[1:]
+    output_text = self.llama_tokenizer.decode(output_token, add_special_tokens=False)
+    output_text = output_text.split('###')[0]  # remove the stop sign '###'
+    output_text = output_text.split('Assistant:')[-1].strip()
+    return output_text
+
+processor = Wav2Vec2FeatureExtractor.from_pretrained("m-a-p/MERT-v1-330M",trust_remote_code=True)
+ds = MusicQADataset(processor, f'{path}/data/music_data', 'Eval')
+dl = DataLoader(
+                ds,
+                batch_size=1,
+                num_workers=0,
+                pin_memory=True,
+                shuffle=False,
+                drop_last=True,
+                collate_fn=ds.collater
+                )
+
+stopping = StoppingCriteriaList([StoppingCriteriaSub([torch.tensor([835]).cuda(),
+                                torch.tensor([2277, 29937]).cuda()])])
+
+from transformers import AutoModel
+model_musicqa = AutoModel.from_pretrained("m-a-p/MusiLingo-musicqa-v1")
+
+for idx, sample in tqdm(enumerate(dl)):
+    ans = answer(Musilingo_musicqa.model, sample, stopping, length_penalty=100, temperature=0.1)
+    txt = sample['text_input'][0]
+    print(txt)
+    print(and)
+```
+
+# Citing This Work
+
+If you find the work useful for your research, please consider citing it using the following BibTeX entry:
+```
+@inproceedings{deng2024musilingo,
+  title={MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response},
+  author={Deng, Zihao and Ma, Yinghao and Liu, Yudong and Guo, Rongchen and Zhang, Ge and Chen, Wenhu and Huang, Wenhao and Benetos, Emmanouil},
+  booktitle={Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024)},
+  year={2024},
+  organization={Association for Computational Linguistics}
+}
+```