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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This code is modified from
# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/tokenizer.py
import re
from typing import Any, Dict, List, Optional, Pattern, Union
import torch
import torchaudio
from encodec import EncodecModel
from encodec.utils import convert_audio
class AudioTokenizer:
"""EnCodec audio tokenizer for encoding and decoding audio.
Attributes:
device: The device on which the codec model is loaded.
codec: The pretrained EnCodec model.
sample_rate: Sample rate of the model.
channels: Number of audio channels in the model.
"""
def __init__(self, device: Any = None) -> None:
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:
"""Encode the audio waveform.
Args:
wav: A tensor representing the audio waveform.
Returns:
A tensor representing the encoded audio.
"""
return self.codec.encode(wav.to(self.device))
def decode(self, frames: torch.Tensor) -> torch.Tensor:
"""Decode the encoded audio frames.
Args:
frames: A tensor representing the encoded audio frames.
Returns:
A tensor representing the decoded audio waveform.
"""
return self.codec.decode(frames)
def tokenize_audio(tokenizer: AudioTokenizer, audio_path: str):
"""
Tokenize the audio waveform using the given AudioTokenizer.
Args:
tokenizer: An instance of AudioTokenizer.
audio_path: Path to the audio file.
Returns:
A tensor of encoded frames from the audio.
Raises:
FileNotFoundError: If the audio file is not found.
RuntimeError: If there's an error processing the audio data.
"""
# try:
# Load and preprocess the audio waveform
wav, sr = torchaudio.load(audio_path)
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
# except FileNotFoundError:
# raise FileNotFoundError(f"Audio file not found at {audio_path}")
# except Exception as e:
# raise RuntimeError(f"Error processing audio data: {e}")
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)
def extract_encodec_token(wav_path):
model = EncodecModel.encodec_model_24khz()
model.set_target_bandwidth(6.0)
wav, sr = torchaudio.load(wav_path)
wav = convert_audio(wav, sr, model.sample_rate, model.channels)
wav = wav.unsqueeze(0)
if torch.cuda.is_available():
model = model.cuda()
wav = wav.cuda()
with torch.no_grad():
encoded_frames = model.encode(wav)
codes_ = torch.cat(
[encoded[0] for encoded in encoded_frames], dim=-1
) # [B, n_q, T]
codes = codes_.cpu().numpy()[0, :, :].T # [T, 8]
return codes
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