BAT update: fix type, upload checkpoint; finish inference code; add d…

…emo code for inference

examples/seld_spatialsoundqa/README.md

@@ -1,39 +1,65 @@
 # <img src="assets/bat.png" alt="SELD_SpatialSoundQA" width="25" height="25"> SELD_SpatialSoundQA
 
-This repo hosts the code and models of "[BAT: Learning to Reason about Spatial Sounds with Large Language Models](https://arxiv.org/abs/2402.01591)" [ICML 2024 [bib](https://github.com/zszheng147/Spatial-AST#citation)]. 
+This repo hosts the code and models of "[BAT: Learning to Reason about Spatial Sounds with Large Language Models](https://arxiv.org/abs/2402.01591)" [ICML 2024 [bib](https://github.com/X-LANCE/SLAM-LLM/tree/main/examples/seld_spatialsoundqa#citation)]. 
 
 Checkout our [demo page](https://zhishengzheng.com/BAT/) and enjoy a QA game with spatial audio.
 
-## Performance and checkpoints
-Encoder | Projector | PEFT | LLM
-|---|---|---|---|
-[Spatial-AST](https://huggingface.co/zhisheng01/Bat/blob/main/spatial-ast.pth) | Q-Former | adapter |[llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) 
+## Performance evaluation on **SpatialSoundQA** 
+We use [Spatial-AST](https://huggingface.co/datasets/zhisheng01/SpatialAudio/blob/main/SpatialAST/finetuned.pth) as audio encoder, [llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) as LLM backbone. We finetune the model by adding Q-Former and LoRA. To calculate MAP, you can refer to [calculate_map.py](https://github.com/X-LANCE/SLAM-LLM/blob/main/examples/seld_spatialsoundqa/scripts/calculate_map.py)
+<img src="assets/performance.png" alt="xxx">
+
+
+## Demo (Spatial Audio Inference)
+Try [`inference.ipynb`](https://github.com/X-LANCE/SLAM-LLM/blob/main/examples/seld_spatialsoundqa/inference.ipynb).
+
 
 ## Data preparation
 You need to prepare the data jsonl in this format. Below is an example.  
-You can download the SpatialSoundQA dataset from [huggingface](https://huggingface.co/datasets/zhisheng01/SpatialSoundQA).
-```
-{"audio_id": "eval/audio/YI-HlrcP6Qg4", "reverb_id": "q9vSo1VnCiC/0.npy", "audio_id2": null, "reverb_id2": null, "question_id": 0, "question_type": "CLASSIFICATION", "question": "Enumerate the sound occurrences in the audio clip.", "answer": "accelerating, revving, vroom; car; vehicle"}
+You can download the SpatialSoundQA dataset from [SpatialAudio](https://huggingface.co/datasets/zhisheng01/SpatialAudio).
+```json
+{
+  "audio_id": "eval/audio/YI-HlrcP6Qg4",
+  "reverb_id": "q9vSo1VnCiC/0.npy", 
+  "audio_id2": null, 
+  "reverb_id2": null, 
+  "question_id": 0, 
+  "question_type": "CLASSIFICATION", 
+  "question": "Enumerate the sound occurrences in the audio clip.", 
+  "answer": "accelerating, revving, vroom; car; vehicle"
+}
+
 ...
-{"audio_id": "eval/audio/YZX2fVPmUidA", "reverb_id": "q9vSo1VnCiC/32.npy", "audio_id2": "eval/audio/YjNjUU01quLs", "reverb_id2": "q9vSo1VnCiC/31.npy", "question_id": 58, "question_type": "MIXUP_NONBINARY_DISTANCE", "question": "How far away is the sound of the banjo from the sound of the whack, thwack?", "answer": "2m"}
+
+{
+  "audio_id": "eval/audio/YZX2fVPmUidA", 
+  "reverb_id": "q9vSo1VnCiC/32.npy", 
+  "audio_id2": "eval/audio/YjNjUU01quLs", 
+  "reverb_id2": "q9vSo1VnCiC/31.npy", 
+  "question_id": 58, 
+  "question_type": "MIXUP_NONBINARY_DISTANCE", 
+  "question": "How far away is the sound of the banjo from the sound of the whack, thwack?", 
+  "answer": "2m"
+}
 ```
 
 ## Train a new model
 ```bash
-bash examples/seld_spatialsoundqa/scripts/finetune_spatial-ast_qformer_llama_2_7b.sh
+cd examples/seld_spatialsoundqa/
+bash scripts/finetune_spatial-ast_qformer_llama_2_7b.sh
 ```
 
 ## Decoding with checkpoints
 ```bash
-bash examples/seld_spatialsoundqa/scripts/decode_spatial-ast_qformer_llama_2_7b.sh
+cd examples/seld_spatialsoundqa/
+bash scripts/decode_spatial-ast_qformer_llama_2_7b.sh
 ```
 
 
 ## TODO
 - [x] Decode with checkpoints
 - [x] Upload SpatialSoundQA dataset
-- [ ] Upload pretrained checkpoints
-- [ ] Update model performance
+- [x] Upload pretrained checkpoints
+- [x] Update model performance
 
 ## Citation
 ```

examples/seld_spatialsoundqa/dataset/spatial_audio_dataset.py

@@ -37,9 +37,8 @@ def __init__(
             split,
         ):
         super().__init__()
-        dataset_path = os.path.join(dataset_config['qa_data_root'], dataset_config['stage'], split + '.jsonl')
-        with open(dataset_path) as f:
-            self.data = [json.loads(line) for line in f.readlines()]
+        dataset_path = os.path.join(dataset_config['qa_data_root'], dataset_config['stage'], split + '.json')
+        self.data = json.load(open(dataset_path))["data"]
 
         self.anechoic_data_root = dataset_config['anechoic_data_root'] # which is AudioSet in this case
         self.reverb_data_root = dataset_config['reverb_data_root']

examples/seld_spatialsoundqa/finetune_seld.py

@@ -1,5 +1,6 @@
 import hydra
 import logging
+from typing import Optional
 from dataclasses import dataclass, field
 from omegaconf import DictConfig, ListConfig, OmegaConf
 
@@ -16,32 +17,20 @@ class RunConfig:
     peft_config: PeftConfig = field(default_factory=PeftConfig)
     debug: bool = field(default=False, metadata={"help": "Use pdb when true"})
     metric: str = field(default="acc", metadata={"help": "The metric for evaluation"})
-    ckpt_path: str = field(
-        default="output/model.pt", metadata={"help": "The path to projector checkpoint"}
+    ckpt_path: Optional[str] = field(
+        default=None, metadata={"help": "The path to projector checkpoint"}
     )
 
 @hydra.main(config_name=None, version_base=None)
 def main_hydra(cfg: DictConfig):
     run_config = RunConfig()
     cfg = OmegaConf.merge(run_config, cfg)
-    def to_plain_list(cfg_item):
-        if isinstance(cfg_item, ListConfig):
-            return OmegaConf.to_container(cfg_item, resolve=True)
-        elif isinstance(cfg_item, DictConfig):
-            return {k: to_plain_list(v) for k, v in cfg_item.items()}
-        else:
-            return cfg_item
-
-    # kwargs = to_plain_list(cfg)
-    kwargs = cfg
-    log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
+    cfg.train_config.peft_config = cfg.peft_config
+
+    log_level = getattr(logging, cfg.get("log_level", "INFO").upper())
     logging.basicConfig(level=log_level)
-
-    if kwargs.get("debug", False):
-        import pdb;
-        pdb.set_trace()
 
-    train(kwargs)
+    train(cfg)
 
 
 if __name__ == "__main__":

examples/seld_spatialsoundqa/inference_seld_batch.py

@@ -36,16 +36,11 @@ class RunConfig:
 def main_hydra(cfg: DictConfig):
     run_config = RunConfig()
     cfg = OmegaConf.merge(run_config, cfg)
-    # kwargs = to_plain_list(cfg)
-    log_level = getattr(logging, cfg.get("log_level", "INFO").upper())
+    cfg.train_config.peft_config = cfg.peft_config
 
+    log_level = getattr(logging, cfg.get("log_level", "INFO").upper())
     logging.basicConfig(level=log_level)
 
-    if cfg.get("debug", False):
-        import pdb
-
-        pdb.set_trace()
-
     inference(cfg)
 
 

examples/seld_spatialsoundqa/model/slam_model_seld.py

@@ -74,26 +74,5 @@ def __init__(
             tokenizer,
             train_config,
             model_config,
-            **kwargs,
-        )
-
-    @torch.no_grad()
-    def inference(
-        self,
-        wav_path=None,
-        reverb_path=None,
-        prompt=None,
-        generation_config=None,
-        logits_processor=None,
-        stopping_criteria=None,
-        prefix_allowed_tokens_fn=None,
-        synced_gpus=None,
-        assistant_model=None,
-        streamer=None,
-        negative_prompt_ids=None,
-        negative_prompt_attention_mask=None,
-        **kwargs,
-    ):
-        #!TODO: 
-        # inference for SELD model
-        pass
+            **kwargs
+        )

examples/seld_spatialsoundqa/scripts/calculate_map.py

@@ -0,0 +1,73 @@
+import os
+
+import numpy as np
+from sklearn import metrics
+
+from tqdm import tqdm
+import openai
+
+openai.api_key = "your-openai-api-key"
+
+def cosine_similarity(A, B):
+    dot_product = np.dot(A, B)
+    norm_A = np.linalg.norm(A)
+    norm_B = np.linalg.norm(B)
+    return dot_product / (norm_A * norm_B)
+
+def get_embedding(text, model="text-embedding-ada-002"):
+   text = text.replace("\n", " ")
+   return np.array(openai.Embedding.create(input = [text], model=model)['data'][0]['embedding'])
+
+def calculate_stats(output, target):
+    classes_num = target.shape[-1]
+    stats = []
+
+    for k in range(classes_num):
+        avg_precision = metrics.average_precision_score(target[:, k], output[:, k], average=None)
+        dict = {'AP': avg_precision}
+        stats.append(dict)
+
+    return stats
+
+labels_path = 'https://huggingface.co/datasets/zhisheng01/SpatialAudio/blob/main/SpatialSoundQA/AudioSet/metadata/class_labels_indices_subset.csv'
+embeds_npy_path = 'https://huggingface.co/datasets/zhisheng01/SpatialAudio/blob/main/SpatialSoundQA/AudioSet/metadata/audioset_class_embeds.npy'
+
+label2id = {}
+with open(labels_path) as f:
+    for idx, line in enumerate(f.readlines()[1:]):
+        label = line.strip().split(',', 2)[-1]
+        label2id[label.lower()] = idx
+#         label2emb.append(get_embedding(label))
+
+# label2emb = np.stack(label2emb)
+# np.save(embeds_npy_path, label2emb)
+
+total_labels_embeddings = np.load(embeds_npy_path)
+
+one_hot_embeds = np.eye(355)
+
+with open("decode_eval-stage2-classification_beam4_gt") as gt_f:
+    gt_lines = gt_f.readlines()
+    targets = []
+    for line in gt_lines:
+        target = np.array([one_hot_embeds[label2id[i]] for i in line.strip().split('\t', 1)[1].split("; ")]).sum(axis=0)
+        targets.append(target)
+    targets = np.stack(targets)
+
+
+with open("decode_eval-stage2-classification_beam4_pred") as pred_f:
+    pred_lines = pred_f.readlines()
+    preds = []
+    for line in tqdm(pred_lines):
+        pred = line.strip().split('\t', 1)[1]
+        pred = get_embedding(pred)
+        pred = np.array([cosine_similarity(pred, embed) for embed in total_labels_embeddings])
+        preds.append(pred)
+
+    preds = np.stack(preds)
+
+stats = calculate_stats(preds, targets)
+
+AP = [stat['AP'] for stat in stats]
+mAP = np.mean([stat['AP'] for stat in stats])
+print("mAP: {:.6f}".format(mAP))

examples/seld_spatialsoundqa/scripts/decode_spatial-ast_qformer_llama_2_7b.sh

@@ -1,24 +1,24 @@
 #!/bin/bash
-#export PYTHONPATH=/root/whisper:$PYTHONPATH
-export CUDA_VISIBLE_DEVICES=2
+
+export CUDA_VISIBLE_DEVICES=0
 export TOKENIZERS_PARALLELISM=false
 # export CUDA_LAUNCH_BLOCKING=1
 
-SLAM_DIR=/mnt/lustre/hpc_stor03/sjtu_home/zhisheng.zheng/SLAM-LLM
+SLAM_DIR=/path/to/SLAM-LLM
 cd $SLAM_DIR
 code_dir=examples/seld_spatialsoundqa
 
-stage=stage1-clsdoa
-qa_data_root=/mnt/lustre/hpc_stor03/sjtu_home/zhisheng.zheng/data/SpatialAudio/closed-end
-reverb_data_root=/mnt/lustre/hpc_stor03/sjtu_home/zhisheng.zheng/data/SpatialAudio/reverb/mp3d
-anechoic_data_root=/mnt/lustre/hpc_stor03/sjtu_home/zhisheng.zheng/data/AudioSet
+audio_encoder_path=/data1/scratch/zhisheng/models/SpatialAST/SpatialAST.pth # https://huggingface.co/datasets/zhisheng01/SpatialAudio/blob/main/SpatialAST/finetuned.pth
+llm_path=/home/zhisheng/models/llama-2-hf # https://huggingface.co/meta-llama/Llama-2-7b-hf
 
-audio_encoder_path=/mnt/lustre/hpc_stor03/sjtu_home/zhisheng.zheng/models/SpatialAST/SpatialAST.pth
-llm_path=/mnt/lustre/hpc_stor03/sjtu_pub/cxgroup/model/Llama-2-7b-hf
+stage=stage2-single
+qa_data_root=/data3/scratch/zhisheng/SpatialAudio/SpatialSoundQA/closed-end # https://huggingface.co/datasets/zhisheng01/SpatialAudio/tree/main/SpatialSoundQA/closed-end
+reverb_data_root=/data3/scratch/zhisheng/SpatialAudio/SpatialSoundQA/mp3d_reverb # https://huggingface.co/datasets/zhisheng01/SpatialAudio/blob/main/SpatialSoundQA/mp3d_reverb.zip
+anechoic_data_root=/data3/scratch/zhisheng/SpatialAudio/SpatialSoundQA/AudioSet # https://huggingface.co/datasets/zhisheng01/SpatialAudio/tree/main/SpatialSoundQA/AudioSet
 
-split=eval
-output_dir=/mnt/lustre/hpc_stor03/sjtu_home/zhisheng.zheng/SLAM-LLM/outputs/bat-llama-2-spatialAST-8qformer-steplrwarmupkeep1e-4-stage1-clsdoa-20240519/
-ckpt_path=$output_dir/bat_epoch_3_step_3288
+split=eval-stage2-classification
+output_dir=?? # be same as in finetune script
+ckpt_path=$output_dir/bat_epoch_4_step_18223
 decode_log=$ckpt_path/decode_${split}_beam4
 
 # -m debugpy --listen 5678 --wait-for-client
@@ -30,29 +30,25 @@ python -u $code_dir/inference_seld_batch.py \
         ++model_config.llm_dim=4096 \
         ++model_config.encoder_name=SpatialAST \
         ++model_config.encoder_projector=q-former \
+        ++model_config.qformer_layers=8 \
         ++model_config.encoder_ckpt=$audio_encoder_path \
+        ++dataset_config.test_split=${split} \
         ++dataset_config.stage=$stage \
         ++dataset_config.qa_data_root=$qa_data_root \
         ++dataset_config.anechoic_data_root=$anechoic_data_root \
         ++dataset_config.reverb_data_root=$reverb_data_root \
         ++dataset_config.fix_length_audio=64 \
         ++dataset_config.inference_mode=true \
-        ++train_config.model_name=bat \
+        ++train_config.model_name=BAT \
         ++train_config.freeze_encoder=true \
         ++train_config.freeze_llm=true \
         ++train_config.batching_strategy=custom \
         ++train_config.num_epochs=1 \
-        ++train_config.val_batch_size=8 \
-        ++train_config.num_workers_dataloader=2 \
+        ++train_config.val_batch_size=1 \
+        ++train_config.num_workers_dataloader=1 \
         ++train_config.output_dir=$output_dir \
         ++train_config.use_peft=true \
-        ++peft_config.peft_method=llama_adapter \
+        ++peft_config.peft_method=lora \
         ++log_config.log_file=$output_dir/test.log \
         ++decode_log=$decode_log \
-        ++ckpt_path=$ckpt_path/model.pt \
-        # ++peft_ckpt=$ckpt_path \
-        # ++train_config.use_peft=true \
-        # ++train_config.peft_config.r=32 \
-        # ++dataset_config.normalize=true \
-        # ++model_config.encoder_projector=q-former \
-        # ++dataset_config.fix_length_audio=64 \
+        ++ckpt_path=$ckpt_path/model.pt