nemo/collections/tts/models/audio_codec.py

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import itertools
from contextlib import nullcontext
from math import ceil
from pathlib import Path
from typing import Iterable, List, Tuple

import torch
import torch.nn.functional as F
from einops import rearrange
from hydra.utils import instantiate
from lightning.pytorch import Trainer
from omegaconf import DictConfig, OmegaConf, open_dict

from nemo.collections.audio.parts.utils.transforms import Resample
from nemo.collections.tts.data.vocoder_dataset import VocoderDataset
from nemo.collections.tts.losses.audio_codec_loss import (
    FeatureMatchingLoss,
    MultiResolutionMelLoss,
    MultiResolutionSTFTLoss,
    RelativeFeatureMatchingLoss,
    SISDRLoss,
    TimeDomainLoss,
)
from nemo.collections.tts.modules.audio_codec_modules import ResNetSpeakerEncoder, default_precision
from nemo.collections.tts.modules.common import GaussianDropout
from nemo.collections.tts.parts.utils.callbacks import LoggingCallback
from nemo.collections.tts.parts.utils.helpers import get_batch_size, get_num_workers
from nemo.collections.tts.parts.utils.tts_dataset_utils import resample_batch
from nemo.core import ModelPT
from nemo.core.classes.common import PretrainedModelInfo, typecheck
from nemo.core.neural_types.elements import (
    AudioSignal,
    EncodedRepresentation,
    IntType,
    LengthsType,
    Optional,
    TokenIndex,
)
from nemo.core.neural_types.neural_type import NeuralType
from nemo.core.optim.lr_scheduler import compute_max_steps, prepare_lr_scheduler
from nemo.utils import logging, model_utils


class AudioCodecModel(ModelPT):
    def __init__(self, cfg: DictConfig, trainer: Trainer = None):
        # Convert to Hydra 1.0 compatible DictConfig
        cfg = model_utils.convert_model_config_to_dict_config(cfg)
        cfg = model_utils.maybe_update_config_version(cfg)
        self.world_size = 1
        if trainer is not None:
            self.world_size = trainer.num_nodes * trainer.num_devices

        super().__init__(cfg=cfg, trainer=trainer)

        # Expected sample rate for input and output audio
        self.sample_rate = cfg.sample_rate
        self.output_sample_rate = cfg.get("output_sample_rate", self.sample_rate)

        # Number of samples of input in each audio frame that is encoded
        self.samples_per_frame = cfg.samples_per_frame

        # Discriminator updates
        self.disc_updates_per_period = cfg.get("disc_updates_per_period", 1)
        self.disc_update_period = cfg.get("disc_update_period", 1)
        if self.disc_updates_per_period > self.disc_update_period:
            raise ValueError(
                f'Number of discriminator updates ({self.disc_updates_per_period}) per period must be less or equal to the configured period ({self.disc_update_period})'
            )

        # Encoder setup
        self.audio_encoder = instantiate(cfg.audio_encoder)

        # Optionally, add gaussian noise to encoder output as an information bottleneck
        encoder_noise_stdev = cfg.get("encoder_noise_stdev", 0.0)
        if encoder_noise_stdev:
            self.encoder_noise = GaussianDropout(stdev=encoder_noise_stdev)
        else:
            self.encoder_noise = None

        if "vector_quantizer" in cfg:
            self.vector_quantizer = instantiate(cfg.vector_quantizer)

            vq_output_types = list(self.vector_quantizer.output_types.keys())

            if len(vq_output_types) == 3 and vq_output_types[-1] == 'commit_loss':
                self.vector_quantizer_has_commit_loss = True
                logging.info('Vector quantizer supports commit loss.')
            else:
                self.vector_quantizer_has_commit_loss = False
                logging.info('Vector quantizer does not support commit loss.')

        else:
            logging.warning('Vector quantizer will not be used.')
            self.vector_quantizer = None

        # Decoder setup
        self.audio_decoder = instantiate(cfg.audio_decoder)

        # Discriminator setup
        if cfg.get("discriminator"):
            self.discriminator = instantiate(cfg.discriminator)
        else:
            self.discriminator = None

        # If 'semantic_codec_path' is provided, the semantic codec will be initialized from the provided path.
        # It will then be registered as a submodule and automatically loaded from the 'semantic_codec' field
        if cfg.get("semantic_codec"):
            semantic_codec_cfg = cfg.get("semantic_codec")
            semantic_codec = AudioCodecModel(cfg=semantic_codec_cfg)
        elif cfg.get("semantic_codec_path"):
            semantic_codec_path = cfg.get("semantic_codec_path")
            semantic_codec = AudioCodecModel.restore_from(semantic_codec_path)
        else:
            semantic_codec = None

        if semantic_codec is not None:
            semantic_codec.eval()
            semantic_codec.freeze()
            self.register_nemo_submodule(name="semantic_codec", config_field="semantic_codec", model=semantic_codec)
        else:
            self.semantic_codec = None

        # Optional config for using semantic distillation loss
        self.use_slm_loss = cfg.get("use_slm_loss", False)
        if self.use_slm_loss:
            self.slm_encoder = instantiate(cfg.get("slm_encoder"))
            self.slm_encoder.eval()
            self.slm_encoder.freeze()
            self.slm_predictor = instantiate(cfg.slm_predictor)
            self.slm_loss_fn = torch.nn.MSELoss()
            self.slm_loss_scale = cfg.get("slm_loss_scale", 1.0)
        else:
            self.slm_encoder = None
            self.slm_predictor = None
            self.slm_loss_fn = None
            self.slm_loss_scale = None

        # Mel loss setup
        loss_resolutions = cfg.loss_resolutions
        mel_loss_dims = cfg.get("mel_loss_dims")
        mel_loss_log_guard = cfg.get("mel_loss_log_guard", 1.0)
        self.mel_loss_l1_scale = cfg.get("mel_loss_l1_scale", 1.0)
        self.mel_loss_l2_scale = cfg.get("mel_loss_l2_scale", 1.0)
        self.mel_loss_fn = MultiResolutionMelLoss(
            sample_rate=self.sample_rate,
            mel_dims=mel_loss_dims,
            resolutions=loss_resolutions,
            log_guard=mel_loss_log_guard,
        )

        # STFT loss setup
        stft_loss_log_guard = cfg.get("stft_loss_log_guard", 1.0)
        self.stft_loss_scale = cfg.get("stft_loss_scale", 0.0)
        self.stft_loss_fn = MultiResolutionSTFTLoss(
            resolutions=loss_resolutions,
            log_guard=stft_loss_log_guard,
        )

        # Time domain loss setup
        self.time_domain_loss_scale = cfg.get("time_domain_loss_scale", 1.0)
        self.si_sdr_loss_scale = cfg.get("si_sdr_loss_scale", 0.0)
        self.time_domain_loss_fn = TimeDomainLoss()
        self.si_sdr_loss_fn = SISDRLoss()

        # Discriminator loss setup
        self.gen_loss_scale = cfg.get("gen_loss_scale", 1.0)
        self.feature_loss_scale = cfg.get("feature_loss_scale", 1.0)
        self.gen_loss_fn = instantiate(cfg.generator_loss)
        self.disc_loss_fn = instantiate(cfg.discriminator_loss)

        self.mmd_loss_start_epoch = cfg.get("mmd_loss_start_epoch", 0)

        if "mmd_loss" in cfg:
            self.mmd_loss_fn = instantiate(cfg.mmd_loss)
            self.mmd_loss_scale = cfg.get("mmd_loss_scale", 1.0)
        else:
            self.mmd_loss_fn = None
            self.mmd_loss_scale = None

        if "mmd_time_loss" in cfg:
            self.mmd_time_loss_fn = instantiate(cfg.mmd_time_loss)
            self.mmd_time_loss_scale = cfg.get("mmd_time_loss_scale", 1.0)
        else:
            self.mmd_time_loss_fn = None
            self.mmd_time_loss_scale = None

        feature_loss_type = cfg.get("feature_loss_type", "relative")
        if feature_loss_type == "relative":
            self.feature_loss_fn = RelativeFeatureMatchingLoss()
        elif feature_loss_type == "absolute":
            self.feature_loss_fn = FeatureMatchingLoss()
        else:
            raise ValueError(f'Unknown feature loss type {feature_loss_type}.')

        # Codebook loss setup
        if self.vector_quantizer:
            self.commit_loss_scale = cfg.get("commit_loss_scale", 1.0)
        else:
            self.commit_loss_scale = 0.0

        if self.commit_loss_scale > 0 and not self.vector_quantizer_has_commit_loss:
            raise ValueError('Commit loss is enabled but the quantizer does not support it.')

        self.use_scl_loss = cfg.get("use_scl_loss", False)
        self.scl_loss_scale = cfg.get("scl_loss_scale", False)
        if self.use_scl_loss:
            self.speaker_encoder = ResNetSpeakerEncoder()
            # load pretrained model
            # self.speaker_encoder.load_checkpoint("https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar")
            self.speaker_encoder.load_checkpoint(
                "https://huggingface.co/Edresson/Speaker_Encoder_H_ASP/resolve/main/pytorch_model.bin",
                strict=False,
            )
            # freeze the pretrained speaker encoder
            self.speaker_encoder.freeze()
            logging.info("Speaker encoder loaded and frozen !!")
            self.speaker_encoder_resampler = Resample(
                orig_freq=self.sample_rate, new_freq=self.speaker_encoder.audio_config["sample_rate"]
            )

        self.disc_start_epoch = cfg.get("disc_start_epoch", 0)

        # Log setup
        self.log_config = cfg.get("log_config", None)

        # Optimizer setup
        self.lr_schedule_interval = None
        self.automatic_optimization = False

    @property
    def dtype(self):
        return next(self.parameters()).dtype

    @property
    def num_codebooks(self):
        if self.vector_quantizer is None:
            raise ValueError("This AudioCodecModel does not have a vector quantizer.")

        return self.vector_quantizer.num_codebooks

    @property
    def codebook_size(self):
        if self.vector_quantizer is None:
            raise ValueError("This AudioCodecModel does not have a vector quantizer.")

        return self.vector_quantizer.codebook_size

    def state_dict(self, destination=None, prefix='', keep_vars=False):
        if hasattr(self, '_no_state_dict') and self._no_state_dict:
            return {}
        # Avoid saving weights of frozen pretrained models
        state_dict = super().state_dict(destination, prefix, keep_vars)
        for key in list(state_dict.keys()):
            if self.use_scl_loss and "speaker_encoder." in key:
                del state_dict[key]
            if "discriminator" in key and ".slm_model.slm_model." in key:
                del state_dict[key]
            if key.startswith("slm_encoder."):
                del state_dict[key]

        return state_dict

    def load_state_dict(self, state_dict, strict=True):
        # Avoid loading weights of frozen pretrained models
        for key in list(state_dict.keys()):
            if self.use_scl_loss and "speaker_encoder." in key:
                del state_dict[key]
            if "discriminator" in key and ".slm_model.slm_model." in key:
                del state_dict[key]
            if key.startswith("slm_encoder."):
                del state_dict[key]

        super().load_state_dict(state_dict, strict=False)

    def get_speaker_embedding(self, audio, requires_grad=False):
        grad_context = nullcontext() if requires_grad else torch.no_grad()
        with grad_context:
            audio_resampled = self.speaker_encoder_resampler(audio)
            g = self.speaker_encoder(audio_resampled, l2_norm=True).unsqueeze(-1)

        return g

    @typecheck(
        input_types={
            "audio": NeuralType(('B', 'T_audio'), AudioSignal()),
            "audio_len": NeuralType(tuple('B'), LengthsType()),
            "sample_rate": NeuralType(tuple(), IntType(), optional=True),
        },
        output_types={
            "encoded": NeuralType(('B', 'D', 'T_encoded'), EncodedRepresentation()),
            "encoded_len": NeuralType(tuple('B'), LengthsType()),
        },
    )
    def encode_audio(
        self, audio: torch.Tensor, audio_len: torch.Tensor, sample_rate: Optional[int] = None
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Apply encoder on the input audio signal. Input will be padded with zeros so
        the last frame has full `self.samples_per_frame` samples.

        Args:
            audio: input time-domain signal
            audio_len: valid length for each example in the batch
            sample_rate: sample rate of input audio (int)

        Returns:
            Encoder output `encoded` and its length in number of frames `encoded_len`
        """
        if not sample_rate:
            sample_rate = self.sample_rate

        audio_preprocessed, audio_preprocessed_len = self.preprocess_audio(
            audio=audio, audio_len=audio_len, sample_rate=sample_rate
        )
        encoded, encoded_len = self.audio_encoder(audio=audio_preprocessed, audio_len=audio_preprocessed_len)

        if self.semantic_codec is not None:
            with torch.no_grad():
                semantic, _ = self.semantic_codec.encode_audio(
                    audio=audio, audio_len=audio_len, sample_rate=sample_rate
                )
            encoded = torch.concat([semantic, encoded], dim=1)

        return encoded, encoded_len

    @typecheck(
        input_types={
            "inputs": NeuralType(('B', 'D', 'T_encoded'), EncodedRepresentation()),
            "input_len": NeuralType(tuple('B'), LengthsType()),
        },
        output_types={
            "audio": NeuralType(('B', 'T_audio'), AudioSignal()),
            "audio_len": NeuralType(tuple('B'), LengthsType()),
        },
    )
    def decode_audio(self, inputs: torch.Tensor, input_len: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """Apply decoder on the input. Note that the input is a non-quantized encoder output or a dequantized representation.

        Args:
            inputs: encoded signal
            input_len: valid length for each example in the batch

        Returns:
            Decoded output `audio` in the time domain and its length in number of samples `audio_len`.
            Note that `audio_len` will be a multiple of `self.samples_per_frame`.
        """
        audio, audio_len = self.audio_decoder(inputs=inputs, input_len=input_len)
        return audio, audio_len

    @typecheck(
        input_types={
            "encoded": NeuralType(('B', 'D', 'T_encoded'), EncodedRepresentation()),
            "encoded_len": NeuralType(tuple('B'), LengthsType()),
        },
        output_types={"tokens": NeuralType(('B', 'C', 'T_encoded'), TokenIndex())},
    )
    def quantize(self, encoded: torch.Tensor, encoded_len: torch.Tensor) -> torch.Tensor:
        """Quantize the continuous encoded representation into a discrete
        representation for each frame.

        Args:
            encoded: encoded signal representation
            encoded_len: valid length of the encoded representation in frames

        Returns:
            A tensor of tokens for each codebook for each frame.
        """
        if not self.vector_quantizer:
            raise ValueError("Cannot quantize without quantizer")

        # vector quantizer is returning [C, B, T], where C is the number of codebooks
        with default_precision(torch.float32):
            tokens = self.vector_quantizer.encode(inputs=encoded, input_len=encoded_len)
        # use batch first for the output
        tokens = rearrange(tokens, 'C B T -> B C T')
        return tokens

    @typecheck(
        input_types={
            "tokens": NeuralType(('B', 'C', 'T_encoded'), TokenIndex()),
            "tokens_len": NeuralType(tuple('B'), LengthsType()),
        },
        output_types={
            "dequantized": NeuralType(('B', 'D', 'T_encoded'), EncodedRepresentation()),
        },
    )
    def dequantize(self, tokens: torch.Tensor, tokens_len: torch.Tensor) -> torch.Tensor:
        """Convert the discrete tokens into a continuous encoded representation.

        Args:
            tokens: discrete tokens for each codebook for each time frame
            tokens_len: valid length of each example in the batch

        Returns:
            Continuous encoded representation of the discrete input representation.
        """
        if not self.vector_quantizer:
            raise ValueError("Cannot dequantize without quantizer")

        # vector quantizer is using [C, B, T], where C is the number of codebooks
        tokens = rearrange(tokens, 'B C T -> C B T')
        with default_precision(torch.float32):
            dequantized = self.vector_quantizer.decode(indices=tokens, input_len=tokens_len)
        dequantized = dequantized.to(self.dtype)  # make sure dequantized is in the right dtype
        return dequantized

    @typecheck(
        input_types={
            "audio": NeuralType(('B', 'T_audio'), AudioSignal()),
            "audio_len": NeuralType(tuple('B'), LengthsType()),
            "sample_rate": NeuralType(tuple(), IntType(), optional=True),
        },
        output_types={
            "tokens": NeuralType(('B', 'C', 'T_encoded'), TokenIndex()),
            "tokens_len": NeuralType(tuple('B'), LengthsType()),
        },
    )
    def encode(
        self, audio: torch.Tensor, audio_len: torch.Tensor, sample_rate: Optional[int] = None
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Convert input time-domain audio signal into a discrete representation (tokens).

        Args:
            audio: input time-domain signal, shape `(batch, number of samples)`
            audio_len: valid length for each example in the batch, shape `(batch size,)`
            sample_rate: sample rate of input audio (int)

        Returns:
            Tokens for each codebook for each frame, shape `(batch, number of codebooks, number of frames)`,
            and the corresponding valid lengths, shape `(batch,)`
        """
        # Apply encoder to obtain a continuous vector for each frame
        encoded, encoded_len = self.encode_audio(audio=audio, audio_len=audio_len, sample_rate=sample_rate)
        # Apply quantizer to obtain discrete representation per frame
        tokens = self.quantize(encoded=encoded, encoded_len=encoded_len)
        return tokens, encoded_len

    @typecheck(
        input_types={
            "tokens": NeuralType(('B', 'C', 'T_encoded'), TokenIndex()),
            "tokens_len": NeuralType(tuple('B'), LengthsType()),
        },
        output_types={
            "audio": NeuralType(('B', 'T_audio'), AudioSignal()),
            "audio_len": NeuralType(tuple('B'), LengthsType()),
        },
    )
    def decode(self, tokens: torch.Tensor, tokens_len: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """Convert discrete tokens into a continuous time-domain signal.

        Args:
            tokens: discrete tokens for each codebook for each time frame, shape `(batch, number of codebooks, number of frames)`
            tokens_len: valid lengths, shape `(batch,)`

        Returns:
            Decoded output `audio` in the time domain and its length in number of samples `audio_len`.
            Note that `audio_len` will be a multiple of `self.samples_per_frame`.
        """
        # Convert a discrete representation to a dequantized vector for each frame
        dequantized = self.dequantize(tokens=tokens, tokens_len=tokens_len)
        dequantized = dequantized.to(self.dtype)  # make sure that the dequantized is in the model dtype
        # Apply decoder to obtain time-domain audio for each frame
        audio, audio_len = self.decode_audio(inputs=dequantized, input_len=tokens_len)

        return audio, audio_len

    @typecheck(
        input_types={
            "audio": NeuralType(('B', 'T_audio'), AudioSignal()),
            "audio_len": NeuralType(tuple('B'), LengthsType()),
            "sample_rate": NeuralType(tuple(), IntType(), optional=True),
        },
        output_types={
            "output_audio": NeuralType(('B', 'T_audio'), EncodedRepresentation()),
            "output_audio_len": NeuralType(tuple('B'), LengthsType()),
        },
    )
    def forward(
        self, audio: torch.Tensor, audio_len: torch.Tensor, sample_rate: Optional[int] = None
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Apply encoder, quantizer, decoder on the input time-domain signal.

        Args:
            audio: input time-domain signal
            audio_len: valid length for each example in the batch
            sample_rate: sample rate of input audio (int)

        Returns:
            Reconstructed time-domain signal `output_audio` and its length in number of samples `output_audio_len`.
        """
        encoded, encoded_len = self.encode_audio(audio=audio, audio_len=audio_len, sample_rate=sample_rate)

        if self.vector_quantizer:
            # quantize to discrete tokens
            tokens = self.quantize(encoded=encoded, encoded_len=encoded_len)
            # decode tokens to audio
            output_audio, output_audio_len = self.decode(tokens=tokens, tokens_len=encoded_len)
        else:
            # no quantization, directly decode to audio
            output_audio, output_audio_len = self.decode_audio(inputs=encoded, input_len=encoded_len)

        return output_audio, output_audio_len

    def pad_audio(self, audio, audio_len, samples_per_frame):
        """Zero pad the end of the audio so that we do not have a partial end frame.
        The output will be zero-padded to have an integer number of frames of
        length `self.samples_per_frame`.

        Args:
            audio: input time-domain signal
            audio_len: valid length for each example in the batch

        Returns:
            Padded time-domain signal `padded_audio` and its length `padded_len`.
        """
        num_frames = audio_len / samples_per_frame
        # To avoid rounding issues at lower precisions, do not call torch.ceil when the length is divisible by the frame rate
        num_frames = torch.where(audio_len % samples_per_frame == 0, num_frames, torch.ceil(num_frames))
        padded_len = samples_per_frame * num_frames.int()
        max_len = padded_len.max().item()
        num_padding = max_len - audio.shape[1]
        padded_audio = F.pad(audio, (0, num_padding))
        return padded_audio, padded_len

    def preprocess_audio(self, audio, audio_len, sample_rate):
        if sample_rate and sample_rate != self.sample_rate:
            audio, audio_len = resample_batch(
                audio=audio, audio_len=audio_len, input_sample_rate=sample_rate, output_sample_rate=self.sample_rate
            )

        audio, audio_len = self.pad_audio(audio=audio, audio_len=audio_len, samples_per_frame=self.samples_per_frame)
        return audio, audio_len

    def _process_batch(self, batch):
        # [B, T_audio]
        audio = batch.get("audio")
        # [B]
        audio_len = batch.get("audio_lens")

        # Pad input audio to the same length as the final output
        target_samples_per_frame = int(self.samples_per_frame / self.sample_rate * self.output_sample_rate)
        audio, audio_len = self.pad_audio(audio=audio, audio_len=audio_len, samples_per_frame=target_samples_per_frame)

        # [B, D, T_encoded]
        encoded, encoded_len = self.encode_audio(audio=audio, audio_len=audio_len, sample_rate=self.output_sample_rate)

        if self.encoder_noise is not None:
            encoded = self.encoder_noise(encoded)

        if self.vector_quantizer:
            with default_precision(torch.float32):
                if self.vector_quantizer_has_commit_loss:
                    encoded, _, commit_loss = self.vector_quantizer(inputs=encoded, input_len=encoded_len)
                else:
                    encoded, _ = self.vector_quantizer(inputs=encoded, input_len=encoded_len)
                    commit_loss = 0.0

            encoded = encoded.to(encoded.dtype)  # make sure encoded is converted to the right dtype
        else:
            commit_loss = 0.0

        # [B, T]
        audio_gen, _ = self.audio_decoder(inputs=encoded, input_len=encoded_len)

        if self.training and self.use_slm_loss:
            slm_emb = self.slm_encoder(audio=audio)
            slm_emb_pred = self.slm_predictor(inputs=encoded)
        else:
            slm_emb = None
            slm_emb_pred = None

        return audio, audio_len, audio_gen, commit_loss, encoded, slm_emb, slm_emb_pred

    @property
    def disc_update_prob(self) -> float:
        """Probability of updating the discriminator."""
        return self.disc_updates_per_period / self.disc_update_period

    def should_update_disc(self, batch_idx) -> bool:
        """Decide whether to update the descriminator based
        on the batch index and configured discriminator update period.
        """
        if self.current_epoch < self.disc_start_epoch:
            return False

        disc_update_step = batch_idx % self.disc_update_period
        return disc_update_step < self.disc_updates_per_period

    def training_step(self, batch, batch_idx):
        if self.discriminator is None:
            optim_gen = self.optimizers()
            optim_disc = None
        else:
            optim_gen, optim_disc = self.optimizers()

        audio, audio_len, audio_gen, commit_loss, codes, slm_emb, slm_emb_pred = self._process_batch(batch)

        metrics = {
            "global_step": self.global_step,
            "lr": optim_gen.param_groups[0]['lr'],
        }

        if optim_disc is not None and self.should_update_disc(batch_idx):
            # Train discriminator
            disc_scores_real, disc_scores_gen, _, _ = self.discriminator(
                audio_real=audio, audio_gen=audio_gen.detach()
            )
            loss_disc = self.disc_loss_fn(disc_scores_real=disc_scores_real, disc_scores_gen=disc_scores_gen)
            metrics["d_loss"] = loss_disc

            optim_disc.zero_grad()
            self.manual_backward(loss_disc)
            optim_disc.step()

        generator_losses = []

        # stft does not support bf16, so make it run in fp32
        loss_mel_l1, loss_mel_l2 = self.mel_loss_fn(
            audio_real=audio.float(), audio_gen=audio_gen.float(), audio_len=audio_len
        )

        if self.mel_loss_l1_scale:
            metrics["g_loss_mel_l1"] = loss_mel_l1
            generator_losses.append(self.mel_loss_l1_scale * loss_mel_l1)
        if self.mel_loss_l2_scale:
            metrics["g_loss_mel_l2"] = loss_mel_l2
            generator_losses.append(self.mel_loss_l2_scale * loss_mel_l2)

        if self.stft_loss_scale:
            loss_stft = self.stft_loss_fn(audio_real=audio.float(), audio_gen=audio_gen.float(), audio_len=audio_len)
            metrics["g_loss_stft"] = loss_stft
            generator_losses.append(self.stft_loss_scale * loss_stft)

        if self.time_domain_loss_scale:
            loss_time_domain = self.time_domain_loss_fn(audio_real=audio, audio_gen=audio_gen, audio_len=audio_len)
            metrics["g_loss_time_domain"] = loss_time_domain
            generator_losses.append(self.time_domain_loss_scale * loss_time_domain)

        if self.si_sdr_loss_scale:
            loss_si_sdr = self.si_sdr_loss_fn(audio_real=audio, audio_gen=audio_gen, audio_len=audio_len)
            metrics["g_loss_si_sdr"] = loss_si_sdr
            generator_losses.append(self.si_sdr_loss_scale * loss_si_sdr)

        if optim_disc is not None:

            _, disc_scores_gen, fmaps_real, fmaps_gen = self.discriminator(audio_real=audio, audio_gen=audio_gen)

            if self.gen_loss_scale:
                loss_gen = self.gen_loss_fn(disc_scores_gen=disc_scores_gen)
                metrics["g_loss_gen"] = loss_gen
                generator_losses.append(self.gen_loss_scale * loss_gen)

            if self.feature_loss_scale:
                loss_feature = self.feature_loss_fn(fmaps_real=fmaps_real, fmaps_gen=fmaps_gen)
                metrics["g_loss_feature"] = loss_feature
                generator_losses.append(self.feature_loss_scale * loss_feature)

        if self.commit_loss_scale:
            metrics["g_loss_commit"] = commit_loss
            generator_losses.append(self.commit_loss_scale * commit_loss)

        if self.mmd_loss_scale:
            loss_mmd = self.mmd_loss_fn(inputs=codes)
            metrics["g_loss_mmd"] = loss_mmd
            if self.current_epoch >= self.mmd_loss_start_epoch:
                generator_losses.append(self.mmd_loss_scale * loss_mmd)

        if self.mmd_time_loss_scale:
            loss_mmd_time = self.mmd_time_loss_fn(inputs=codes)
            metrics["g_loss_mmd_time"] = loss_mmd_time
            if self.current_epoch >= self.mmd_loss_start_epoch:
                generator_losses.append(self.mmd_time_loss_scale * loss_mmd_time)

        if self.use_slm_loss:
            loss_slm = self.slm_loss_fn(input=slm_emb_pred, target=slm_emb)
            metrics["g_loss_slm"] = loss_slm
            generator_losses.append(self.slm_loss_scale * loss_slm)

        # compute embeddings for speaker consistency loss
        if self.use_scl_loss:
            # concate generated and GT waveforms
            audios_batch = torch.cat((audio.squeeze(1), audio_gen.squeeze(1)), dim=0)

            # get speaker embeddings with grads
            pred_embs = self.get_speaker_embedding(audios_batch, requires_grad=True)

            # split generated and GT speaker embeddings
            gt_spk_emb, syn_spk_emb = torch.chunk(pred_embs, 2, dim=0)

            # speaker consistency loss like YourTTS paper
            loss_scl = -1 * torch.nn.functional.cosine_similarity(gt_spk_emb, syn_spk_emb).mean() * self.scl_loss_scale

            metrics["g_loss_scl"] = loss_scl
            generator_losses.append(metrics["g_loss_scl"])

        loss_gen_all = sum(generator_losses)

        optim_gen.zero_grad()
        self.manual_backward(loss_gen_all)
        optim_gen.step()

        self.update_lr()

        self.log_dict(metrics, on_step=True, sync_dist=True)
        self.log("t_loss", loss_mel_l1, prog_bar=True, logger=False, sync_dist=True)

    def on_train_epoch_end(self):
        self.update_lr("epoch")

    def validation_step(self, batch, batch_idx):
        audio, audio_len, audio_gen, *_ = self._process_batch(batch)

        loss_mel_l1, loss_mel_l2 = self.mel_loss_fn(
            audio_real=audio.float(), audio_gen=audio_gen.float(), audio_len=audio_len
        )
        loss_stft = self.stft_loss_fn(audio_real=audio.float(), audio_gen=audio_gen.float(), audio_len=audio_len)
        loss_time_domain = self.time_domain_loss_fn(audio_real=audio, audio_gen=audio_gen, audio_len=audio_len)
        loss_si_sdr = self.si_sdr_loss_fn(audio_real=audio, audio_gen=audio_gen, audio_len=audio_len)

        # Use only main reconstruction losses for val_loss
        val_loss = loss_mel_l1 + loss_stft + loss_time_domain

        metrics = {
            "val_loss": val_loss,
            "val_loss_mel_l1": loss_mel_l1,
            "val_loss_mel_l2": loss_mel_l2,
            "val_loss_stft": loss_stft,
            "val_loss_time_domain": loss_time_domain,
            "val_loss_si_sdr": loss_si_sdr,
        }
        # compute embeddings for speaker consistency loss
        if self.use_scl_loss:
            # concate generated and GT waveforms
            audios_batch = torch.cat((audio.squeeze(1), audio_gen.squeeze(1)), dim=0)

            # get speaker embeddings with grads
            pred_embs = self.get_speaker_embedding(audios_batch, requires_grad=True)

            # split generated and GT speaker embeddings
            gt_spk_emb, syn_spk_emb = torch.chunk(pred_embs, 2, dim=0)

            # speaker consistency loss like YourTTS paper
            loss_scl = -1 * torch.nn.functional.cosine_similarity(gt_spk_emb, syn_spk_emb).mean() * self.scl_loss_scale

            metrics["val_loss_scl"] = loss_scl
            metrics["val_loss"] += metrics["val_loss_scl"]

        self.log_dict(metrics, on_epoch=True, sync_dist=True)

    def get_dataset(self, cfg, is_sharded=False):
        if is_sharded:
            with open_dict(cfg):
                cfg.dataset.global_rank = self.global_rank
                cfg.dataset.world_size = self.world_size
                cfg.dataset._target_ = 'nemo.collections.tts.data.vocoder_dataset.TarredVocoderDataset'
            dataset = instantiate(cfg.dataset)
        elif '_target_' in cfg.dataset:
            dataset = instantiate(cfg.dataset)
        else:
            dataset = VocoderDataset(**cfg.dataset.dataset_args)

        sampler = dataset.get_sampler(cfg.dataloader_params.batch_size, world_size=self.trainer.world_size)
        data_loader = torch.utils.data.DataLoader(
            dataset, collate_fn=dataset.collate_fn, sampler=sampler, **cfg.dataloader_params
        )
        return data_loader

    def _setup_train_dataloader(self, cfg):
        with open_dict(cfg):
            is_sharded = cfg.dataset.pop('is_sharded', False)

        return self.get_dataset(cfg, is_sharded=is_sharded)

    def _setup_test_dataloader(self, cfg):
        return self.get_dataset(cfg)

    def setup_training_data(self, cfg):
        self._train_dl = self._setup_train_dataloader(cfg)
        batch_size = cfg['dataloader_params']['batch_size']
        # Need to set this because if using an IterableDataset, the length of the dataloader is the total number
        # of samples rather than the number of batches, and this messes up the tqdm progress bar.
        # So we set the number of steps manually (to the correct number) to fix this.
        if (
            self._train_dl is not None
            and hasattr(self._train_dl, 'dataset')
            and isinstance(self._train_dl.dataset, torch.utils.data.IterableDataset)
        ):
            # We also need to check if limit_train_batches is already set.
            # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
            # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).
            if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float):
                self._trainer.limit_train_batches = int(
                    self._trainer.limit_train_batches
                    * ceil((len(self._train_dl.dataset) / self.world_size) / batch_size)
                )
            elif self._trainer is None:
                logging.warning(
                    "Model Trainer was not set before constructing the dataset, incorrect number of "
                    "training batches will be used. Please set the trainer and rebuild the dataset."
                )

    def setup_validation_data(self, cfg):
        self._validation_dl = self._setup_test_dataloader(cfg)

    def setup_test_data(self, cfg):
        pass

    @property
    def max_steps(self):
        if "max_steps" in self._cfg:
            return self._cfg.get("max_steps")

        if "max_epochs" not in self._cfg:
            raise ValueError("Must specify 'max_steps' or 'max_epochs'.")

        if "steps_per_epoch" in self._cfg:
            return self._cfg.max_epochs * self._cfg.steps_per_epoch
        return compute_max_steps(
            max_epochs=self._cfg.max_epochs,
            accumulate_grad_batches=self.trainer.accumulate_grad_batches,
            limit_train_batches=self.trainer.limit_train_batches,
            num_workers=get_num_workers(self.trainer),
            num_samples=len(self._train_dl.dataset),
            batch_size=get_batch_size(self._train_dl),
            drop_last=self._train_dl.drop_last,
        )

    def configure_optimizers(self):
        optim_config = self._cfg.optim.copy()

        OmegaConf.set_struct(optim_config, False)
        sched_config = optim_config.pop("sched", None)
        OmegaConf.set_struct(optim_config, True)

        se_params = self.speaker_encoder.parameters() if self.use_scl_loss else []
        vq_params = self.vector_quantizer.parameters() if self.vector_quantizer else []
        gen_params = itertools.chain(
            self.audio_encoder.parameters(), self.audio_decoder.parameters(), vq_params, se_params
        )
        optim_g = instantiate(optim_config, params=gen_params)

        if self.discriminator is None:
            optim_d = None
        else:
            disc_params = self.discriminator.parameters()
            optim_d = instantiate(optim_config, params=disc_params)

        if sched_config is None:
            logging.debug('Scheduler is not used')
            if optim_d is None:
                return optim_g
            else:
                return optim_g, optim_d

        logging.debug('Setting up schedulers')
        OmegaConf.set_struct(sched_config, False)
        sched_config["max_steps"] = self.max_steps
        OmegaConf.set_struct(sched_config, True)

        scheduler_g = prepare_lr_scheduler(
            optimizer=optim_g, scheduler_config=sched_config, train_dataloader=self._train_dl
        )

        self.lr_schedule_interval = scheduler_g["interval"]

        if optim_d is None:
            return [optim_g], [scheduler_g]
        else:
            scheduler_d = prepare_lr_scheduler(
                optimizer=optim_d, scheduler_config=sched_config, train_dataloader=self._train_dl
            )
            return [optim_g, optim_d], [scheduler_g, scheduler_d]

    def update_lr(self, interval="step"):
        schedulers = self.lr_schedulers()
        if schedulers is None or self.lr_schedule_interval != interval:
            return

        if not isinstance(schedulers, Iterable):
            schedulers.step()
        else:
            for sch in schedulers:
                sch.step()

    def configure_callbacks(self):
        if not self.log_config:
            return []

        data_loader = self._setup_test_dataloader(self.log_config)
        generators = instantiate(self.log_config.generators)
        log_dir = Path(self.log_config.log_dir) if self.log_config.log_dir else None
        log_callback = LoggingCallback(
            generators=generators,
            data_loader=data_loader,
            log_epochs=self.log_config.log_epochs,
            epoch_frequency=self.log_config.epoch_frequency,
            output_dir=log_dir,
            loggers=self.trainer.loggers,
            log_tensorboard=self.log_config.log_tensorboard,
            log_wandb=self.log_config.log_wandb,
        )

        return [log_callback]

    @classmethod
    def list_available_models(cls) -> List[PretrainedModelInfo]:
        models = []

        model = PretrainedModelInfo(
            pretrained_model_name="audio_codec_16khz_small",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/audio_codec_16khz_small/versions/v1/files/audio_codec_16khz_small.nemo",
            description="For details about this model please refer to the model card: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/audio_codec_16khz_small",
        )
        models.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="mel_codec_22khz_medium",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mel_codec_22khz_medium/versions/v1/files/mel_codec_22khz_medium.nemo",
            description="For details about this model please refer to the model card: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/mel_codec_22khz_medium",
        )
        models.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="mel_codec_44khz_medium",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mel_codec_44khz_medium/versions/v1/files/mel_codec_44khz_medium.nemo",
            description="For details about this model please refer to the model card: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/mel_codec_44khz_medium",
        )
        models.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="mel_codec_22khz_fullband_medium",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mel_codec_22khz_fullband_medium/versions/v1/files/mel_codec_22khz_fullband_medium.nemo",
            description="For details about this model please refer to the model card: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/mel_codec_22khz_fullband_medium",
        )
        models.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="mel_codec_44khz_fullband_medium",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mel_codec_44khz_fullband_medium/versions/v1/files/mel_codec_44khz_fullband_medium.nemo",
            description="For details about this model please refer to the model card: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/mel_codec_44khz_fullband_medium",
        )
        models.append(model)

        return models