inference/svs/base_svs_infer.py

import os

import torch
import numpy as np
from modules.hifigan.hifigan import HifiGanGenerator
from vocoders.hifigan import HifiGAN
from inference.svs.opencpop.map import cpop_pinyin2ph_func

from utils import load_ckpt
from utils.hparams import set_hparams, hparams
from utils.text_encoder import TokenTextEncoder
from pypinyin import pinyin, lazy_pinyin, Style
import librosa
import glob
import re


class BaseSVSInfer:
    def __init__(self, hparams, device=None):
        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.hparams = hparams
        self.device = device

        phone_list = ["AP", "SP", "a", "ai", "an", "ang", "ao", "b", "c", "ch", "d", "e", "ei", "en", "eng", "er", "f", "g",
                  "h", "i", "ia", "ian", "iang", "iao", "ie", "in", "ing", "iong", "iu", "j", "k", "l", "m", "n", "o",
                  "ong", "ou", "p", "q", "r", "s", "sh", "t", "u", "ua", "uai", "uan", "uang", "ui", "un", "uo", "v",
                  "van", "ve", "vn", "w", "x", "y", "z", "zh"]
        self.ph_encoder = TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',')
        self.pinyin2phs = cpop_pinyin2ph_func()
        self.spk_map = {'opencpop': 0}

        self.model = self.build_model()
        self.model.eval()
        self.model.to(self.device)
        self.vocoder = self.build_vocoder()
        self.vocoder.eval()
        self.vocoder.to(self.device)

    def build_model(self):
        raise NotImplementedError

    def forward_model(self, inp):
        raise NotImplementedError

    def build_vocoder(self):
        base_dir = hparams['vocoder_ckpt']
        config_path = f'{base_dir}/config.yaml'
        ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
        lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1]
        print('| load HifiGAN: ', ckpt)
        ckpt_dict = torch.load(ckpt, map_location="cpu")
        config = set_hparams(config_path, global_hparams=False)
        state = ckpt_dict["state_dict"]["model_gen"]
        vocoder = HifiGanGenerator(config)
        vocoder.load_state_dict(state, strict=True)
        vocoder.remove_weight_norm()
        vocoder = vocoder.eval().to(self.device)
        return vocoder

    def run_vocoder(self, c, **kwargs):
        c = c.transpose(2, 1)  # [B, 80, T]
        f0 = kwargs.get('f0')  # [B, T]
        if f0 is not None and hparams.get('use_nsf'):
            # f0 = torch.FloatTensor(f0).to(self.device)
            y = self.vocoder(c, f0).view(-1)
        else:
            y = self.vocoder(c).view(-1)
            # [T]
        return y[None]

    def preprocess_word_level_input(self, inp):
        # Pypinyin can't solve polyphonic words
        text_raw = inp['text'].replace('最长', '最常').replace('长睫毛', '常睫毛') \
            .replace('那么长', '那么常').replace('多长', '多常') \
            .replace('很长', '很常')  # We hope someone could provide a better g2p module for us by opening pull requests.

        # lyric
        pinyins = lazy_pinyin(text_raw, strict=False)
        ph_per_word_lst = [self.pinyin2phs[pinyin.strip()] for pinyin in pinyins if pinyin.strip() in self.pinyin2phs]

        # Note
        note_per_word_lst = [x.strip() for x in inp['notes'].split('|') if x.strip() != '']
        mididur_per_word_lst = [x.strip() for x in inp['notes_duration'].split('|') if x.strip() != '']

        if len(note_per_word_lst) == len(ph_per_word_lst) == len(mididur_per_word_lst):
            print('Pass word-notes check.')
        else:
            print('The number of words does\'t match the number of notes\' windows. ',
                  'You should split the note(s) for each word by | mark.')
            print(ph_per_word_lst, note_per_word_lst, mididur_per_word_lst)
            print(len(ph_per_word_lst), len(note_per_word_lst), len(mididur_per_word_lst))
            return None

        note_lst = []
        ph_lst = []
        midi_dur_lst = []
        is_slur = []
        for idx, ph_per_word in enumerate(ph_per_word_lst):
            # for phs in one word:
            # single ph like ['ai']  or multiple phs like ['n', 'i']
            ph_in_this_word = ph_per_word.split()

            # for notes in one word:
            # single note like ['D4'] or multiple notes like ['D4', 'E4'] which means a 'slur' here.
            note_in_this_word = note_per_word_lst[idx].split()
            midi_dur_in_this_word = mididur_per_word_lst[idx].split()
            # process for the model input
            # Step 1.
            #  Deal with note of 'not slur' case or the first note of 'slur' case
            #  j        ie
            #  F#4/Gb4  F#4/Gb4
            #  0        0
            for ph in ph_in_this_word:
                ph_lst.append(ph)
                note_lst.append(note_in_this_word[0])
                midi_dur_lst.append(midi_dur_in_this_word[0])
                is_slur.append(0)
            # step 2.
            #  Deal with the 2nd, 3rd... notes of 'slur' case
            #  j        ie         ie
            #  F#4/Gb4  F#4/Gb4    C#4/Db4
            #  0        0          1
            if len(note_in_this_word) > 1:  # is_slur = True, we should repeat the YUNMU to match the 2nd, 3rd... notes.
                for idx in range(1, len(note_in_this_word)):
                    ph_lst.append(ph_in_this_word[1])
                    note_lst.append(note_in_this_word[idx])
                    midi_dur_lst.append(midi_dur_in_this_word[idx])
                    is_slur.append(1)
        ph_seq = ' '.join(ph_lst)

        if len(ph_lst) == len(note_lst) == len(midi_dur_lst):
            print(len(ph_lst), len(note_lst), len(midi_dur_lst))
            print('Pass word-notes check.')
        else:
            print('The number of words does\'t match the number of notes\' windows. ',
                  'You should split the note(s) for each word by | mark.')
            return None
        return ph_seq, note_lst, midi_dur_lst, is_slur

    def preprocess_phoneme_level_input(self, inp):
        ph_seq = inp['ph_seq']
        note_lst = inp['note_seq'].split()
        midi_dur_lst = inp['note_dur_seq'].split()
        is_slur = inp['is_slur_seq'].split()
        print(len(note_lst), len(ph_seq.split()), len(midi_dur_lst))
        if len(note_lst) == len(ph_seq.split()) == len(midi_dur_lst):
            print('Pass word-notes check.')
        else:
            print('The number of words does\'t match the number of notes\' windows. ',
                  'You should split the note(s) for each word by | mark.')
            return None
        return ph_seq, note_lst, midi_dur_lst, is_slur

    def preprocess_input(self, inp, input_type='word'):
        """

        :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)}
        :return:
        """

        item_name = inp.get('item_name', '<ITEM_NAME>')
        spk_name = inp.get('spk_name', 'opencpop')

        # single spk
        spk_id = self.spk_map[spk_name]

        # get ph seq, note lst, midi dur lst, is slur lst.
        if input_type == 'word':
            ret = self.preprocess_word_level_input(inp)
        elif input_type == 'phoneme':  # like transcriptions.txt in Opencpop dataset.
            ret = self.preprocess_phoneme_level_input(inp)
        else:
            print('Invalid input type.')
            return None

        if ret:
            ph_seq, note_lst, midi_dur_lst, is_slur = ret
        else:
            print('==========> Preprocess_word_level or phone_level input wrong.')
            return None

        # convert note lst to midi id; convert note dur lst to midi duration
        try:
            midis = [librosa.note_to_midi(x.split("/")[0]) if x != 'rest' else 0
                     for x in note_lst]
            midi_dur_lst = [float(x) for x in midi_dur_lst]
        except Exception as e:
            print(e)
            print('Invalid Input Type.')
            return None

        ph_token = self.ph_encoder.encode(ph_seq)
        item = {'item_name': item_name, 'text': inp['text'], 'ph': ph_seq, 'spk_id': spk_id,
                'ph_token': ph_token, 'pitch_midi': np.asarray(midis), 'midi_dur': np.asarray(midi_dur_lst),
                'is_slur': np.asarray(is_slur), }
        item['ph_len'] = len(item['ph_token'])
        return item

    def input_to_batch(self, item):
        item_names = [item['item_name']]
        text = [item['text']]
        ph = [item['ph']]
        txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device)
        txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device)
        spk_ids = torch.LongTensor(item['spk_id'])[None, :].to(self.device)

        pitch_midi = torch.LongTensor(item['pitch_midi'])[None, :hparams['max_frames']].to(self.device)
        midi_dur = torch.FloatTensor(item['midi_dur'])[None, :hparams['max_frames']].to(self.device)
        is_slur = torch.LongTensor(item['is_slur'])[None, :hparams['max_frames']].to(self.device)

        batch = {
            'item_name': item_names,
            'text': text,
            'ph': ph,
            'txt_tokens': txt_tokens,
            'txt_lengths': txt_lengths,
            'spk_ids': spk_ids,
            'pitch_midi': pitch_midi,
            'midi_dur': midi_dur,
            'is_slur': is_slur
        }
        return batch

    def postprocess_output(self, output):
        return output

    def infer_once(self, inp):
        inp = self.preprocess_input(inp, input_type=inp['input_type'] if inp.get('input_type') else 'word')
        output = self.forward_model(inp)
        output = self.postprocess_output(output)
        return output

    @classmethod
    def example_run(cls, inp):
        from utils.audio import save_wav
        set_hparams(print_hparams=False)
        infer_ins = cls(hparams)
        out = infer_ins.infer_once(inp)
        os.makedirs('infer_out', exist_ok=True)
        save_wav(out, f'infer_out/example_out.wav', hparams['audio_sample_rate'])


# if __name__ == '__main__':
    # debug
    # a = BaseSVSInfer(hparams)
    # a.preprocess_input({'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP',
    #                     'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest',
    #                     'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590'
    #                     })

    # b = {
    #     'text': '小酒窝长睫毛AP是你最美的记号',
    #     'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4',
    #     'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340'
    # }
    # c = {
    #     'text': '小酒窝长睫毛AP是你最美的记号',
    #     'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao',
    #     'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4',
    #     'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340',
    #     'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0'
    # }  # input like Opencpop dataset.
    # a.preprocess_input(b)
    # a.preprocess_input(c, input_type='phoneme')