unilm/edgelm/fairseq/models/text_to_speech/vocoder.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import logging
import json
from typing import Dict

import numpy as np
import torch
from torch import nn
import torch.nn.functional as F

from fairseq.data.audio.audio_utils import (
    get_window, get_fourier_basis, get_mel_filters, TTSSpectrogram
)
from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
from fairseq.models.text_to_speech.hifigan import Generator as HiFiGANModel

logger = logging.getLogger(__name__)


class PseudoInverseMelScale(torch.nn.Module):
    def __init__(self, n_stft, n_mels, sample_rate, f_min, f_max) -> None:
        super(PseudoInverseMelScale, self).__init__()
        self.n_mels = n_mels
        basis = get_mel_filters(
            sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max
        )
        basis = torch.pinverse(basis)  # F x F_mel
        self.register_buffer('basis', basis)

    def forward(self, melspec: torch.Tensor) -> torch.Tensor:
        # pack batch
        shape = melspec.shape  # B_1 x ... x B_K x F_mel x T
        n_mels, time = shape[-2], shape[-1]
        melspec = melspec.view(-1, n_mels, time)

        freq, _ = self.basis.size()  # F x F_mel
        assert self.n_mels == n_mels, (self.n_mels, n_mels)
        specgram = self.basis.matmul(melspec).clamp(min=0)

        # unpack batch
        specgram = specgram.view(shape[:-2] + (freq, time))
        return specgram


class GriffinLim(torch.nn.Module):
    def __init__(
            self, n_fft: int, win_length: int, hop_length: int, n_iter: int,
            window_fn=torch.hann_window
    ):
        super(GriffinLim, self).__init__()
        self.transform = TTSSpectrogram(
            n_fft, win_length, hop_length, return_phase=True
        )

        basis = get_fourier_basis(n_fft)
        basis = torch.pinverse(n_fft / hop_length * basis).T[:, None, :]
        basis *= get_window(window_fn, n_fft, win_length)
        self.register_buffer('basis', basis)

        self.n_fft = n_fft
        self.win_length = win_length
        self.hop_length = hop_length
        self.n_iter = n_iter

        self.tiny = 1.1754944e-38

    @classmethod
    def get_window_sum_square(
            cls, n_frames, hop_length, win_length, n_fft,
            window_fn=torch.hann_window
    ) -> torch.Tensor:
        w_sq = get_window(window_fn, n_fft, win_length) ** 2
        n = n_fft + hop_length * (n_frames - 1)
        x = torch.zeros(n, dtype=torch.float32)
        for i in range(n_frames):
            ofst = i * hop_length
            x[ofst: min(n, ofst + n_fft)] += w_sq[:max(0, min(n_fft, n - ofst))]
        return x

    def inverse(self, magnitude: torch.Tensor, phase) -> torch.Tensor:
        x = torch.cat(
            [magnitude * torch.cos(phase), magnitude * torch.sin(phase)],
            dim=1
        )
        x = F.conv_transpose1d(x, self.basis, stride=self.hop_length)
        win_sum_sq = self.get_window_sum_square(
            magnitude.shape[-1], hop_length=self.hop_length,
            win_length=self.win_length, n_fft=self.n_fft
        ).to(magnitude.device)
        # remove modulation effects
        approx_nonzero_indices = win_sum_sq > self.tiny
        x[:, :, approx_nonzero_indices] /= win_sum_sq[approx_nonzero_indices]
        x *= self.n_fft / self.hop_length
        x = x[:, :, self.n_fft // 2:]
        x = x[:, :, :-self.n_fft // 2:]
        return x

    def forward(self, specgram: torch.Tensor) -> torch.Tensor:
        angles = np.angle(np.exp(2j * np.pi * np.random.rand(*specgram.shape)))
        angles = torch.from_numpy(angles).to(specgram)
        _specgram = specgram.view(-1, specgram.shape[-2], specgram.shape[-1])
        waveform = self.inverse(_specgram, angles).squeeze(1)
        for _ in range(self.n_iter):
            _, angles = self.transform(waveform)
            waveform = self.inverse(_specgram, angles).squeeze(1)
        return waveform.squeeze(0)


class GriffinLimVocoder(nn.Module):
    def __init__(self, sample_rate, win_size, hop_size, n_fft,
                 n_mels, f_min, f_max, window_fn,
                 spec_bwd_max_iter=32,
                 fp16=False):
        super().__init__()
        self.inv_mel_transform = PseudoInverseMelScale(
            n_stft=n_fft // 2 + 1, n_mels=n_mels, sample_rate=sample_rate,
            f_min=f_min, f_max=f_max
        )
        self.gl_transform = GriffinLim(
            n_fft=n_fft, win_length=win_size, hop_length=hop_size,
            window_fn=window_fn, n_iter=spec_bwd_max_iter
        )
        if fp16:
            self.half()
            self.inv_mel_transform.half()
            self.gl_transform.half()
        else:
            self.float()
            self.inv_mel_transform.float()
            self.gl_transform.float()

    def forward(self, x):
        # x: (B x) T x D -> (B x) 1 x T
        # NOTE: batched forward produces noisier waveform. recommend running
        # one utterance at a time
        self.eval()
        x = x.exp().transpose(-1, -2)
        x = self.inv_mel_transform(x)
        x = self.gl_transform(x)
        return x

    @classmethod
    def from_data_cfg(cls, args, data_cfg: S2TDataConfig):
        feat_cfg = data_cfg.config["features"]
        window_fn = getattr(torch, feat_cfg["window_fn"] + "_window")
        return cls(
            sample_rate=feat_cfg["sample_rate"],
            win_size=int(feat_cfg["win_len_t"] * feat_cfg["sample_rate"]),
            hop_size=int(feat_cfg["hop_len_t"] * feat_cfg["sample_rate"]),
            n_fft=feat_cfg["n_fft"], n_mels=feat_cfg["n_mels"],
            f_min=feat_cfg["f_min"], f_max=feat_cfg["f_max"],
            window_fn=window_fn, spec_bwd_max_iter=args.spec_bwd_max_iter,
            fp16=args.fp16
        )


class HiFiGANVocoder(nn.Module):
    def __init__(
            self, checkpoint_path: str, model_cfg: Dict[str, str],
            fp16: bool = False
    ) -> None:
        super().__init__()
        self.model = HiFiGANModel(model_cfg)
        state_dict = torch.load(checkpoint_path)
        self.model.load_state_dict(state_dict["generator"])
        if fp16:
            self.model.half()
        logger.info(f"loaded HiFiGAN checkpoint from {checkpoint_path}")

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # (B x) T x D -> (B x) 1 x T
        model = self.model.eval()
        if len(x.shape) == 2:
            return model(x.unsqueeze(0).transpose(1, 2)).detach().squeeze(0)
        else:
            return model(x.transpose(-1, -2)).detach()

    @classmethod
    def from_data_cfg(cls, args, data_cfg: S2TDataConfig):
        vocoder_cfg = data_cfg.vocoder
        assert vocoder_cfg.get("type", "griffin_lim") == "hifigan"
        with open(vocoder_cfg["config"]) as f:
            model_cfg = json.load(f)
        return cls(vocoder_cfg["checkpoint"], model_cfg, fp16=args.fp16)


def get_vocoder(args, data_cfg: S2TDataConfig):
    if args.vocoder == "griffin_lim":
        return GriffinLimVocoder.from_data_cfg(args, data_cfg)
    elif args.vocoder == "hifigan":
        return HiFiGANVocoder.from_data_cfg(args, data_cfg)
    else:
        raise ValueError("Unknown vocoder")