NeMo TTS Configuration Files#

This section describes the NeMo configuration file setup that is specific to models in the TTS collection. For general information about how to set up and run experiments that is common to all NeMo models (e.g. Experiment Manager and PyTorch Lightning trainer parameters), see the NeMo Models section.

The model section of the NeMo TTS configuration files generally requires information about the dataset(s) being used, the preprocessor for audio files, parameters for any augmentation being performed, as well as the model architecture specification. The sections on this page cover each of these in more detail.

Example configuration files for all of the NeMo TTS scripts can be found in the config directory of the examples.

Dataset Configuration#

Training, validation, and test parameters are specified using the model.train_ds, model.validation_ds, and model.test_ds sections in the configuration file, respectively. Depending on the task, there may be arguments specifying the sample rate of the audio files, supplementary data such as speech/text alignment priors and speaker IDs, etc., the threshold to trim leading and trailing silence from an audio signal, pitch normalization parameters, and so on. You may also decide to leave fields such as the manifest_filepath blank, to be specified via the command-line at runtime.

Any initialization parameter that is accepted for the class nemo.collections.tts.torch.data.TTSDataset can be set in the config file. Refer to the Dataset Processing Classes section of the API for a list of datasets classes and their respective parameters. An example TTS train and validation configuration should look similar to the following:

model:
  train_ds:
    dataset:
      _target_: nemo.collections.tts.torch.data.TTSDataset
      manifest_filepath: ???
      sample_rate: 44100
      sup_data_path: ???
      sup_data_types: ["align_prior_matrix", "pitch"]
      n_fft: 2048
      win_length: 2048
      hop_length: 512
      window: hann
      n_mels: 80
      lowfreq: 0
      highfreq: null
      max_duration: null
      min_duration: 0.1
      ignore_file: null
      trim: false
      pitch_fmin: 65.40639132514966
      pitch_fmax: 2093.004522404789
      pitch_norm: true
      pitch_mean: 212.35873413085938
      pitch_std: 68.52806091308594
      use_beta_binomial_interpolator: true

    dataloader_params:
      drop_last: false
      shuffle: true
      batch_size: 32
      num_workers: 12
      pin_memory: true

Audio Preprocessor Configuration#

If you are loading audio files for your experiment, you will likely want to use a preprocessor to convert from the raw audio signal to features (e.g. mel-spectrogram or MFCC). The preprocessor section of the config specifies the audio preprocessor to be used via the _target_ field, as well as any initialization parameters for that preprocessor. An example of specifying a preprocessor is as follows. Refer to the Audio Preprocessors API section for the preprocessor options, expected arguments, and defaults.

model:
  preprocessor:
    _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
    features: 80
    lowfreq: 0
    highfreq: null
    n_fft: 2048
    n_window_size: 2048
    window_size: false
    n_window_stride: 512
    window_stride: false
    pad_to: 1
    pad_value: 0
    sample_rate: 44100
    window: hann
    normalize: null
    preemph: null
    dither: 0.0
    frame_splicing: 1
    log: true
    log_zero_guard_type: add
    log_zero_guard_value: 1e-05
    mag_power: 1.0

Text Normalizer Configuration#

Text normalization (TN) converts text from written form into its verbalized form, and it is an essential preprocessing step before text-to-speech Synthesis. TN ensures that TTS can handle all input texts without skipping unknown symbols. For example, “$123” is converted to “one hundred and twenty three dollars”. Currently, NeMo supports text normalizers for English, German, Spanish, and Chinese. Refer to the previous Section (Inverse) Text Normalization for more details. Below shows an example of specifying text normalizer for English.

model:
  text_normalizer:
    _target_: nemo_text_processing.text_normalization.normalize.Normalizer
    lang: en
    input_case: cased
    whitelist: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv"

  text_normalizer_call_kwargs:
    verbose: false
    punct_pre_process: true
    punct_post_process: true

Tokenizer Configuration#

Tokenization converts input text string to a list of integer tokens. It may pad leading and/or trailing whitespaces to a string. NeMo tokenizer supports grapheme-only inputs, phoneme-only inputs, or a mixer of grapheme and phoneme inputs to disambiguate pronunciations of heteronyms for English, German, and Spanish. It also utilizes a grapheme-to-phoneme (G2P) tool to transliterate out-of-vocabulary (OOV) words. Please refer to the Section ../text_processing/g2p/g2p and TTS tokenizer collection for more details. Note that G2P integration to NeMo TTS tokenizers pipeline is upcoming soon. The following example sets up a EnglishPhonemesTokenizer with a mixer of grapheme and phoneme inputs where each word shown in the heteronym list is transliterated into graphemes or phonemes by a 50% chance.

model:
  text_tokenizer:
    _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer
    punct: true
    stresses: true
    chars: true
    apostrophe: true
    pad_with_space: true
    g2p:
      _target_: nemo_text_processing.g2p.modules.EnglishG2p
      phoneme_dict: ${phoneme_dict_path}
      heteronyms: ${heteronyms_path}
    phoneme_probability: 0.5

Model Architecture Configuration#

Each configuration file should describe the model architecture being used for the experiment. Models in the NeMo TTS collection need several module sections with the _target_ field specifying which model architecture or component is used. Please refer to TTS module collection for details. Below shows an example of FastPitch model architecture,

model:
  input_fft: #n_embed and padding_idx are added by the model
    _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder
    n_layer: 6
    n_head: 1
    d_model: 384
    d_head: 64
    d_inner: 1536
    kernel_size: 3
    dropout: 0.1
    dropatt: 0.1
    dropemb: 0.0
    d_embed: 384

  output_fft:
    _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder
    n_layer: 6
    n_head: 1
    d_model: 384
    d_head: 64
    d_inner: 1536
    kernel_size: 3
    dropout: 0.1
    dropatt: 0.1
    dropemb: 0.0

  alignment_module:
    _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder
    n_text_channels: 384

  duration_predictor:
    _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor
    input_size: 384
    kernel_size: 3
    filter_size: 256
    dropout: 0.1
    n_layers: 2

  pitch_predictor:
    _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor
    input_size: 384
    kernel_size: 3
    filter_size: 256
    dropout: 0.1
    n_layers: 2

  optim:
    name: adamw
    lr: 1e-3
    betas: [0.9, 0.999]
    weight_decay: 1e-6

    sched:
      name: NoamAnnealing
      warmup_steps: 1000
      last_epoch: -1
      d_model: 1  # Disable scaling based on model dim

Finetuning Configuration#

All TTS scripts support easy finetuning by partially/fully loading the pretrained weights from a checkpoint into the currently instantiated model. Note that the currently instantiated model should have parameters that match the pre-trained checkpoint (such that weights may load properly). In order to directly finetune a pre-existing checkpoint, please follow the tutorial of Finetuning FastPitch for a new speaker.

Pre-trained weights can be provided in multiple ways:

  1. Providing a path to a NeMo model (via init_from_nemo_model)

  2. Providing a name of a pretrained NeMo model (which will be downloaded via the cloud) (via init_from_pretrained_model)

  3. Providing a path to a Pytorch Lightning checkpoint file (via init_from_ptl_ckpt)

There are multiple TTS model finetuning scripts in examples/tts/<model>_finetune.py. You can finetune any model by substituting the <model> tag. An example of finetuning a HiFiGAN model is shown below.

Fine-tuning via a NeMo model#

python examples/tts/hifigan_finetune.py \
    --config-path=<path to dir of configs> \
    --config-name=<name of config without .yaml>) \
    model/train_ds=train_ds_finetune \
    model/validation_ds=val_ds_finetune \
    train_dataset="<path to manifest file>" \
    validation_dataset="<path to manifest file>" \
    model.optim.lr=0.00001 \
    ~model.optim.sched \
    trainer.devices=-1 \
    trainer.accelerator='gpu' \
    trainer.max_epochs=50 \
    +init_from_nemo_model="<path to .nemo model file>"

Fine-tuning via a NeMo pretrained model name#

python examples/tts/hifigan_finetune.py \
    --config-path=<path to dir of configs> \
    --config-name=<name of config without .yaml>) \
    model/train_ds=train_ds_finetune \
    model/validation_ds=val_ds_finetune \
    train_dataset="<path to manifest file>" \
    validation_dataset="<path to manifest file>" \
    model.optim.lr=0.00001 \
    ~model.optim.sched \
    trainer.devices=-1 \
    trainer.accelerator='gpu' \
    trainer.max_epochs=50 \
    +init_from_pretrained_model="<name of pretrained checkpoint>"

Fine-tuning via a Pytorch Lightning checkpoint#

python examples/tts/hifigan_finetune.py \
    --config-path=<path to dir of configs> \
    --config-name=<name of config without .yaml>) \
    model/train_ds=train_ds_finetune \
    model/validation_ds=val_ds_finetune \
    train_dataset="<path to manifest file>" \
    validation_dataset="<path to manifest file>" \
    model.optim.lr=0.00001 \
    ~model.optim.sched \
    trainer.devices=-1 \
    trainer.accelerator='gpu' \
    trainer.max_epochs=50 \
    +init_from_ptl_ckpt="<name of pytorch lightning checkpoint>"