modeling_vits.py

from typing import Any, Optional, Union
import numpy as np
import torch
from torch import nn
from transformers import VitsPreTrainedModel , VitsConfig
from transformers.models.vits.modeling_vits import VitsTextEncoder , VitsResidualCouplingBlock , VitsHifiGan , VitsStochasticDurationPredictor , VitsDurationPredictor , VitsPosteriorEncoder , VitsModelOutput
from transformers.utils import auto_docstring
from torch.nn.utils.parametrizations import weight_norm
@auto_docstring(
    custom_intro="""
    The complete VITS model, for text-to-speech synthesis.
    """
)
class VitsModel(VitsPreTrainedModel):
    def __init__(self, config: VitsConfig):
        super().__init__(config)
        self.config = config
        self.text_encoder = VitsTextEncoder(config)
        self.flow = VitsResidualCouplingBlock(config)
        self.decoder = VitsHifiGan(config)

        if config.use_stochastic_duration_prediction:
            self.duration_predictor = VitsStochasticDurationPredictor(config)
        else:
            self.duration_predictor = VitsDurationPredictor(config)

        if config.num_speakers > 1:
            self.embed_speaker = nn.Embedding(config.num_speakers, config.speaker_embedding_size)
        
        if config.num_emotions > 1:
            self.embed_emotion = nn.Embedding(config.num_emotions, config.emotion_embedding_size)

        # This is used only for training.
        self.posterior_encoder = VitsPosteriorEncoder(config)

        # These parameters control the synthesised speech properties
        self.speaking_rate = config.speaking_rate
        self.noise_scale = config.noise_scale
        self.noise_scale_duration = config.noise_scale_duration

        # Weight Norm Apply
        for block in self.decoder.resblocks:
            block.convs1 = nn.ModuleList([weight_norm(layer) for layer in block.convs1])
            block.convs2 = nn.ModuleList([weight_norm(layer) for layer in block.convs2])
        self.decoder.upsampler = nn.ModuleList([weight_norm(layer) for layer in self.decoder.upsampler])

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        speaker_id: Optional[int] = None,
        emotion_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[torch.FloatTensor] = None,
        **kwargs,
    ) -> Union[tuple[Any], VitsModelOutput]:
        r"""
        speaker_id (`int`, *optional*):
            Which speaker embedding to use. Only used for multispeaker models.
        emotion_id (`int`, *optional*):
            Which emotion embedding to use. Only used for multiemotion models.

        labels (`torch.FloatTensor` of shape `(batch_size, config.spectrogram_bins, sequence_length)`, *optional*):
            Float values of target spectrogram. Timesteps set to `-100.0` are ignored (masked) for the loss
            computation.

        Example:

        ```python
        >>> from transformers import VitsTokenizer, VitsModel, set_seed
        >>> import torch

        >>> tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng")
        >>> model = VitsModel.from_pretrained("facebook/mms-tts-eng")

        >>> inputs = tokenizer(text="Hello - my dog is cute", return_tensors="pt")

        >>> set_seed(555)  # make deterministic

        >>> with torch.no_grad():
        ...     outputs = model(inputs["input_ids"])
        >>> outputs.waveform.shape
        torch.Size([1, 45824])
        ```
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if labels is not None:
            raise NotImplementedError("Training of VITS is not supported yet.")

        mask_dtype = self.text_encoder.embed_tokens.weight.dtype
        if attention_mask is not None:
            input_padding_mask = attention_mask.unsqueeze(-1).to(mask_dtype)
        else:
            input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).to(mask_dtype)

        if self.config.num_speakers > 1 and speaker_id is not None and self.config.num_emotions > 1 and emotion_id is not None:
            if not 0 <= speaker_id < self.config.num_speakers:
                raise ValueError(f"Set `speaker_id` in the range 0-{self.config.num_speakers - 1}. or Set `emotion_id` in the range 0-{self.config.num_emotions - 1}.")
            if isinstance(speaker_id, int) and isinstance(emotion_id, int):
                speaker_id = torch.full(size=(1,), fill_value=speaker_id, device=self.device)
                emotion_id = torch.full(size=(1,), fill_value=emotion_id, device=self.device)
            speaker_embeddings = self.embed_speaker(speaker_id).unsqueeze(-1) + self.embed_emotion(emotion_id).unsqueeze(-1)
        else:
            speaker_embeddings = None

        text_encoder_output = self.text_encoder(
            input_ids=input_ids,
            padding_mask=input_padding_mask,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
        hidden_states = hidden_states.transpose(1, 2)
        input_padding_mask = input_padding_mask.transpose(1, 2)
        prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
        prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances

        if self.config.use_stochastic_duration_prediction:
            log_duration = self.duration_predictor(
                hidden_states,
                input_padding_mask,
                speaker_embeddings,
                reverse=True,
                noise_scale=self.noise_scale_duration,
            )
        else:
            log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)

        length_scale = 1.0 / self.speaking_rate
        duration = torch.ceil(torch.exp(log_duration) * input_padding_mask * length_scale)
        predicted_lengths = torch.clamp_min(torch.sum(duration, [1, 2]), 1).long()

        # Create a padding mask for the output lengths of shape (batch, 1, max_output_length)
        indices = torch.arange(predicted_lengths.max(), dtype=predicted_lengths.dtype, device=predicted_lengths.device)
        output_padding_mask = indices.unsqueeze(0) < predicted_lengths.unsqueeze(1)
        output_padding_mask = output_padding_mask.unsqueeze(1).to(input_padding_mask.dtype)

        # Reconstruct an attention tensor of shape (batch, 1, out_length, in_length)
        attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(output_padding_mask, -1)
        batch_size, _, output_length, input_length = attn_mask.shape
        cum_duration = torch.cumsum(duration, -1).view(batch_size * input_length, 1)
        indices = torch.arange(output_length, dtype=duration.dtype, device=duration.device)
        valid_indices = indices.unsqueeze(0) < cum_duration
        valid_indices = valid_indices.to(attn_mask.dtype).view(batch_size, input_length, output_length)
        padded_indices = valid_indices - nn.functional.pad(valid_indices, [0, 0, 1, 0, 0, 0])[:, :-1]
        attn = padded_indices.unsqueeze(1).transpose(2, 3) * attn_mask

        # Expand prior distribution
        prior_means = torch.matmul(attn.squeeze(1), prior_means).transpose(1, 2)
        prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances).transpose(1, 2)

        prior_latents = prior_means + torch.randn_like(prior_means) * torch.exp(prior_log_variances) * self.noise_scale
        latents = self.flow(prior_latents, output_padding_mask, speaker_embeddings, reverse=True)

        spectrogram = latents * output_padding_mask
        waveform = self.decoder(spectrogram, speaker_embeddings)
        waveform = waveform.squeeze(1)
        sequence_lengths = predicted_lengths * np.prod(self.config.upsample_rates)

        if not return_dict:
            outputs = (waveform, sequence_lengths, spectrogram) + text_encoder_output[3:]
            return outputs

        return VitsModelOutput(
            waveform=waveform,
            sequence_lengths=sequence_lengths,
            spectrogram=spectrogram,
            hidden_states=text_encoder_output.hidden_states,
            attentions=text_encoder_output.attentions,
        )


__all__ = ["VitsModel"]