[Feature] Add audio demo

demo/README.md

@@ -10,6 +10,7 @@
 - [SpatioTemporal Action Detection Video Demo](#spatiotemporal-action-detection-video-demo): A demo script to predict the spatiotemporal action detection result using a single video.
 - [SpatioTemporal Action Detection ONNX Video Demo](#spatiotemporal-action-detection-onnx-video-demo): A demo script to predict the SpatioTemporal Action Detection result using the onnx file instead of building the PyTorch models.
 - [Inferencer Demo](#inferencer): A demo script to implement fast predict for video analysis tasks based on unified inferencer interface.
+- [Audio Demo](#audio-demo): A demo script to predict the recognition result using a single audio file.
 
 ## Modify configs through script arguments
 
@@ -438,3 +439,31 @@ Assume that you are located at `$MMACTION2`.
        --rec tsn \
        --label-file tools/data/kinetics/label_map_k400.txt
    ```
+
+## Audio Demo
+
+Demo script to predict the audio-based action recognition using a single audio feature.
+
+The script [`extract_audio.py`](/tools/data/extract_audio.py) can be used to extract audios from videos and the script [`build_audio_features.py`](/tools/data/build_audio_features.py) can be used to extract the audio features.
+
+```shell
+python demo/demo_audio.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${AUDIO_FILE} {LABEL_FILE} [--device ${DEVICE}]
+```
+
+Optional arguments:
+
+- `DEVICE`: Type of device to run the demo. Allowed values are cuda devices like `cuda:0` or `cpu`. If not specified, it will be set to `cuda:0`.
+
+Examples:
+
+Assume that you are located at `$MMACTION2` and have already downloaded the checkpoints to the directory `checkpoints/`,
+or use checkpoint url from `configs/` to directly load the corresponding checkpoint, which will be automatically saved in `$HOME/.cache/torch/checkpoints`.
+
+1. Recognize an audio file as input by using a tsn model on cuda by default.
+
+   ```shell
+   python demo/demo_audio.py \
+       configs/recognition_audio/resnet/tsn_r18_8xb320-64x1x1-100e_kinetics400-audio-feature.py \
+       https://download.openmmlab.com/mmaction/v1.0/recognition_audio/resnet/tsn_r18_8xb320-64x1x1-100e_kinetics400-audio-feature/tsn_r18_8xb320-64x1x1-100e_kinetics400-audio-feature_20230702-e4642fb0.pth \
+       audio_feature.npy tools/data/kinetics/label_map_k400.txt
+   ```

demo/demo_audio.py

@@ -0,0 +1,57 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+from operator import itemgetter
+
+import torch
+from mmengine import Config, DictAction
+
+from mmaction.apis import inference_recognizer, init_recognizer
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='MMAction2 demo')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file/url')
+    parser.add_argument('audio', help='audio file')
+    parser.add_argument('label', help='label file')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        default={},
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. For example, '
+        "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
+    parser.add_argument(
+        '--device', type=str, default='cuda:0', help='CPU/CUDA device option')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    device = torch.device(args.device)
+    cfg = Config.fromfile(args.config)
+    cfg.merge_from_dict(args.cfg_options)
+    model = init_recognizer(cfg, args.checkpoint, device=device)
+
+    if not args.audio.endswith('.npy'):
+        raise NotImplementedError('Demo works on extracted audio features')
+    pred_result = inference_recognizer(model, args.audio)
+
+    pred_scores = pred_result.pred_scores.item.tolist()
+    score_tuples = tuple(zip(range(len(pred_scores)), pred_scores))
+    score_sorted = sorted(score_tuples, key=itemgetter(1), reverse=True)
+    top5_label = score_sorted[:5]
+
+    labels = open(args.label).readlines()
+    labels = [x.strip() for x in labels]
+    results = [(labels[k[0]], k[1]) for k in top5_label]
+
+    print('The top-5 labels with corresponding scores are:')
+    for result in results:
+        print(f'{result[0]}: ', result[1])
+
+
+if __name__ == '__main__':
+    main()

mmaction/apis/inference.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
 from pathlib import Path
 from typing import List, Optional, Union
 
@@ -80,8 +81,11 @@ def inference_recognizer(model: nn.Module,
     input_flag = None
     if isinstance(video, dict):
         input_flag = 'dict'
-    elif isinstance(video, str):
-        input_flag = 'video'
+    elif isinstance(video, str) and osp.exists(video):
+        if video.endswith('.npy'):
+            input_flag = 'audio'
+        else:
+            input_flag = 'video'
     else:
         raise RuntimeError(f'The type of argument `video` is not supported: '
                            f'{type(video)}')
@@ -90,6 +94,12 @@ def inference_recognizer(model: nn.Module,
         data = video
     if input_flag == 'video':
         data = dict(filename=video, label=-1, start_index=0, modality='RGB')
+    if input_flag == 'audio':
+        data = dict(
+            audio_path=video,
+            total_frames=len(np.load(video)),
+            start_index=0,
+            label=-1)
 
     data = test_pipeline(data)
     data = pseudo_collate([data])