Table of Contents
Shortcuts

Conversational AI

These are amazing ecosystems to help with Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text to speech (TTS).

NeMo

NVIDIA NeMo is a toolkit for building new State-of-the-Art Conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new Conversational AI model architectures.

Conversational AI architectures are typically very large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training.

Note

Every NeMo model is a LightningModule that comes equipped with all supporting infrastructure for training and reproducibility.

NeMo Models

NeMo Models contain everything needed to train and reproduce state of the art Conversational AI research and applications, including:

  • neural network architectures

  • datasets/data loaders

  • data preprocessing/postprocessing

  • data augmentors

  • optimizers and schedulers

  • tokenizers

  • language models

NeMo uses Hydra for configuring both NeMo models and the PyTorch Lightning Trainer. Depending on the domain and application, many different AI libraries will have to be configured to build the application. Hydra makes it easy to bring all of these libraries together so that each can be configured from .yaml or the Hydra CLI.

Note

Every NeMo model has an example configuration file and a corresponding script that contains all configurations needed for training.

The end result of using NeMo, Pytorch Lightning, and Hydra is that NeMo models all have the same look and feel. This makes it easy to do Conversational AI research across multiple domains. NeMo models are also fully compatible with the PyTorch ecosystem.

Installing NeMo

Before installing NeMo, please install Cython first.

pip install Cython

For ASR and TTS models, also install these linux utilities.

apt-get update && apt-get install -y libsndfile1 ffmpeg

Then installing the latest NeMo release is a simple pip install.

pip install nemo_toolkit[all]==1.0.0b1

To install the main branch from GitHub:

python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]

To install from a local clone of NeMo:

./reinstall.sh # from cloned NeMo's git root

For Docker users, the NeMo container is available on NGC.

docker pull nvcr.io/nvidia/nemo:v1.0.0b1

docker run --runtime=nvidia -it --rm -v --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:v1.0.0b1

Experiment Manager

NeMo’s Experiment Manager leverages PyTorch Lightning for model checkpointing, TensorBoard Logging, and Weights and Biases logging. The Experiment Manager is included by default in all NeMo example scripts.

exp_manager(trainer, cfg.get("exp_manager", None))

And is configurable via .yaml with Hydra.

exp_manager:
    exp_dir: null
    name: *name
    create_tensorboard_logger: True
    create_checkpoint_callback: True

Optionally launch Tensorboard to view training results in ./nemo_experiments (by default).

tensorboard --bind_all --logdir nemo_experiments

Automatic Speech Recognition (ASR)

Everything needed to train Convolutional ASR models is included with NeMo. NeMo supports multiple Speech Recognition architectures, including Jasper and QuartzNet. NeMo Speech Models can be trained from scratch on custom datasets or fine-tuned using pre-trained checkpoints trained on thousands of hours of audio that can be restored for immediate use.

Some typical ASR tasks are included with NeMo:

See this asr notebook for a full tutorial on doing ASR with NeMo, PyTorch Lightning, and Hydra.

Specify ASR Model Configurations with YAML File

NeMo Models and the PyTorch Lightning Trainer can be fully configured from .yaml files using Hydra.

See this asr config for the entire speech to text .yaml file.

# configure the PyTorch Lightning Trainer
trainer:
    gpus: 0 # number of gpus
    max_epochs: 5
    max_steps: null # computed at runtime if not set
    num_nodes: 1
    accelerator: ddp
    ...
# configure the ASR model
model:
    ...
    encoder:
        cls: nemo.collections.asr.modules.ConvASREncoder
        params:
            feat_in: *n_mels
            activation: relu
            conv_mask: true

        jasper:
            - filters: 128
            repeat: 1
            kernel: [11]
            stride: [1]
            dilation: [1]
            dropout: *dropout
            ...
    # all other configuration, data, optimizer, preprocessor, etc
    ...

Developing ASR Model From Scratch

speech_to_text.py

# hydra_runner calls hydra.main and is useful for multi-node experiments
@hydra_runner(config_path="conf", config_name="config")
def main(cfg):
    trainer = Trainer(**cfg.trainer)
    asr_model = EncDecCTCModel(cfg.model, trainer)
    trainer.fit(asr_model)

Hydra makes every aspect of the NeMo model, including the PyTorch Lightning Trainer, customizable from the command line.

python NeMo/examples/asr/speech_to_text.py --config-name=quartznet_15x5 \
    trainer.gpus=4 \
    trainer.max_epochs=128 \
    +trainer.precision=16 \
    model.train_ds.manifest_filepath=<PATH_TO_DATA>/librispeech-train-all.json \
    model.validation_ds.manifest_filepath=<PATH_TO_DATA>/librispeech-dev-other.json \
    model.train_ds.batch_size=64 \
    +model.validation_ds.num_workers=16 \
    +model.train_ds.num_workers=16

Note

Training NeMo ASR models can take days/weeks so it is highly recommended to use multiple GPUs and multiple nodes with the PyTorch Lightning Trainer.

Using State-Of-The-Art Pre-trained ASR Model

Transcribe audio with QuartzNet model pretrained on ~3300 hours of audio.

quartznet = EncDecCTCModel.from_pretrained("QuartzNet15x5Base-En")

files = ["path/to/my.wav"]  # file duration should be less than 25 seconds

for fname, transcription in zip(files, quartznet.transcribe(paths2audio_files=files)):
    print(f"Audio in {fname} was recognized as: {transcription}")

To see the available pretrained checkpoints:

EncDecCTCModel.list_available_models()

NeMo ASR Model Under the Hood

Any aspect of ASR training or model architecture design can easily be customized with PyTorch Lightning since every NeMo model is a Lightning Module.

class EncDecCTCModel(ASRModel):
    """Base class for encoder decoder CTC-based models."""

    ...

    def forward(self, input_signal, input_signal_length):
        processed_signal, processed_signal_len = self.preprocessor(
            input_signal=input_signal,
            length=input_signal_length,
        )
        # Spec augment is not applied during evaluation/testing
        if self.spec_augmentation is not None and self.training:
            processed_signal = self.spec_augmentation(input_spec=processed_signal)
        encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_len)
        log_probs = self.decoder(encoder_output=encoded)
        greedy_predictions = log_probs.argmax(dim=-1, keepdim=False)
        return log_probs, encoded_len, greedy_predictions

    # PTL-specific methods
    def training_step(self, batch, batch_nb):
        audio_signal, audio_signal_len, transcript, transcript_len = batch
        log_probs, encoded_len, predictions = self.forward(
            input_signal=audio_signal, input_signal_length=audio_signal_len
        )
        loss_value = self.loss(
            log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len
        )
        wer_num, wer_denom = self._wer(predictions, transcript, transcript_len)
        self.log_dict(
            {
                "train_loss": loss_value,
                "training_batch_wer": wer_num / wer_denom,
                "learning_rate": self._optimizer.param_groups[0]["lr"],
            }
        )
        return loss_value

Neural Types in NeMo ASR

NeMo Models and Neural Modules come with Neural Type checking. Neural type checking is extremely useful when combining many different neural network architectures for a production-grade application.

@property
def input_types(self) -> Optional[Dict[str, NeuralType]]:
    if hasattr(self.preprocessor, "_sample_rate"):
        audio_eltype = AudioSignal(freq=self.preprocessor._sample_rate)
    else:
        audio_eltype = AudioSignal()
    return {
        "input_signal": NeuralType(("B", "T"), audio_eltype),
        "input_signal_length": NeuralType(tuple("B"), LengthsType()),
    }


@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
    return {
        "outputs": NeuralType(("B", "T", "D"), LogprobsType()),
        "encoded_lengths": NeuralType(tuple("B"), LengthsType()),
        "greedy_predictions": NeuralType(("B", "T"), LabelsType()),
    }

Natural Language Processing (NLP)

Everything needed to finetune BERT-like language models for NLP tasks is included with NeMo. NeMo NLP Models include HuggingFace Transformers and NVIDIA Megatron-LM BERT and Bio-Megatron models. NeMo can also be used for pretraining BERT-based language models from HuggingFace.

Any of the HuggingFace encoders or Megatron-LM encoders can easily be used for the NLP tasks that are included with NeMo:

Named Entity Recognition (NER)

NER (or more generally token classification) is the NLP task of detecting and classifying key information (entities) in text. This task is very popular in Healthcare and Finance. In finance, for example, it can be important to identify geographical, geopolitical, organizational, persons, events, and natural phenomenon entities. See this NER notebook for a full tutorial on doing NER with NeMo, PyTorch Lightning, and Hydra.

Specify NER Model Configurations with YAML File

Note

NeMo Models and the PyTorch Lightning Trainer can be fully configured from .yaml files using Hydra.

See this token classification config for the entire NER (token classification) .yaml file.

# configure any argument of the PyTorch Lightning Trainer
trainer:
    gpus: 1 # the number of gpus, 0 for CPU
    num_nodes: 1
    max_epochs: 5
    ...
# configure any aspect of the token classification model here
model:
    dataset:
        data_dir: ??? # /path/to/data
        class_balancing: null # choose from [null, weighted_loss]. Weighted_loss enables the weighted class balancing of the loss, may be used for handling unbalanced classes
        max_seq_length: 128
        ...
  tokenizer:
    tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece
    vocab_file: null # path to vocab file
    ...
# the language model can be from HuggingFace or Megatron-LM
language_model:
    pretrained_model_name: bert-base-uncased
    lm_checkpoint: null
    ...
# the classifier for the downstream task
  head:
    num_fc_layers: 2
    fc_dropout: 0.5
    activation: 'relu'
    ...
# all other configuration: train/val/test/ data, optimizer, experiment manager, etc
...

Developing NER Model From Scratch

token_classification.py

# hydra_runner calls hydra.main and is useful for multi-node experiments
@hydra_runner(config_path="conf", config_name="token_classification_config")
def main(cfg: DictConfig) -> None:
    trainer = pl.Trainer(**cfg.trainer)
    model = TokenClassificationModel(cfg.model, trainer=trainer)
    trainer.fit(model)

After training, we can do inference with the saved NER model using PyTorch Lightning.

Inference from file:

gpu = 1 if cfg.trainer.gpus != 0 else 0
trainer = pl.Trainer(gpus=gpu)
model.set_trainer(trainer)
model.evaluate_from_file(
    text_file=os.path.join(cfg.model.dataset.data_dir, cfg.model.validation_ds.text_file),
    labels_file=os.path.join(cfg.model.dataset.data_dir, cfg.model.validation_ds.labels_file),
    output_dir=exp_dir,
    add_confusion_matrix=True,
    normalize_confusion_matrix=True,
)

Or we can run inference on a few examples:

queries = ["we bought four shirts from the nvidia gear store in santa clara.", "Nvidia is a company in Santa Clara."]
results = model.add_predictions(queries)

for query, result in zip(queries, results):
    logging.info(f"Query : {query}")
    logging.info(f"Result: {result.strip()}\n")

Hydra makes every aspect of the NeMo model, including the PyTorch Lightning Trainer, customizable from the command line.

python token_classification.py \
    model.language_model.pretrained_model_name=bert-base-cased \
    model.head.num_fc_layers=2 \
    model.dataset.data_dir=/path/to/my/data  \
    trainer.max_epochs=5 \
    trainer.gpus=[0,1]

Tokenizers

Tokenization is the process of converting natural language text into integer arrays which can be used for machine learning. For NLP tasks, tokenization is an essential part of data preprocessing. NeMo supports all BERT-like model tokenizers from HuggingFace’s AutoTokenizer and also supports Google’s SentencePieceTokenizer which can be trained on custom data.

To see the list of supported tokenizers:

from nemo.collections import nlp as nemo_nlp

nemo_nlp.modules.get_tokenizer_list()

See this tokenizer notebook for a full tutorial on using tokenizers in NeMo.

Language Models

Language models are used to extract information from (tokenized) text. Much of the state-of-the-art in natural language processing is achieved by fine-tuning pretrained language models on the downstream task.

With NeMo, you can either pretrain a BERT model on your data or use a pretrained language model from HuggingFace Transformers or NVIDIA Megatron-LM.

To see the list of language models available in NeMo:

nemo_nlp.modules.get_pretrained_lm_models_list(include_external=True)

Easily switch between any language model in the above list by using .get_lm_model.

nemo_nlp.modules.get_lm_model(pretrained_model_name="distilbert-base-uncased")

See this language model notebook for a full tutorial on using pretrained language models in NeMo.

Using a Pre-trained NER Model

NeMo has pre-trained NER models that can be used to get started with Token Classification right away. Models are automatically downloaded from NGC, cached locally to disk, and loaded into GPU memory using the .from_pretrained method.

# load pre-trained NER model
pretrained_ner_model = TokenClassificationModel.from_pretrained(model_name="NERModel")

# define the list of queries for inference
queries = [
    "we bought four shirts from the nvidia gear store in santa clara.",
    "Nvidia is a company.",
    "The Adventures of Tom Sawyer by Mark Twain is an 1876 novel about a young boy growing "
    + "up along the Mississippi River.",
]
results = pretrained_ner_model.add_predictions(queries)

for query, result in zip(queries, results):
    print()
    print(f"Query : {query}")
    print(f"Result: {result.strip()}\n")

NeMo NER Model Under the Hood

Any aspect of NLP training or model architecture design can easily be customized with PyTorch Lightning since every NeMo model is a Lightning Module.

class TokenClassificationModel(ModelPT):
    """
    Token Classification Model with BERT, applicable for tasks such as Named Entity Recognition
    """

    ...

    def forward(self, input_ids, token_type_ids, attention_mask):
        hidden_states = self.bert_model(
            input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask
        )
        logits = self.classifier(hidden_states=hidden_states)
        return logits

    # PTL-specfic methods
    def training_step(self, batch, batch_idx):
        """
        Lightning calls this inside the training loop with the data from the training dataloader
        passed in as `batch`.
        """
        input_ids, input_type_ids, input_mask, subtokens_mask, loss_mask, labels = batch
        logits = self(input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask)

        loss = self.loss(logits=logits, labels=labels, loss_mask=loss_mask)
        self.log_dict({"train_loss": loss, "lr": self._optimizer.param_groups[0]["lr"]})
        return loss

    ...

Neural Types in NeMo NLP

NeMo Models and Neural Modules come with Neural Type checking. Neural type checking is extremely useful when combining many different neural network architectures for a production-grade application.

@property
def input_types(self) -> Optional[Dict[str, NeuralType]]:
    return self.bert_model.input_types


@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
    return self.classifier.output_types

Text-To-Speech (TTS)

Everything needed to train TTS models and generate audio is included with NeMo. NeMo TTS Models can be trained from scratch on your own data or pretrained models can be downloaded automatically. NeMo currently supports a two step inference procedure. First, a model is used to generate a mel spectrogram from text. Second, a model is used to generate audio from a mel spectrogram.

Mel Spectrogram Generators:

Audio Generators:

Specify TTS Model Configurations with YAML File

Note

NeMo Models and PyTorch Lightning Trainer can be fully configured from .yaml files using Hydra.

tts/conf/glow_tts.yaml

# configure the PyTorch Lightning Trainer
trainer:
    gpus: -1 # number of gpus
    max_epochs: 350
    num_nodes: 1
    accelerator: ddp
    ...

# configure the TTS model
model:
    ...
    encoder:
        cls: nemo.collections.tts.modules.glow_tts.TextEncoder
            params:
            n_vocab: 148
            out_channels: *n_mels
            hidden_channels: 192
            filter_channels: 768
            filter_channels_dp: 256
            ...
# all other configuration, data, optimizer, parser, preprocessor, etc
...

Developing TTS Model From Scratch

tts/glow_tts.py

# hydra_runner calls hydra.main and is useful for multi-node experiments
@hydra_runner(config_path="conf", config_name="glow_tts")
def main(cfg):
    trainer = pl.Trainer(**cfg.trainer)
    model = GlowTTSModel(cfg=cfg.model, trainer=trainer)
    trainer.fit(model)

Hydra makes every aspect of the NeMo model, including the PyTorch Lightning Trainer, customizable from the command line.

python NeMo/examples/tts/glow_tts.py \
    trainer.gpus=4 \
    trainer.max_epochs=400 \
    ...
    train_dataset=/path/to/train/data \
    validation_datasets=/path/to/val/data \
    model.train_ds.batch_size = 64 \

Note

Training NeMo TTS models from scratch can take days or weeks so it is highly recommended to use multiple GPUs and multiple nodes with the PyTorch Lightning Trainer.

Using State-Of-The-Art Pre-trained TTS Model

Generate speech using models trained on LJSpeech <https://keithito.com/LJ-Speech-Dataset/>, around 24 hours of single speaker data.

See this TTS notebook for a full tutorial on generating speech with NeMo, PyTorch Lightning, and Hydra.

# load pretrained spectrogram model
spec_gen = SpecModel.from_pretrained("GlowTTS-22050Hz").cuda()

# load pretrained Generators
vocoder = WaveGlowModel.from_pretrained("WaveGlow-22050Hz").cuda()


def infer(spec_gen_model, vocder_model, str_input):
    with torch.no_grad():
        parsed = spec_gen.parse(text_to_generate)
        spectrogram = spec_gen.generate_spectrogram(tokens=parsed)
        audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
    if isinstance(spectrogram, torch.Tensor):
        spectrogram = spectrogram.to("cpu").numpy()
    if len(spectrogram.shape) == 3:
        spectrogram = spectrogram[0]
    if isinstance(audio, torch.Tensor):
        audio = audio.to("cpu").numpy()
    return spectrogram, audio


text_to_generate = input("Input what you want the model to say: ")
spec, audio = infer(spec_gen, vocoder, text_to_generate)

To see the available pretrained checkpoints:

# spec generator
GlowTTSModel.list_available_models()

# vocoder
WaveGlowModel.list_available_models()

NeMo TTS Model Under the Hood

Any aspect of TTS training or model architecture design can easily be customized with PyTorch Lightning since every NeMo model is a LightningModule.

glow_tts.py

class GlowTTSModel(SpectrogramGenerator):
    """
    GlowTTS model used to generate spectrograms from text
    Consists of a text encoder and an invertible spectrogram decoder
    """

    ...
    # NeMo models come with neural type checking
    @typecheck(
        input_types={
            "x": NeuralType(("B", "T"), TokenIndex()),
            "x_lengths": NeuralType(("B"), LengthsType()),
            "y": NeuralType(("B", "D", "T"), MelSpectrogramType(), optional=True),
            "y_lengths": NeuralType(("B"), LengthsType(), optional=True),
            "gen": NeuralType(optional=True),
            "noise_scale": NeuralType(optional=True),
            "length_scale": NeuralType(optional=True),
        }
    )
    def forward(self, *, x, x_lengths, y=None, y_lengths=None, gen=False, noise_scale=0.3, length_scale=1.0):
        if gen:
            return self.glow_tts.generate_spect(
                text=x, text_lengths=x_lengths, noise_scale=noise_scale, length_scale=length_scale
            )
        else:
            return self.glow_tts(text=x, text_lengths=x_lengths, spect=y, spect_lengths=y_lengths)

    ...

    def step(self, y, y_lengths, x, x_lengths):
        z, y_m, y_logs, logdet, logw, logw_, y_lengths, attn = self(
            x=x, x_lengths=x_lengths, y=y, y_lengths=y_lengths, gen=False
        )

        l_mle, l_length, logdet = self.loss(
            z=z,
            y_m=y_m,
            y_logs=y_logs,
            logdet=logdet,
            logw=logw,
            logw_=logw_,
            x_lengths=x_lengths,
            y_lengths=y_lengths,
        )

        loss = sum([l_mle, l_length])

        return l_mle, l_length, logdet, loss, attn

    # PTL-specfic methods
    def training_step(self, batch, batch_idx):
        y, y_lengths, x, x_lengths = batch

        y, y_lengths = self.preprocessor(input_signal=y, length=y_lengths)

        l_mle, l_length, logdet, loss, _ = self.step(y, y_lengths, x, x_lengths)

        self.log_dict({"l_mle": l_mle, "l_length": l_length, "logdet": logdet}, prog_bar=True)
        return loss

    ...

Neural Types in NeMo TTS

NeMo Models and Neural Modules come with Neural Type checking. Neural type checking is extremely useful when combining many different neural network architectures for a production-grade application.

@typecheck(
    input_types={
        "x": NeuralType(("B", "T"), TokenIndex()),
        "x_lengths": NeuralType(("B"), LengthsType()),
        "y": NeuralType(("B", "D", "T"), MelSpectrogramType(), optional=True),
        "y_lengths": NeuralType(("B"), LengthsType(), optional=True),
        "gen": NeuralType(optional=True),
        "noise_scale": NeuralType(optional=True),
        "length_scale": NeuralType(optional=True),
    }
)
def forward(self, *, x, x_lengths, y=None, y_lengths=None, gen=False, noise_scale=0.3, length_scale=1.0):
    ...

Learn More

Note

NeMo tutorial notebooks can be run on Google Colab.

NVIDIA NeMo is actively being developed on GitHub. Contributions are welcome!

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. Read PyTorch Lightning's Privacy Policy.