# data2vec-aqc 


## Training a new speech model with the CLI tools

Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length)

### Prepare training data manifest:

First, install the `soundfile` library:
```shell script
pip install soundfile
```

Next, run:

```shell script
$ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext $ext --valid-percent $valid
```

$ext should be set to flac, wav, or whatever format your dataset happens to use that soundfile can read.

$valid should be set to some reasonable percentage (like 0.01) of training data to use for validation.
To use a pre-defined validation set (like dev-other from librispeech), set to it 0 and then overwrite valid.tsv with a
separately pre-processed manifest file.

### Train a data2vec-aqc Base model:

This configuration was used for the base model trained on the Librispeech dataset in the data2vec-aqc paper

Note that the input is expected to be single channel, sampled at 16 kHz

```shell script
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/audio/pretraining \
--config-name base_librispeech task.data=/path/to/manifests common.user_dir=examples/data2vec
```

Note: you can simulate 16 GPUs by using k GPUs and adding command line parameters
`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 16/k

### Parameters of interest

* The `cluster_factor` and `scale_factor` parameters (for the clustering module) can be modified from the `model` section of the pre-training configs which can be found from the [pre-training config](https://github.com/Speech-Lab-IITM/data2vec-aqc/examples/data2vec/config/audio/pretraining).
* The augmentations used for data2vec-aqc requires the noise set of MUSAN dataset. The path to the same is to be specified in the `path_to_musan_noise_set` variable of the __getitem__ method of the [raw_audio_dataset](https://github.com/Speech-Lab-IITM/data2vec-aqc/fairseq/data/audio/raw_audio_dataset.py) file.

### Fine-tune a pre-trained model with CTC:

Fine-tuning a model requires parallel audio and labels file, as well as a vocabulary file in fairseq format.
A letter vocabulary can be downloaded [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt).
An example [script](../wav2vec/libri_labels.py) that generates labels for the Librispeech dataset from the tsv file produced by wav2vec_manifest.py can be used as follows:

```shell script
split=train
$ python libri_labels.py /path/to/tsv --output-dir /output/dir --output-name $split
```

Fine-tuning on 100h of Librispeech with letter targets:
```shell script
$ fairseq-hydra-train \
    distributed_training.distributed_port=$PORT \
    task.data=/path/to/data \
    model.w2v_path=/path/to/model.pt \
    --config-dir /path/to/fairseq-py/examples/wav2vec/config/finetuning \
    --config-name base_100h common.user_dir=examples/data2vec
```

There are other config files in the config/finetuning directory that can be used to fine-tune on other splits.
You can specify the right config via the `--config-name` parameter.

Decoding with a language model during training requires flashlight [python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter).
If you want to use a language model, add `+criterion.wer_args='[/path/to/kenlm, /path/to/lexicon, 2, -1]'` to the command line.

### Evaluating a CTC model:

Evaluating a CTC model with a language model requires [flashlight python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter) to be installed.

Fairseq transformer language model used in the wav2vec 2.0 paper can be obtained from the [wav2letter model repository](https://github.com/facebookresearch/wav2letter/tree/master/recipes/sota/2019).
Be sure to upper-case the language model vocab after downloading it.

Letter dictionary for pre-trained models can be found [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt).

Next, run the evaluation command:

```shell script
python examples/speech_recognition/new/infer.py --config-dir examples/speech_recognition/new/conf \
--config-name infer task=audio_finetuning task.data=/path/to/manifests common.user_dir=examples/data2vec \
task.labels=ltr decoding.type=kenlm \
decoding.lmweight=${lmweight} decoding.wordscore=${wordscore} decoding.silweight=${silscore} \
decoding.lexicon=/path/to/lexicon \
decoding.lmpath=/path/to/lm decoding.unique_wer_file=True \
dataset.gen_subset=dev_clean,dev_other,test_clean,test_other \
common_eval.path=/path/to/checkpoint.pt decoding.beam=1500 distributed_training.distributed_world_size=${num_gpus}
```

To get raw numbers, use decoding.type=viterbi and omit the lexicon. To use the transformer language model, use decoding.type=fairseqlm.