Full Code of iamyuanchung/Autoregressive-Predictive-Coding for AI

Repository: iamyuanchung/Autoregressive-Predictive-Coding
Branch: master
Commit: 890c40dcaad3
Files: 7
Total size: 23.1 KB

Directory structure:
gitextract_f00nvz2h/

├── README.md
├── apc_model.py
├── datasets.py
├── load_pretrained_model.py
├── prepare_data.py
├── train_apc.py
└── utils.py

================================================
FILE CONTENTS
================================================

================================================
FILE: README.md
================================================
## Autoregressive Predictive Coding
This repository contains the official implementation (in PyTorch) of Autoregressive Predictive Coding (APC) proposed in [An Unsupervised Autoregressive Model for Speech Representation Learning](https://arxiv.org/abs/1904.03240).

APC is a speech feature extractor trained on a large amount of unlabeled data. With an unsupervised, autoregressive training objective, representations learned by APC not only capture general acoustic characteristics such as speaker and phone information from the speech signals, but are also highly accessible to downstream models--our experimental results on phone classification show that a linear classifier taking the APC representations as the input features significantly outperforms a multi-layer percepron using the surface features.

## Dependencies
* Python 3.5
* PyTorch 1.0

## Dataset
In the paper, we used the train-clean-360 split from the [LibriSpeech](http://www.openslr.org/12/) corpus for training the APC models, and the dev-clean split for keeping track of the training loss. We used the log Mel spectrograms, which were generated by running the Kaldi scripts, as the input acoustic features to the APC models. Of course you can generate the log Mel spectrograms yourself, but to help you better reproduce our results, here we provide the links to the data proprocessed by us that can be directly fed to the APC models. We also include other data splits that we did not use in the paper for you to explore, e.g., you can try training an APC model on a larger and nosier set (e.g., train-other-500) and see if it learns more robust speech representations.
* [train-clean-100](https://www.dropbox.com/s/kl6ivulhucukdz1/train-clean-100.xz?dl=0)
* [train-clean-360](https://www.dropbox.com/s/0hzg2momellrpoj/train-clean-360.xz?dl=0) (used for training APC models in our paper)
* [train-other-500](https://www.dropbox.com/s/uy0aex30ufq2po8/train-other.xz?dl=0)
* [dev-clean](https://www.dropbox.com/s/4f1ypyowwmkfapx/dev-clean.xz?dl=0) (used for tracing the training loss)

## Training APC
Below we will follow the paper and use train-clean-360 and dev-clean as demonstration. Once you have downloaded the data, unzip them by running:
```bash
xz -d train-clean-360.xz
xz -d dev-clean.xz
```
Then, create a directory `librispeech_data/kaldi` and move the data into it:
```bash
mkdir -p librispeech_data/kaldi
mv train-clean-360-hires-norm.blogmel librispeech_data/kaldi
mv dev-clean-hires-norm.blogmel librispeech_data/kaldi
```
Now we will have to transform the data into the format loadable by the PyTorch DataLoader. To do so, simply run:
```bash
# Prepare the training set
python prepare_data.py --librispeech_from_kaldi librispeech_data/kaldi/train-clean-360-hires-norm.blogmel --save_dir librispeech_data/preprocessed/train-clean-360-hires-norm.blogmel
# Prepare the valication set
python prepare_data.py --librispeech_from_kaldi librispeech_data/kaldi/dev-clean-hires-norm.blogmel --save_dir librispeech_data/preprocessed/dev-clean-hires-norm-blogmel
```
Once the program is done, you will see a directory `preprocessed/` inside `librispeech_data/` that contains all the preprocessed PyTorch tensors.

To train an APC model, simply run:
```bash
python train_apc.py
```
By default, the trained models will be put in `logs/`. You can also use Tensorboard to trace the training progress. There are many other configurations you can try, check `train_apc.py` for more details--it is highly documented and should be self-explanatory.

## Feature extraction
Once you have trained your APC model, you can use it to extract speech features from your target dataset. To do so, feed-forward the trained model on the target dataset and retrieve the extracted features by running:
```bash
_, feats = model.forward(inputs, lengths)
```
`feats` is a PyTorch tensor of shape (`num_layers`, `batch_size`, `seq_len`, `rnn_hidden_size`) where:
- `num_layers` is the RNN depth of your APC model
- `batch_size` is your inference batch size
- `seq_len` is the maximum sequence length and is determined when you run `prepare_data.py`. By default this value is 1600.
- `rnn_hidden_size` is the dimensionality of the RNN hidden unit.

As you can see, `feats` is essentially the RNN hidden states in an APC model. You can think of APC as a speech version of [ELMo](https://www.aclweb.org/anthology/N18-1202) if you are familiar with it.

There are many ways to incorporate `feats` into your downstream task. One of the easiest way is to take only the outputs of the last RNN layer (i.e., `feats[-1, :, :, :]`) as the input features to your downstream model, which is what we did in our paper. Feel free to explore other mechanisms.

## Pre-trained models
We release the pre-trained models that were used to produce the numbers reported in the paper. `load_pretrained_model.py` provides a simple example of loading a pre-trained model.
* [n = 1](https://www.dropbox.com/s/qyb1gicjkhv0wz9/bs32-rhl3-rhs512-rd0-adam-res-ts1.pt?dl=0)
* [n = 2](https://www.dropbox.com/s/76amvx3fccfmp2n/bs32-rhl3-rhs512-rd0-adam-res-ts2.pt?dl=0)
* [n = 3](https://www.dropbox.com/s/9nwj8y0djiw9pek/bs32-rhl3-rhs512-rd0-adam-res-ts3.pt?dl=0)
* [n = 5](https://www.dropbox.com/s/8pqlr5wg89eicwk/bs32-rhl3-rhs512-rd0-adam-res-ts5.pt?dl=0)
* [n = 10](https://www.dropbox.com/s/ucpf66k89xkm1jw/bs32-rhl3-rhs512-rd0-adam-res-ts10.pt?dl=0)
* [n = 20](https://www.dropbox.com/s/wa01myucfifloqo/bs32-rhl3-rhs512-rd0-adam-res-ts20.pt?dl=0)

## Reference
Please cite our paper(s) if you find this repository useful. This first paper proposes the APC objective, while the second paper applies it to speech recognition, speech translation, and speaker identification, and provides more systematic analysis on the learned representations. Cite both if you are kind enough!
```
@inproceedings{chung2019unsupervised,
  title = {An unsupervised autoregressive model for speech representation learning},
  author = {Chung, Yu-An and Hsu, Wei-Ning and Tang, Hao and Glass, James},
  booktitle = {Interspeech},
  year = {2019}
}
```
```
@inproceedings{chung2020generative,
  title = {Generative pre-training for speech with autoregressive predictive coding},
  author = {Chung, Yu-An and Glass, James},
  booktitle = {ICASSP},
  year = {2020}
}
```

## Contact
Feel free to shoot me an <a href="mailto:andyyuan@mit.edu">email</a> for any inquiries about the paper and this repository.


================================================
FILE: apc_model.py
================================================
import torch
from torch import nn
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence


class Prenet(nn.Module):
  """Prenet is a multi-layer fully-connected network with ReLU activations.
  During training and testing (i.e., feature extraction), each input frame is
  passed into the Prenet, and the Prenet output is then fed to the RNN. If
  Prenet configuration is None, the input frames will be directly fed to the
  RNN without any transformation.
  """

  def __init__(self, input_size, num_layers, hidden_size, dropout):
    super(Prenet, self).__init__()
    input_sizes = [input_size] + [hidden_size] * (num_layers - 1)
    output_sizes = [hidden_size] * num_layers

    self.layers = nn.ModuleList(
      [nn.Linear(in_features=in_size, out_features=out_size)
      for (in_size, out_size) in zip(input_sizes, output_sizes)])

    self.relu = nn.ReLU()
    self.dropout = nn.Dropout(dropout)


  def forward(self, inputs):
    # inputs: (batch_size, seq_len, mel_dim)
    for layer in self.layers:
      inputs = self.dropout(self.relu(layer(inputs)))

    return inputs
    # inputs: (batch_size, seq_len, out_dim)


class Postnet(nn.Module):
  """Postnet is a simple linear layer for predicting the target frames given
  the RNN context during training. We don't need the Postnet for feature
  extraction.
  """

  def __init__(self, input_size, output_size=80):
    super(Postnet, self).__init__()
    self.layer = nn.Conv1d(in_channels=input_size, out_channels=output_size,
                           kernel_size=1, stride=1)


  def forward(self, inputs):
    # inputs: (batch_size, seq_len, hidden_size)
    inputs = torch.transpose(inputs, 1, 2)
    # inputs: (batch_size, hidden_size, seq_len) -- for conv1d operation

    return torch.transpose(self.layer(inputs), 1, 2)
    # (batch_size, seq_len, output_size) -- back to the original shape


class APCModel(nn.Module):
  """This class defines Autoregressive Predictive Coding (APC), a model that
  learns to extract general speech features from unlabeled speech data. These
  features are shown to contain rich speaker and phone information, and are
  useful for a wide range of downstream tasks such as speaker verification
  and phone classification.

  An APC model consists of a Prenet (optional), a multi-layer GRU network,
  and a Postnet. For each time step during training, the Prenet transforms
  the input frame into a latent representation, which is then consumed by
  the GRU network for generating internal representations across the layers.
  Finally, the Postnet takes the output of the last GRU layer and attempts to
  predict the target frame.

  After training, to extract features from the data of your interest, which
  do not have to be i.i.d. with the training data, simply feed-forward the
  the data through the APC model, and take the the internal representations
  (i.e., the GRU hidden states) as the extracted features and use them in
  your tasks.
  """

  def __init__(self, mel_dim, prenet_config, rnn_config):
    super(APCModel, self).__init__()
    self.mel_dim = mel_dim

    if prenet_config is not None:
      # Make sure the dimensionalities are correct
      assert prenet_config.input_size == mel_dim
      assert prenet_config.hidden_size == rnn_config.input_size
      assert rnn_config.input_size == rnn_config.hidden_size
      self.prenet = Prenet(
        input_size=prenet_config.input_size,
        num_layers=prenet_config.num_layers,
        hidden_size=prenet_config.hidden_size,
        dropout=prenet_config.dropout)
    else:
      assert rnn_config.input_size == mel_dim
      self.prenet = None

    in_sizes = ([rnn_config.input_size] +
                [rnn_config.hidden_size] * (rnn_config.num_layers - 1))
    out_sizes = [rnn_config.hidden_size] * rnn_config.num_layers
    self.rnns = nn.ModuleList(
      [nn.GRU(input_size=in_size, hidden_size=out_size, batch_first=True)
      for (in_size, out_size) in zip(in_sizes, out_sizes)])

    self.rnn_dropout = nn.Dropout(rnn_config.dropout)
    self.rnn_residual = rnn_config.residual

    self.postnet = Postnet(
      input_size=rnn_config.hidden_size,
      output_size=self.mel_dim)


  def forward(self, inputs, lengths):
    """Forward function for both training and testing (feature extraction).

    input:
      inputs: (batch_size, seq_len, mel_dim)
      lengths: (batch_size,)

    return:
      predicted_mel: (batch_size, seq_len, mel_dim)
      internal_reps: (num_layers + x, batch_size, seq_len, rnn_hidden_size),
        where x is 1 if there's a prenet, otherwise 0
    """
    seq_len = inputs.size(1)

    if self.prenet is not None:
      rnn_inputs = self.prenet(inputs)
      # rnn_inputs: (batch_size, seq_len, rnn_input_size)
      internal_reps = [rnn_inputs]
      # also include prenet_outputs in internal_reps
    else:
      rnn_inputs = inputs
      internal_reps = []

    packed_rnn_inputs = pack_padded_sequence(rnn_inputs, lengths, True)

    for i, layer in enumerate(self.rnns):
      packed_rnn_outputs, _ = layer(packed_rnn_inputs)

      rnn_outputs, _ = pad_packed_sequence(
        packed_rnn_outputs, True, total_length=seq_len)
      # outputs: (batch_size, seq_len, rnn_hidden_size)

      if i + 1 < len(self.rnns):
        # apply dropout except the last rnn layer
        rnn_outputs = self.rnn_dropout(rnn_outputs)

      rnn_inputs, _ = pad_packed_sequence(
        packed_rnn_inputs, True, total_length=seq_len)
      # rnn_inputs: (batch_size, seq_len, rnn_hidden_size)

      if self.rnn_residual and rnn_inputs.size(-1) == rnn_outputs.size(-1):
        # Residual connections
        rnn_outputs = rnn_outputs + rnn_inputs

      internal_reps.append(rnn_outputs)

      packed_rnn_inputs = pack_padded_sequence(rnn_outputs, lengths, True)

    predicted_mel = self.postnet(rnn_outputs)
    # predicted_mel: (batch_size, seq_len, mel_dim)

    internal_reps = torch.stack(internal_reps)

    return predicted_mel, internal_reps
    # predicted_mel is only for training; internal_reps is the extracted
    # features


================================================
FILE: datasets.py
================================================
from os import listdir
from os.path import join
import pickle

import torch
from torch.utils import data


class LibriSpeech(data.Dataset):
  def __init__(self, path):
    self.path = path
    self.ids = [f for f in listdir(self.path) if f.endswith('.pt')]
    with open(join(path, 'lengths.pkl'), 'rb') as f:
      self.lengths = pickle.load(f)

  def __len__(self):
    return len(self.ids)

  def __getitem__(self, index):
    x = torch.load(join(self.path, self.ids[index]))
    l = self.lengths[self.ids[index]]
    return x, l


================================================
FILE: load_pretrained_model.py
================================================
"""Example of loading a pre-trained APC model."""

import torch

from apc_model import APCModel
from utils import PrenetConfig, RNNConfig


def main():
  prenet_config = None
  rnn_config = RNNConfig(input_size=80, hidden_size=512, num_layers=3,
                         dropout=0.)
  pretrained_apc = APCModel(mel_dim=80, prenet_config=prenet_config,
                            rnn_config=rnn_config).cuda()

  pretrained_weights_path = 'bs32-rhl3-rhs512-rd0-adam-res-ts3.pt'
  pretrained_apc.load_state_dict(torch.load(pretrained_weights_path))

  # Load data and perform your task ...


================================================
FILE: prepare_data.py
================================================
import os
import argparse
import pickle

import torch
import torch.nn.functional as F


def main():
  parser = argparse.ArgumentParser("Configuration for data preparation")
  parser.add_argument("--librispeech_from_kaldi", default="./librispeech_data/kaldi/dev-clean-hires-norm.blogmel", type=str,
    help="Path to the librispeech log Mel features generated by the Kaldi scripts")
  parser.add_argument("--max_seq_len", default=1600, type=int,
    help="The maximum length (number of frames) of each sequence; sequences will be truncated or padded (with zero vectors)
          to this length")
  parser.add_argument("--save_dir", default="./librispeech_data/preprocessed/dev-clean", type=str,
    help="Directory to save the preprocessed pytorch tensors")
  config = parser.parse_args()

  os.makedirs(config.save_dir, exist_ok=True)

  id2len = {}
  with open(config.librispeech_from_kaldi, 'r') as f:
    # process the file line by line
    for line in f:
      data = line.strip().split()

      if len(data) == 1:
        if data[0] == '.':  # end of the current utterance
          id2len[utt_id + '.pt'] = min(len(log_mel), config.max_seq_len)
          log_mel = torch.FloatTensor(log_mel)  # convert the 2D list to a pytorch tensor
          log_mel = F.pad(log_mel, (0, 0, 0, config.max_seq_len - log_mel.size(0))) # pad or truncate
          torch.save(log_mel, os.path.join(config.save_dir, utt_id + '.pt'))

        else: # here starts a new utterance
          utt_id = data[0]
          log_mel = []

      else:
        log_mel.append([float(i) for i in data])

  with open(os.path.join(config.save_dir, 'lengths.pkl'), 'wb') as f:
    pickle.dump(id2len, f, protocol=4)


if __name__ == '__main__':
  main()


================================================
FILE: train_apc.py
================================================
import os
import logging
import argparse

import numpy as np
import torch
from torch.autograd import Variable
from torch import nn, optim
from torch.utils import data
import tensorboard_logger
from tensorboard_logger import log_value

from apc_model import APCModel
from datasets import LibriSpeech
from utils import PrenetConfig, RNNConfig



def main():
  parser = argparse.ArgumentParser(
    description="Configuration for training an APC model")

  # Prenet architecture (note that all APC models in the paper DO NOT
  # incoporate a prenet)
  parser.add_argument("--prenet_num_layers", default=0, type=int,
    help="Number of ReLU layers as prenet")
  parser.add_argument("--prenet_dropout", default=0., type=float,
    help="Dropout for prenet")

  # RNN architecture
  parser.add_argument("--rnn_num_layers", default=3, type=int,
    help="Number of RNN layers in the APC model")
  parser.add_argument("--rnn_hidden_size", default=512, type=int,
    help="Number of hidden units in each RNN layer")
  parser.add_argument("--rnn_dropout", default=0., type=float,
    help="Dropout for each RNN output layer except the last one")
  parser.add_argument("--rnn_residual", action="store_true",
    help="Apply residual connections between RNN layers if specified")

  # Training configuration
  parser.add_argument("--optimizer", default="adam", type=str,
    help="The gradient descent optimizer (e.g., sgd, adam, etc.)")
  parser.add_argument("--batch_size", default=32, type=int,
    help="Training minibatch size")
  parser.add_argument("--learning_rate", default=0.0001, type=float,
    help="Initial learning rate")
  parser.add_argument("--epochs", default=100, type=int,
    help="Number of training epochs")
  parser.add_argument("--time_shift", default=1, type=int,
    help="Given f_{t}, predict f_{t + n}, where n is the time_shift")
  parser.add_argument("--clip_thresh", default=1.0, type=float,
    help="Threshold for clipping the gradients")

  # Misc configurations
  parser.add_argument("--feature_dim", default=80, type=int,
    help="The dimension of the input frame")
  parser.add_argument("--load_data_workers", default=2, type=int,
    help="Number of parallel data loaders")
  parser.add_argument("--experiment_name", default="foo", type=str,
    help="Name of this experiment")
  parser.add_argument("--store_path", default="./logs", type=str,
    help="Where to save the trained models and logs")
  parser.add_argument("--librispeech_path",
    default="./librispeech_data/preprocessed", type=str,
    help="Path to the librispeech directory")

  config = parser.parse_args()

  model_dir = os.path.join(config.store_path, config.experiment_name + '.dir')
  os.makedirs(config.store_path, exist_ok=True)
  os.makedirs(model_dir, exist_ok=True)

  logging.basicConfig(
    level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s',
    filename=os.path.join(model_dir, config.experiment_name),
    filemode='w')

  # define a new Handler to log to console as well
  console = logging.StreamHandler()
  console.setLevel(logging.INFO)
  formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
  console.setFormatter(formatter)
  logging.getLogger('').addHandler(console)

  logging.info('Model Parameters: ')
  logging.info('Prenet Depth: %d' % (config.prenet_num_layers))
  logging.info('Prenet Dropout: %f' % (config.prenet_dropout))
  logging.info('RNN Depth: %d ' % (config.rnn_num_layers))
  logging.info('RNN Hidden Dim: %d' % (config.rnn_hidden_size))
  logging.info('RNN Residual Connections: %s' % (config.rnn_residual))
  logging.info('RNN Dropout: %f' % (config.rnn_dropout))
  logging.info('Optimizer: %s ' % (config.optimizer))
  logging.info('Batch Size: %d ' % (config.batch_size))
  logging.info('Initial Learning Rate: %f ' % (config.learning_rate))
  logging.info('Time Shift: %d' % (config.time_shift))
  logging.info('Gradient Clip Threshold: %f' % (config.clip_thresh))

  if config.prenet_num_layers == 0:
    prenet_config = None
    rnn_config = RNNConfig(
      config.feature_dim, config.rnn_hidden_size, config.rnn_num_layers,
      config.rnn_dropout, config.rnn_residual)
  else:
    prenet_config = PrenetConfig(
      config.feature_dim, config.rnn_hidden_size, config.prenet_num_layers,
      config.prenet_dropout)
    rnn_config = RNNConfig(
      config.rnn_hidden_size, config.rnn_hidden_size, config.rnn_num_layers,
      config.rnn_dropout, config.rnn_residual)

  model = APCModel(
    mel_dim=config.feature_dim,
    prenet_config=prenet_config,
    rnn_config=rnn_config).cuda()

  criterion = nn.L1Loss()

  if config.optimizer == 'adam':
    optimizer = optim.Adam(model.parameters(), lr=config.learning_rate)
  elif config.optimizer == 'adadelta':
    optimizer = optim.Adadelta(model.parameters())
  elif config.optimizer == 'sgd':
    optimizer = optim.SGD(model.parameters(), lr=config.learning_rate)
  elif config.optimizer == 'adagrad':
    optimizer = optim.Adagrad(model.parameters(), lr=config.learning_rate)
  elif config.optimizer == 'rmsprop':
    optimizer = optim.RMSprop(model.parameters(), lr=config.learning_rate)
  else:
    raise NotImplementedError("Learning method not supported for the task")

  # setup tensorboard logger
  tensorboard_logger.configure(
    os.path.join(model_dir, config.experiment_name + '.tb_log'))

  train_set = LibriSpeech(os.path.join(
    config.librispeech_path, 'train-clean-360'))
  train_data_loader = data.DataLoader(
    train_set, batch_size=config.batch_size,
    num_workers=config.load_data_workers, shuffle=True)

  val_set = LibriSpeech(os.path.join(config.librispeech_path, 'dev-clean'))
  val_data_loader = data.DataLoader(
    val_set, batch_size=config.batch_size,
    num_workers=config.load_data_workers, shuffle=False)

  torch.save(model.state_dict(),
    open(os.path.join(model_dir, config.experiment_name + '__epoch_0.model'),
    'wb'))

  global_step = 0
  for epoch_i in range(config.epochs):

    ####################
    ##### Training #####
    ####################

    model.train()
    train_losses = []
    for batch_x, batch_l in train_data_loader:

      _, indices = torch.sort(batch_l, descending=True)

      batch_x = Variable(batch_x[indices]).cuda()
      batch_l = Variable(batch_l[indices]).cuda()

      outputs, _ = model(
        batch_x[:, :-config.time_shift, :], batch_l - config.time_shift)

      optimizer.zero_grad()
      loss = criterion(outputs, batch_x[:, config.time_shift:, :])
      train_losses.append(loss.item())
      loss.backward()
      grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
                                                 config.clip_thresh)
      optimizer.step()

      log_value("training loss (step-wise)", float(loss.item()), global_step)
      log_value("gradient norm", grad_norm, global_step)

      global_step += 1

    ######################
    ##### Validation #####
    ######################

    model.eval()
    val_losses = []
    with torch.set_grad_enabled(False):
      for val_batch_x, val_batch_l in val_data_loader:
        _, val_indices = torch.sort(val_batch_l, descending=True)

        val_batch_x = Variable(val_batch_x[val_indices]).cuda()
        val_batch_l = Variable(val_batch_l[val_indices]).cuda()

        val_outputs, _ = model(
          val_batch_x[:, :-config.time_shift, :],
          val_batch_l - config.time_shift)

        val_loss = criterion(val_outputs,
                             val_batch_x[:, config.time_shift:, :])
        val_losses.append(val_loss.item())

    logging.info('Epoch: %d Training Loss: %.5f Validation Loss: %.5f' % (epoch_i + 1, np.mean(train_losses), np.mean(val_losses)))

    log_value("training loss (epoch-wise)", np.mean(train_losses), epoch_i)
    log_value("validation loss (epoch-wise)", np.mean(val_losses), epoch_i)

    torch.save(model.state_dict(),
      open(os.path.join(model_dir, config.experiment_name + '__epoch_%d' % (epoch_i + 1) + '.model'), 'wb'))


if __name__ == '__main__':
  main()


================================================
FILE: utils.py
================================================
from collections import namedtuple


PrenetConfig = namedtuple(
  'PrenetConfig', ['input_size', 'hidden_size', 'num_layers', 'dropout'])

RNNConfig = namedtuple(
  'RNNConfig',
  ['input_size', 'hidden_size', 'num_layers', 'dropout', 'residual'])