## OOD-ASR: Out-of-domain Automatic Speech Recognition Tasks

specified by the command `-d ctc`

### Cross-lingual Tasks
1. Download [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/datasets)
   - `es`: Spanish
   - `zh-CN`: Chinese (China)
   - `ar`: Arabic
2. Modify the preprocessing script `corpus/preprocess_cv.sh`
   - `cv_root`: path to common voice 7.0 dataset
   - `data_root`: path to save preprocessed data (`.tsv` files)
3. Run the following script
   ```bash
   cd downstream/ctc/corpus
   bash preprocess_cv.sh
   ```
   This step includes preprocessing transcriptions and downsampling audio waveforms.
4. Check processed files (in `${data_root}`)
    ```
    .
    ├── ar
    │   ├── dev.tsv
    │   ├── test.tsv
    │   ├── train.tsv
    │   └── train.txt
    ├── es
    │   ├── dev.tsv
    │   ├── test.tsv
    │   ├── train.tsv
    │   └── train.txt
    └── zh-CN
        ├── dev.tsv
        ├── test.tsv
        ├── train.tsv
        └── train.txt
    ```
5. Modify training configs in `cv_config/`
    ```
    downstream_expert:
        corpus:
            name: 'common_voice'
            path: 'path/to/cv-corpus-7.0-2021-07-21/.../clips'

            train: ['path/to/train.tsv']
            dev: ['path/to/dev.tsv']
            test: ['path/to/test.tsv']
    ```
6. Training
    ```bash
    parser.add_argument('-k', '--upstream_ckpt', metavar='{PATH,URL,GOOGLE_DRIVE_ID}', help='Only set when the specified upstream need it')
    parser.add_argument('-g', '--upstream_model_config', help='The config file for constructing the pretrained model')
    parser.add_argument('-r', '--upstream_refresh', action='store_true', help='Re-download cached ckpts for on-the-fly upstream variants')
    parser.add_argument('-f', '--upstream_trainable', action='store_true', help='Fine-tune, set upstream.train(). Default is upstream.eval()')
    parser.add_argument('-s', '--upstream_feature_selection', default='hidden_states', help='Specify the layer to be extracted as the representation')
    parser.add_argument('-l', '--upstream_layer_selection', type=int, help='Select a specific layer for the features selected by -s')
    parser.add_argument('--upstream_feature_normalize', action='store_true', help='Specify whether to normalize hidden features before weighted sum')
    parser.add_argument('--upstream_model_name', default="model.pt", help='The name of the model file in the HuggingFace Hub repo.')
    parser.add_argument('--upstream_revision', help="The commit hash of the specified HuggingFace Repository")

    python3 run_downstream.py -n ExpName -m train -u Upstream -d ctc -c downstream/ctc/cv_config/cv_${lang}.yaml
    ```
    Replace `${lang}` with `es`, `zh`, or `ar`.
7. Testing
    ```bash
    python3 run_downstream.py -m evaluate -e result/downstream/ExpName/dev-best.ckpt
    ```

### Spontaneous Speech
1. Clone [vectominist/SBCSAE-preprocess](https://github.com/vectominist/SBCSAE-preprocess) for data preprocessing
    ```bash
    git clone https://github.com/vectominist/SBCSAE-preprocess.git
    ```
2. Follow the instructions in [vectominist/SBCSAE-preprocess] to download and process data.
3. Modify training the config in `sbcsae.yaml`
    ```
    downstream_expert:
        corpus:
            name: 'sbcsae'
            path: 'path/to/SBCSAE/wav'

            train: ['path/to/sbcsae/train.tsv']
            dev: ['path/to/sbcsae/dev.tsv']
            test: ['path/to/sbcsae/test.tsv']
    ```
4. Training
    ```bash
    python3 run_downstream.py -n ExpName -m train -u Upstream -d ctc -c downstream/ctc/sbcsae.yaml
    ```
5. Testing
    ```bash
    python3 run_downstream.py -m evaluate -e result/downstream/ExpName/dev-best.ckpt
    ```
