icefall/egs/audioset/AT/RESULTS.md

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## Results
### zipformer
See <https://github.com/k2-fsa/icefall/pull/1421> for more details
[zipformer](./zipformer)
#### normal-scaled model, number of model parameters: 65549011, i.e., 65.55 M
You can find a pretrained model, training logs, decoding logs, and decoding results at:
<https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-2024-03-12#/>
The model achieves the following mean averaged precision on AudioSet:
| Model | mAP |
| ------ | ------- |
| Zipformer-AT | 45.1 |
The training command is:
```bash
export CUDA_VISIBLE_DEVICES="4,5,6,7"
subset=full
python zipformer/train.py \
--world-size 4 \
--num-epochs 50 \
--exp-dir zipformer/exp_at_as_${subset} \
--start-epoch 1 \
--use-fp16 1 \
--num-events 527 \
--audioset-subset $subset \
--max-duration 1000 \
--enable-musan True \
--master-port 13455
```
We recommend that you train the model with weighted sampler, as the model converges
faster with better performance:
| Model | mAP |
| ------ | ------- |
| Zipformer-AT, train with weighted sampler | 46.6 |
The evaluation command is:
```bash
export CUDA_VISIBLE_DEVICES="4,5,6,7"
subset=full
weighted_sampler=1
bucket_sampler=0
lr_epochs=15
python zipformer/train.py \
--world-size 4 \
--audioset-subset $subset \
--num-epochs 120 \
--start-epoch 1 \
--use-fp16 1 \
--num-events 527 \
--lr-epochs $lr_epochs \
--exp-dir zipformer/exp_AS_${subset}_weighted_sampler${weighted_sampler} \
--weighted-sampler $weighted_sampler \
--bucketing-sampler $bucket_sampler \
--max-duration 1000 \
--enable-musan True \
--master-port 13452
```
The command for evaluation is the same. The pre-trained model can be downloaded from https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-M-weighted-sampler
#### small-scaled model, number of model parameters: 22125218, i.e., 22.13 M
You can find a pretrained model, training logs, decoding logs, and decoding results at:
<https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-small-2024-04-23#/>
The model achieves the following mean averaged precision on AudioSet:
| Model | mAP |
| ------ | ------- |
| Zipformer-S-AT | 45.1 |
The training command is:
```bash
export CUDA_VISIBLE_DEVICES="4,5,6,7"
subset=full
python zipformer/train.py \
--world-size 4 \
--num-epochs 50 \
--exp-dir zipformer/exp_small_at_as_${subset} \
--start-epoch 1 \
--use-fp16 1 \
--num-events 527 \
--num-encoder-layers 2,2,2,2,2,2 \
--feedforward-dim 512,768,768,768,768,768 \
--encoder-dim 192,256,256,256,256,256 \
--encoder-unmasked-dim 192,192,192,192,192,192 \
--audioset-subset $subset \
--max-duration 1200 \
--enable-musan True \
--master-port 13455
```
The evaluation command is:
```bash
python zipformer/evaluate.py \
--epoch 31 \
--avg 4 \
--num-encoder-layers 2,2,2,2,2,2 \
--feedforward-dim 512,768,768,768,768,768 \
--encoder-dim 192,256,256,256,256,256 \
--encoder-unmasked-dim 192,192,192,192,192,192 \
--exp-dir zipformer/exp_small_at_as_full \
--max-duration 500
```