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Add cls docs of training and custom dataset. test=doc_fix

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# Customize Dataset for Audio Classification
Following this tutorial you can customize your dataset for audio classification task by using `paddlespeech` and `paddleaudio`.
A base class of classification dataset is `paddleaudio.dataset.AudioClassificationDataset`. To customize your dataset you should write a dataset class derived from `AudioClassificationDataset`.
Assuming you have some wave files that stored in your own directory. You should prepare a meta file with the information of filepaths and labels. For example the absolute path of it is `/PATH/TO/META_FILE.txt`:
```
/PATH/TO/WAVE_FILE/1.wav cat
/PATH/TO/WAVE_FILE/2.wav cat
/PATH/TO/WAVE_FILE/3.wav dog
/PATH/TO/WAVE_FILE/4.wav dog
```
Here is an example to build your custom dataset in `custom_dataset.py`:
```python
from paddleaudio.datasets.dataset import AudioClassificationDataset
class CustomDataset(AudioClassificationDataset):
# All *.wav file with same sample rate 16k/24k/32k/44k.
sample_rate = 16000
meta_file = '/PATH/TO/META_FILE.txt'
# List all the class labels
label_list = [
'cat',
'dog',
]
def __init__(self):
files, labels = self._get_data()
super(CustomDataset, self).__init__(
files=files, labels=labels, feat_type='raw')
def _get_data(self):
'''
This method offer information of wave files and labels.
'''
files = []
labels = []
with open(self.meta_file) as f:
for line in f:
file, label_str = line.strip().split(' ')
files.append(file)
labels.append(self.label_list.index(label_str))
return files, labels
```
Then you can build dataset and data loader from `CustomDataset`:
```python
import paddle
from paddleaudio.features import LogMelSpectrogram
from custom_dataset import CustomDataset
train_ds = CustomDataset()
feature_extractor = LogMelSpectrogram(sr=train_ds.sample_rate)
train_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=4, shuffle=True, drop_last=False)
train_loader = paddle.io.DataLoader(
train_ds,
batch_sampler=train_sampler,
return_list=True,
use_buffer_reader=True)
```
Train model with `CustomDataset`:
```python
from paddlespeech.cls.models import cnn14
from paddlespeech.cls.models import SoundClassifier
backbone = cnn14(pretrained=True, extract_embedding=True)
model = SoundClassifier(backbone, num_class=len(train_ds.label_list))
optimizer = paddle.optimizer.Adam(
learning_rate=1e-6, parameters=model.parameters())
criterion = paddle.nn.loss.CrossEntropyLoss()
steps_per_epoch = len(train_sampler)
epochs = 10
for epoch in range(1, epochs + 1):
model.train()
for batch_idx, batch in enumerate(train_loader):
waveforms, labels = batch
# Need a padding when lengths of waveforms differ in a batch.
feats = feature_extractor(waveforms)
feats = paddle.transpose(feats, [0, 2, 1])
logits = model(feats)
loss = criterion(logits, labels)
loss.backward()
optimizer.step()
if isinstance(optimizer._learning_rate,
paddle.optimizer.lr.LRScheduler):
optimizer._learning_rate.step()
optimizer.clear_grad()
# Calculate loss
avg_loss = loss.numpy()[0]
# Calculate metrics
preds = paddle.argmax(logits, axis=1)
num_corrects = (preds == labels).numpy().sum()
num_samples = feats.shape[0]
avg_acc = num_corrects / num_samples
print_msg = 'Epoch={}/{}, Step={}/{}'.format(
epoch, epochs, batch_idx + 1, steps_per_epoch)
print_msg += ' loss={:.4f}'.format(avg_loss)
print_msg += ' acc={:.4f}'.format(avg_acc)
print_msg += ' lr={:.6f}'.format(optimizer.get_lr())
print(print_msg)
```
If you want to save the checkpoint of model and evaluate from a specific dataset, please see `paddlespeech/cli/exp/panns/train.py` for more details.
# Quick Start of Audio Classification
Several shell scripts provided in `./examples/esc50/cls0` will help us to quickly give it a try, for most major modules, including data preparation, model training, model evaluation, with [ESC50](ttps://github.com/karolpiczak/ESC-50) dataset.
Some of the scripts in `./examples` are not configured with GPUs. If you want to train with 8 GPUs, please modify `CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7`. If you don't have any GPU available, please set `CUDA_VISIBLE_DEVICES=` to use CPUs instead.
Let's start a audio classification task with the following steps:
- Go to the directory
```bash
cd examples/esc50/cls0
```
- Source env
```bash
source path.sh
```
- Main entry point
```bash
CUDA_VISIBLE_DEVICES=0 ./run.sh 1
```
This demo includes fine-tuning, evaluating and deploying a audio classificatio model. More detailed information is provided in the following sections.
## Fine-tuning a model
PANNs([PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/pdf/1912.10211.pdf)) are pretrained models with [Audioset](https://research.google.com/audioset/). They can be easily used to extract audio embeddings for audio classification task.
To start a model fine-tuning, please run:
```bash
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
feat_backend=numpy
./local/train.sh ${ngpu} ${feat_backend}
```
## Deploy a model
Once you save a model checkpoint, you can export it to static graph and deploy by python scirpt:
- Export to a static graph
```bash
./local/export.sh ${ckpt_dir} ./export
```
The argument `ckpt_dir` should be a directory in which a model checkpoint stored, for example `checkpoint/epoch_50`.
The static graph will be exported to `./export`.
- Inference
```bash
./local/static_model_infer.sh ${infer_device} ./export ${audio_file}
```
The argument `infer_device` can be `cpu` or `gpu`, and it means which device to be used to infer. And `audio_file` should be a wave file with name `*.wav`.
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