[TTS] Add STFT and SI-SDR loss to audio codec recipe

examples/tts/conf/audio_codec/audio_codec_24000.yaml

@@ -0,0 +1,170 @@
+# This config contains the default values for training 24khz audio codec model
+# If you want to train model on other dataset, you can change config values according to your dataset.
+# Most dataset-specific arguments are in the head of the config file, see below.
+
+name: EnCodec
+
+max_epochs: ???
+# Adjust batch size based on GPU memory
+batch_size: 16
+# When doing weighted sampling with multiple manifests, this defines how many training steps are in an epoch.
+# If null, then weighted sampling is disabled.
+weighted_sampling_steps_per_epoch: null
+
+# Dataset metadata for each manifest
+# https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L39-L41
+train_ds_meta: ???
+val_ds_meta: ???
+
+log_ds_meta: ???
+log_dir: ???
+
+# Modify these values based on your sample rate
+sample_rate: 24000
+train_n_samples: 24000
+down_sample_rates: [2, 4, 5, 8]
+up_sample_rates: [8, 5, 4, 2]
+# The number of samples per encoded audio frame. Should be the product of the down_sample_rates.
+# For example 2 * 4 * 5 * 8 = 320.
+samples_per_frame: 320
+
+model:
+
+  max_epochs: ${max_epochs}
+  steps_per_epoch: ${weighted_sampling_steps_per_epoch}
+
+  sample_rate: ${sample_rate}
+  samples_per_frame: ${samples_per_frame}
+
+  mel_loss_l1_scale: 15.0
+  mel_loss_l2_scale: 0.0
+  stft_loss_scale: 15.0
+  time_domain_loss_scale: 0.0
+
+  # Probability of updating the discriminator during each training step
+  # For example, update the discriminator 2/3 times (2 updates for every 3 batches)
+  disc_updates_per_period: 2
+  disc_update_period: 3
+
+  # All resolutions for reconstruction loss, ordered [num_fft, hop_length, window_length]
+  loss_resolutions: [
+    [32, 8, 32], [64, 16, 64], [128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]
+  ]
+  mel_loss_dims: [5, 10, 20, 40, 80, 160, 320]
+  mel_loss_log_guard: 1.0
+  stft_loss_log_guard: 1.0
+
+  train_ds:
+    dataset:
+      _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
+      weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch}
+      sample_rate: ${sample_rate}
+      n_samples: ${train_n_samples}
+      min_duration: 1.01
+      max_duration: null
+      dataset_meta: ${train_ds_meta}
+
+    dataloader_params:
+      batch_size: ${batch_size}
+      drop_last: true
+      num_workers: 4
+
+  validation_ds:
+    dataset:
+      _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
+      sample_rate: ${sample_rate}
+      n_samples: null
+      min_duration: null
+      max_duration: null
+      trunc_duration: 10.0 # Only use the first 10 seconds of audio for computing validation loss
+      dataset_meta: ${val_ds_meta}
+
+    dataloader_params:
+      batch_size: 8
+      num_workers: 2
+
+  # Configures how audio samples are generated and saved during training.
+  # Remove this section to disable logging.
+  log_config:
+    log_dir: ${log_dir}
+    log_epochs: [10, 50]
+    epoch_frequency: 100
+    log_tensorboard: false
+    log_wandb: false
+
+    generators:
+      - _target_: nemo.collections.tts.parts.utils.callbacks.AudioCodecArtifactGenerator
+        log_audio: true
+        log_encoding: true
+        log_dequantized: true
+
+    dataset:
+      _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
+      sample_rate: ${sample_rate}
+      n_samples: null
+      min_duration: null
+      max_duration: null
+      trunc_duration: 15.0 # Only log the first 15 seconds of generated audio.
+      dataset_meta: ${log_ds_meta}
+
+    dataloader_params:
+      batch_size: 4
+      num_workers: 2
+
+  audio_encoder:
+    _target_: nemo.collections.tts.modules.encodec_modules.HifiGanEncoder
+    down_sample_rates: ${down_sample_rates}
+
+  audio_decoder:
+    _target_: nemo.collections.tts.modules.encodec_modules.SEANetDecoder
+    up_sample_rates: ${up_sample_rates}
+
+  vector_quantizer:
+    _target_: nemo.collections.tts.modules.encodec_modules.ResidualVectorQuantizer
+    num_codebooks: 8
+
+  discriminator:
+    _target_: nemo.collections.tts.modules.encodec_modules.MultiResolutionDiscriminatorSTFT
+    resolutions: [[128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]]
+
+  # The original EnCodec uses hinged loss, but squared-GAN loss is more stable
+  # and reduces the need to tune the loss weights or use a gradient balancer.
+  generator_loss:
+    _target_: nemo.collections.tts.losses.audio_codec_loss.GeneratorSquaredLoss
+
+  discriminator_loss:
+    _target_: nemo.collections.tts.losses.audio_codec_loss.DiscriminatorSquaredLoss
+
+  optim:
+    _target_: torch.optim.Adam
+    lr: 3e-4
+    betas: [0.5, 0.9]
+
+    sched:
+      name: ExponentialLR
+      gamma: 0.998
+
+trainer:
+  num_nodes: 1
+  devices: 1
+  accelerator: gpu
+  strategy: ddp_find_unused_parameters_true
+  precision: 32 # Vector quantization only works with 32-bit precision.
+  max_epochs: ${max_epochs}
+  accumulate_grad_batches: 1
+  enable_checkpointing: False # Provided by exp_manager
+  logger: false # Provided by exp_manager
+  log_every_n_steps: 100
+  check_val_every_n_epoch: 10
+  benchmark: false
+
+exp_manager:
+  exp_dir: null
+  name: ${name}
+  create_tensorboard_logger: true
+  create_checkpoint_callback: true
+  create_wandb_logger: false
+  checkpoint_callback_params:
+    monitor: val_loss
+  resume_if_exists: false
+  resume_ignore_no_checkpoint: false

examples/tts/conf/audio_codec/encodec.yaml → examples/tts/conf/audio_codec/encodec_24000.yaml

@@ -36,17 +36,23 @@ model:
   sample_rate: ${sample_rate}
   samples_per_frame: ${samples_per_frame}
 
-  mel_loss_scale: 5.0
+  mel_loss_l1_scale: 1.0
+  mel_loss_l2_scale: 1.0
+  stft_loss_scale: 0.0
   time_domain_loss_scale: 0.1
+
   # Probability of updating the discriminator during each training step
   # For example, update the discriminator 2/3 times (2 updates for every 3 batches)
   disc_updates_per_period: 2
   disc_update_period: 3
 
-  # All resolutions for mel reconstruction loss, ordered [num_fft, hop_length, window_length]
-  mel_loss_resolutions: [
+  # All resolutions for reconstruction loss, ordered [num_fft, hop_length, window_length]
+  loss_resolutions: [
     [32, 8, 32], [64, 16, 64], [128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]
   ]
+  mel_loss_dims: [64, 64, 64, 64, 64, 64, 64]
+  mel_loss_log_guard: 1E-5
+  stft_loss_log_guard: 1.0
 
   train_ds:
     dataset: