Preprocessing/EnCodecAudioPreprocessor.py

from collections import OrderedDict

import torch
from torchaudio.transforms import Resample

from Preprocessing.Codec.encodec import EnCodec


class CodecAudioPreprocessor:

    def __init__(self, input_sr, output_sr=16000, device="cpu", path_to_model="Preprocessing/Codec/encodec_16k_320d.pt"):
        self.device = device
        self.input_sr = input_sr
        self.output_sr = output_sr
        self.resample = Resample(orig_freq=input_sr, new_freq=output_sr).to(self.device)
        self.model = EnCodec(n_filters=32, D=512)
        parameter_dict = torch.load(path_to_model, map_location="cpu")
        new_state_dict = OrderedDict()
        for k, v in parameter_dict.items():
            name = k[7:]
            new_state_dict[name] = v
        self.model.load_state_dict(new_state_dict)
        remove_encodec_weight_norm(self.model)
        self.model.eval()
        self.model.to(device)

    def resample_audio(self, audio, current_sampling_rate):
        if current_sampling_rate != self.input_sr:
            print("warning, change in sampling rate detected. If this happens too often, consider re-ordering the audios so that the sampling rate stays constant for multiple samples")
            self.resample = Resample(orig_freq=current_sampling_rate, new_freq=self.output_sr).to(self.device)
            self.input_sr = current_sampling_rate
        if type(audio) != torch.tensor and type(audio) != torch.Tensor:
            audio = torch.tensor(audio, device=self.device, dtype=torch.float32)
        audio = self.resample(audio.float().to(self.device))
        return audio

    @torch.inference_mode()
    def audio_to_codebook_indexes(self, audio, current_sampling_rate):
        if current_sampling_rate != self.output_sr:
            audio = self.resample_audio(audio, current_sampling_rate)
        elif type(audio) != torch.tensor and type(audio) != torch.Tensor:
            audio = torch.tensor(audio, device=self.device, dtype=torch.float32)
        return self.model.encode(audio.float().unsqueeze(0).unsqueeze(0).to(self.device)).squeeze()

    @torch.inference_mode()
    def indexes_to_audio(self, codebook_indexes):
        return self.model.decode(codebook_indexes).squeeze()


def remove_encodec_weight_norm(model):
    from Preprocessing.Codec.seanet import SConv1d
    from Preprocessing.Codec.seanet import SConvTranspose1d
    from Preprocessing.Codec.seanet import 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)


if __name__ == '__main__':
    import soundfile

    import time

    with torch.inference_mode():
        test_audio1 = "../audios/ad01_0000.wav"
        test_audio2 = "../audios/angry.wav"
        test_audio3 = "../audios/ry.wav"
        test_audio4 = "../audios/test.wav"
        ap = CodecAudioPreprocessor(input_sr=1, path_to_model="Codec/encodec_16k_320d.pt")

        wav, sr = soundfile.read(test_audio1)
        indexes_1 = ap.audio_to_codebook_indexes(wav, current_sampling_rate=sr)
        wav, sr = soundfile.read(test_audio2)
        indexes_2 = ap.audio_to_codebook_indexes(wav, current_sampling_rate=sr)
        wav, sr = soundfile.read(test_audio3)
        indexes_3 = ap.audio_to_codebook_indexes(wav, current_sampling_rate=sr)
        wav, sr = soundfile.read(test_audio4)
        indexes_4 = ap.audio_to_codebook_indexes(wav, current_sampling_rate=sr)

        print(indexes_4)

        t0 = time.time()

        audio1 = ap.indexes_to_audio(indexes_1)
        audio2 = ap.indexes_to_audio(indexes_2)
        audio3 = ap.indexes_to_audio(indexes_3)
        audio4 = ap.indexes_to_audio(indexes_4)

        t1 = time.time()

        print(audio1.shape)
        print(audio2.shape)
        print(audio3.shape)
        print(audio4.shape)

        print(t1 - t0)
        soundfile.write(file=f"../audios/1_reconstructed_in_{t1 - t0}_encodec.wav", data=audio1, samplerate=16000)
        soundfile.write(file=f"../audios/2_reconstructed_in_{t1 - t0}_encodec.wav", data=audio2, samplerate=16000)
        soundfile.write(file=f"../audios/3_reconstructed_in_{t1 - t0}_encodec.wav", data=audio3, samplerate=16000)
        soundfile.write(file=f"../audios/4_reconstructed_in_{t1 - t0}_encodec.wav", data=audio4, samplerate=16000)