ecog2txt

Code for decoding speech as text from neural data

This package contains Python code for the high-level aspects of decoding speech from neural data, including transfer learning across multiple subjects. It was used for all results in the paper "Machine translation of cortical activity to text with an encoder-decoder framework" (Makin et al., Nature Neuroscience, 2020). These high-level aspects include the structuring of the training, the organization by subjects, and the construction of TFRecords. The (low-level) training itself is done with the adjacent machine_learning package, which implements sequence-to-sequence networks in TensorFlow.

Installation

1. Install TensorFlow 1.15.5, the final version of TF1.x.

   pip install tensorflow-gpu==1.15.5
   

   If you don't have a GPU you should install the CPU version

   pip install tensorflow==1.15.5
   

   Please consult the Tensorflow installation documents. The most important facts to know are that TF1.15 requires CUDA 10.0, libcudnn7>=7.6.5.32-1+cuda10.0, and libnccl2>=2.6.4-1+cuda10.0. (I have only tested with up to, not beyond, the listed versions of these libraries). Make sure the driver for your GPU is compatible with these versions of the cudNN and NCCL libraries. And the latest version of Python supported by TF1.15 is 3.7.

2. Install the three required packages:

   git clone https://github.com/jgmakin/utils_jgm.git
   pip install -e utils_jgm
   
   git clone https://github.com/jgmakin/machine_learning.git
   pip install -e machine_learning
   
   git clone https://github.com/jgmakin/ecog2txt.git
   pip install -e ecog2txt
   
   

Note that utils_jgm requires the user to set up a configuration file; please see the README for that package.

Getting started

In order to unify the vast set of parameters (paths, experimental block structure, neural-network hyperparameters, etc.), all experiments are organized with the help of two configuration files, block_breakdowns.json, and YOUR_EXPERIMENT_manifest.yaml, examples of each are included in this repository.

1. Edit the block_breakdowns.json to match your use case. The entries are

   SUBJECT_ID: {BLOCK: {"type: BLOCK_TYPE, "default_dataset": DEFAULT_DATASET_VALUE}}

   where the DEFAULT_DATASET_VALUE is one of "training"/"validation"/"testing"; and the BLOCK_TYPE is whatever descriptive title you want to give to your block (e.g., "mocha-3"). Assigning types to the blocks allows them to be filtered out of datasets, according to information provided in the manifest (see next item). Place your edited copy into a directory we will call json_dir.

2. Edit one of the .yaml manifest files to something sensible for your case. The most important thing to know is that many of the classes in this package (and machine_learning) load their default attributes from this manifest. That means that, even though the keyword arguments for their constructors (__init__() methods) may appear to default to None, this None actually instructs the class to default to the argument's value in the manifest.

   You don't have to set all the values before your first run, but in the very least, you should:

   • Fix the paths/dirs. For the most part they are for writing, not reading, so you can set them wherever you like. For the three reading paths:
     • json_dir must point to the location of your block_breakdowns.json file (see previous item).
     • bad_electrodes_path must point to a (possibly empty) plain-text file listing (one entry per line) any bad channels. NB that these are assumed to be 1-indexed! (but will internally be converted to zero-indexing). Alternatively, you can provide (either via the manifest or as an argument to the ECoGDataGenerator) the good_electrodes directly.
     • electrode_path: you can ignore this unless you plan to plot results on the cortical surface (in which case contact me).
   • block_types: these set necessary conditions for membership in one of the datasets, training/validation/testing. For example, in the mochastar_word_sequence.yaml manifest file, the testing and validation sets are allowed to include only mocha-1, but the training set is allowed to include mocha-1, ..., mocha-9. So if a mocha-3 block has validation as its "default_dataset" in the block_breakdowns.json, it would be excluded altogether.
   • grid_size: Set this to match the dimensions of your ECoG grid.
   • text_sequence_vocab_file: You can provide a file with a list, one word per line, of all words to be targeted by the decoder. This key specifies just the name of the file; the file itself must live in the text_dir specified in __init__.py. If you set this key to None, the package will attempt to build a list of unique targets directly from the TFRecords. An example vocab_file, vocab.mocha-timit.1806, is included in this package.
   • data_mapping: Use this to set which data to use as inputs and outputs for the sequence-to-sequence network--see _ecog_token_generator below.
   • DataGenerator: In the manifest, this points to the ECoGDataGenerator in data_generators.py, but you will probably want to subclass this class and point to your new (sub)class instead--see next item.

   You can probably get away with leaving the rest of the values in the .yaml at their default values, at least for your first run.

   Finally, make sure YOUR_EXPERIMENT_manifest.yaml lives at the text_dir specified in __init__.py (you can change this as you like, but remember that the text_sequence_vocab_file must live in the same directory).

3. ECoGDataGenerator, found in data_generators.py, is a shell class for generating data--more particularly for writing out the TFRecords that will be used for training and assessing your model--that plays nicely with the other classes. However, three of its (required!) methods are unspecified because they depend on how you store your data. (Dummy versions appear in ECoGDataGenerator; you can inspect their input and outputs there.) You should subclass ECoGDataGenerator and fill in these methods:

   • _ecog_token_generator: a Python generator that yields data structures in the form of a dict, each entry of which corresponds to a set of inputs and outputs on a single trial. For example, the entries might be ecog_sequence,text_sequence, audio_sequence, and phoneme_sequence. The last two are not strictly necessary for speech decoding and can be left out--or you can add more. Just make sure that you return at least the data structures requested in the data_mapping specified in the manifest. So e.g. if the data_mapping is data_mapping = {'decoder_targets': 'text_sequence', 'encoder_inputs': 'ecog_sequence'} then _ecog_token_generator must yield dictionaries containing at least (but not limited to) a text_sequence and an ecog_sequence. The entire dictionary will be written to a TFRecord (one for each block), so it's better to yield more rather than fewer data structures, in case you change your mind later about the data_mapping but don't want to have to rewrite all the TFRecords.

     And one more thing: the text_sequence_vocab_file key in the experiment manifest is linked to the text_sequence in this data mapping. So if you plan to call your decoder_targets something else, say my_words, then make sure to rename the key in the experiment manifest that points to a vocab file to my_words_vocab_file.

   • _get_wav_data: should return the sampling_rate and audio signal for one (e.g.) block of audio data. This will allow you to make use of the built-in _get_MFCC_features in constructing your _ecog_token_generator. If you're never going to generate an audio_sequence, however, you can ignore it.

   • _query: should return the total number of examples in a group of blocks. This will allow you to allocate memory efficiently when using the get method. However, the methods _query and get are not used elsewhere in the code; they are convenience functions for examining the data directly rather than through a TFRecord.

Training a model

The basic commands to train a model are as follows (you can e.g. run this in a Python notebook).

import ecog2txt.trainers as e2t_trainers
import ecog2txt.data_generators

# CREATE A NEW MODEL
trainer = e2t_trainers.MultiSubjectTrainer(
    experiment_manifest_name=YOUR_EXPERIMENT_manifest.yaml,
    subject_ids=[400, 401],
    SN_kwargs={
        'FF_dropout': 0.4,          # overwriting whatever is in the manifest
        'TEMPORALLY_CONVOLVE': True # overwriting whatever is in the manifest
    },
    DG_kwargs={
        'REFERENCE_BIPOLAR': True,  # overwriting whatever is in the manifest
    },
    ES_kwargs = {
        'data_mapping': {           # overwriting whatever is in the manifest
            'encoder_inputs': 'ecog_sequence',
            'decoder_targets': 'text_sequence',
        },
    },
)

# MAKE SURE ALL THE TFRECORDS ARE WRITTEN
for subject in trainer.ecog_subjects:
    subject.write_tf_records_maybe()
trainer.subject_to_table()

# TRAIN THE TWO SUBJECTS IN PARALLEL
assessments = trainer.parallel_transfer_learn()