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#!/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
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")
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])
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