data/tokenizer.py

#!/usr/bin/env python3
# Copyright    2023                            (authors: Feiteng Li)
#
# 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 re
from dataclasses import asdict, dataclass
from typing import Any, Dict, List, Optional, Pattern, Union

import numpy as np
import torch
import torchaudio
from encodec import EncodecModel
from encodec.utils import convert_audio

try:
    from pypinyin import Style, pinyin
    from pypinyin.style._utils import get_finals, get_initials
except Exception:
    pass


def remove_encodec_weight_norm(model):
    from encodec.modules import SConv1d
    from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock
    from torch.nn.utils import remove_weight_norm

    encoder = model.encoder.model
    for key in encoder._modules:
        if isinstance(encoder._modules[key], SEANetResnetBlock):
            remove_weight_norm(encoder._modules[key].shortcut.conv.conv)
            block_modules = encoder._modules[key].block._modules
            for skey in block_modules:
                if isinstance(block_modules[skey], SConv1d):
                    remove_weight_norm(block_modules[skey].conv.conv)
        elif isinstance(encoder._modules[key], SConv1d):
            remove_weight_norm(encoder._modules[key].conv.conv)

    decoder = model.decoder.model
    for key in decoder._modules:
        if isinstance(decoder._modules[key], SEANetResnetBlock):
            remove_weight_norm(decoder._modules[key].shortcut.conv.conv)
            block_modules = decoder._modules[key].block._modules
            for skey in block_modules:
                if isinstance(block_modules[skey], SConv1d):
                    remove_weight_norm(block_modules[skey].conv.conv)
        elif isinstance(decoder._modules[key], SConvTranspose1d):
            remove_weight_norm(decoder._modules[key].convtr.convtr)
        elif isinstance(decoder._modules[key], SConv1d):
            remove_weight_norm(decoder._modules[key].conv.conv)


class AudioTokenizer:
    """EnCodec audio."""

    def __init__(
        self,
        device: Any = None,
    ) -> None:
        # Instantiate a pretrained EnCodec model
        model = EncodecModel.encodec_model_24khz()
        model.set_target_bandwidth(6.0)
        remove_encodec_weight_norm(model)

        if not device:
            device = torch.device("cpu")
            if torch.cuda.is_available():
                device = torch.device("cuda:0")
            if torch.backends.mps.is_available():
                device = torch.device("mps")

        self._device = device

        self.codec = model.to(device)
        self.sample_rate = model.sample_rate
        self.channels = model.channels

    @property
    def device(self):
        return self._device

    def encode(self, wav: torch.Tensor) -> torch.Tensor:
        return self.codec.encode(wav.to(self.device))

    def decode(self, frames: torch.Tensor) -> torch.Tensor:
        return self.codec.decode(frames)


def tokenize_audio(tokenizer: AudioTokenizer, audio):
    # Load and pre-process the audio waveform
    if isinstance(audio, str):
        wav, sr = torchaudio.load(audio)
    else:
        wav, sr = audio
    wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels)
    wav = wav.unsqueeze(0)

    # Extract discrete codes from EnCodec
    with torch.no_grad():
        encoded_frames = tokenizer.encode(wav)
    return encoded_frames


if __name__ == "__main__":
    model = EncodecModel.encodec_model_24khz()
    model.set_target_bandwidth(6.0)

    samples = torch.from_numpy(np.random.random([4, 1, 1600])).type(
        torch.float32
    )
    codes_raw = model.encode(samples)

    remove_encodec_weight_norm(model)
    codes_norm = model.encode(samples)

    assert torch.allclose(codes_raw[0][0], codes_norm[0][0])