Wav2vec2-Pretraining

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Wav2vec2-Pretraining

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Wav2vec 2.0 Self-Supervised Pretraining

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# PRETRAINING WAV2VEC 2.0 ON YOUR OWN DATASETS
<a name = "documentation" ></a>
### Documentation
Now you can pre-train Wav2vec 2.0 model on your dataset, push it into the Huggingface hub, and finetune it on downstream tasks with just a few lines of code. Follow the below instruction on how to use it.

### Supporting features include:
- [x] Train on your own local datasets.
- [x] Visualization with Tensorboard.
- [x] Resume training (optimizer, a learning rate scheduler, gradient scaler,...) from the latest checkpoint.
- [x] No data caching. Data is loaded on the fly (no disk memory consuming).
- [x] Checkpoint is saved separately at each epoch.

<a name = "installation" ></a>
### Installation
```
pip install -r requirements.txt
```

<a name = "usage" ></a>
### Usage
1. Prepare your dataset:
 - Your dataset can be in .txt or .csv format.
 - Only PATH column is compulsory, the others (eg: DURATION, TRANSCRIPT, ...) are not necessary. PATH contains the paths to your stored audio files. Depending on your dataset location, it can be either absolute paths or relative paths. 
 - If DURATION column is not provided, all audio will be used. Audio length should be at least 0.5s.
 - Check out our [data_example.csv](examples/data_example.csv) file for more information.

2. Run: I strongly recommend running ```python run.py --help``` to understand the arguments before training.
 - Train:
 ```
 CUDA_VISIBLE_DEVICES="0" accelerate launch \
 --multi_gpu \
 --num_machines="1" \
 --num_processes="1" \
 --mixed_precision="fp16" \
 --num_cpu_threads_per_process="12" \
 run_wav2vec2_pretraining_no_trainer.py \
 --train_datasets \ 
 data/train_clean_100.tsv \
 data/train_clean_360.tsv \
 data/train_other_500.tsv \
 --val_datasets \
 data/dev_clean.tsv \
 data/dev_other.tsv \
 --audio_column_name="path" \
 --duration_column_name="duration" \
 --separator="\t" \
 --model_name_or_path="facebook/wav2vec2-base" \
 --load_from_pretrained \
 --output_dir="wav2vec2_pretraining" \
 --max_train_steps="200000" \
 --num_warmup_steps="32000" \
 --gradient_accumulation_steps="8" \
 --learning_rate="0.005" \
 --weight_decay="0.01" \
 --max_duration_in_seconds="15.6" \
 --min_duration_in_seconds="0.5" \
 --logging_steps="1" \
 --saving_steps="100" \
 --per_device_train_batch_size="16" \
 --per_device_eval_batch_size="8" \
 --gradient_checkpointing
 ```
 - Resume: Same as Train, but with an additional argument
 ```
 --resume
 ```
3. Tips for training: Some good metrics to guarantee your pretraining process is running right: contrast_loss and cosine_sim. Usually, the contrastive loss should be below 2.0, and cosine_sim should be higher than 50%.

4. How to use your pre-trained model: 
    - I load my pre-trained model checkpoint from epoch 10 and get the last hidden state embedding:
        ```
        from transformers import Wav2Vec2Processor, Wav2Vec2Model
        import torch
        import librosa

# load audio
 wav, sr = librosa.load(<audio_path>, sr=16000)

# load pretrained
 feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("<output_dir>/saved_model/epoch_10")
 model = Wav2Vec2Model.from_pretrained("<output_dir>/saved_model/epoch_10")

# run forward pass
        inputs = feature_extractor(wav, sampling_rate=sr, return_tensors="pt")
        with torch.no_grad():
            outputs = model(**inputs)

last_hidden_state = outputs.last_hidden_state
        print(last_hidden_state.shape)
        ```
    - Finetune for ASR task: Check out this [REPO](https://github.com/khanld/ASR-Wav2vec-Finetune) for finetuning Wav2vec 2.0 for Automatic Speech Recognition using Connectionist Temporal Classification.

<a name = "logs" ></a>
### Logs and Visualization
The logs during the training will be stored, and you can visualize it using TensorBoard by running this command:
```
# specify the <name> in config.json
tensorboard --logdir ~/<output_dir>/logs

# specify a port 8080
tensorboard --logdir ~/<output_dir>/logs --port 8080
```
![tensorboard](https://raw.githubusercontent.com/khanld/Wav2vec2-Pretraining/main/examples/TensorBoard.png)

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