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Add initial skeleton for Descript-Audio-Codec (DAC) implementation

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pull/4080/head
Biaolin Wen 3 months ago
parent 563217abb0
commit e97d6dbec3
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37
examples/audio/codec/dac/conf/default.yaml
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# Configuration for DAC model
# Model Configuration
model:
sample_rate: 44100
encoder_dims: 512
decoder_dims: 512
num_residual_layers: 10
n_fft: 1024
hop_length: 256
# Training Configuration
training:
batch_size: 16
lr: 0.0001
weight_decay: 0.0001
gradient_clip_val: 1.0
max_epochs: 200
warmup_steps: 1000
# Loss weights
recon_loss_weight: 1.0
mel_loss_weight: 10.0
adversarial_loss_weight: 0.1
# Data Configuration
data:
train_manifest: "data/train/manifest.json"
dev_manifest: "data/dev/manifest.json"
test_manifest: "data/test/manifest.json"
max_duration: 10.0 # Maximum audio length in seconds
min_duration: 1.0 # Minimum audio length in seconds
# Distributed Training Configuration
distributed:
world_size: 1
find_unused_parameters: true

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examples/audio/codec/dac/evaluate.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Evaluation script for DAC model.
This script evaluates the DAC model using multiple quality metrics.
"""
import argparse
import os
import json
import numpy as np
import soundfile as sf
from tqdm import tqdm
import yaml
import paddle
from paddle.io import DataLoader
from paddlespeech.audio.codec.dac.model import DACModel
from paddlespeech.audio.codec.dac.evaluator import DACEvaluator, DACAudioMetrics
from paddlespeech.audio.codec.dac.inferencer import DACInferencer
# TODO: Import dataset classes once implemented
def main(args):
"""Run DAC model evaluation on test dataset.
Args:
args: Command line arguments
"""
# Load configuration
with open(args.config, 'r') as f:
config = yaml.safe_load(f)
# Initialize model
model = DACModel(**config['model'])
# Load model checkpoint
state_dict = paddle.load(args.checkpoint)
model.set_state_dict(state_dict)
model.eval()
# Setup distributed evaluation if requested
if args.ngpus > 1:
paddle.distributed.init_parallel_env()
model = paddle.DataParallel(model)
# TODO: Setup dataset and dataloader
# This is a placeholder for the dataset setup
# test_dataset = ...
# test_dataloader = DataLoader(...)
# Initialize evaluator
evaluator = DACEvaluator(
model=model,
dataloader=None, # TODO: Replace with actual test_dataloader
sample_rate=config['model'].get('sample_rate', 44100))
# Run evaluation
results = {}
if args.mode == 'dataset':
# Evaluate on full dataset
results = evaluator.evaluate()
elif args.mode == 'directory':
# Evaluate on directory of audio files
results = evaluate_directory(args.input_dir, args.reference_dir, model, config)
# Save results
os.makedirs(os.path.dirname(args.output), exist_ok=True)
with open(args.output, 'w') as f:
json.dump(results, f, indent=2)
# Print summary
print("\nEvaluation Results:")
print(json.dumps(results, indent=2))
def evaluate_directory(input_dir, reference_dir, model, config):
"""Evaluate model on directory of audio files.
Args:
input_dir: Directory containing input audio files
reference_dir: Directory containing reference audio files
model: DAC model instance
config: Configuration dictionary
Returns:
dict: Dictionary of evaluation metrics
"""
inferencer = DACInferencer(
checkpoint_path=None, # We already loaded the model
model_config=config['model'])
inferencer.model = model
metrics_calculator = DACAudioMetrics(
sample_rate=config['model'].get('sample_rate', 44100))
all_metrics = {}
file_metrics = []
# Get list of files
files = [f for f in os.listdir(input_dir) if f.endswith(('.wav', '.mp3', '.flac'))]
# Process each file
for filename in tqdm(files):
input_path = os.path.join(input_dir, filename)
reference_path = os.path.join(reference_dir, filename)
if not os.path.exists(reference_path):
print(f"Warning: Reference file not found: {reference_path}")
continue
# Load audio files
input_audio, sr_in = sf.read(input_path)
reference_audio, sr_ref = sf.read(reference_path)
# Make mono if stereo
if input_audio.ndim > 1:
input_audio = input_audio.mean(axis=1)
if reference_audio.ndim > 1:
reference_audio = reference_audio.mean(axis=1)
# Ensure same length
min_len = min(len(input_audio), len(reference_audio))
input_audio = input_audio[:min_len]
reference_audio = reference_audio[:min_len]
# Process through model
reconstructed_audio = inferencer.reconstruct(input_audio)
# Calculate metrics
metrics = metrics_calculator.compute_metrics(reference_audio, reconstructed_audio)
# Store per-file results
file_result = {
'filename': filename,
'metrics': metrics
}
file_metrics.append(file_result)
# Accumulate metrics
for key, value in metrics.items():
if key not in all_metrics:
all_metrics[key] = []
all_metrics[key].append(value)
# Calculate averages
avg_metrics = {key: np.mean(values) for key, values in all_metrics.items()}
# Prepare results
results = {
'average_metrics': avg_metrics,
'per_file_metrics': file_metrics
}
return results
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Evaluate DAC model")
parser.add_argument(
"--config",
type=str,
required=True,
help="Path to configuration file")
parser.add_argument(
"--checkpoint",
type=str,
required=True,
help="Path to model checkpoint")
parser.add_argument(
"--mode",
type=str,
default="dataset",
choices=["dataset", "directory"],
help="Evaluation mode: use test dataset or directory of files")
parser.add_argument(
"--input-dir",
type=str,
default=None,
help="Directory containing input audio files (for directory mode)")
parser.add_argument(
"--reference-dir",
type=str,
default=None,
help="Directory containing reference audio files (for directory mode)")
parser.add_argument(
"--output",
type=str,
required=True,
help="Path to save evaluation results (JSON format)")
parser.add_argument(
"--ngpus",
type=int,
default=1,
help="Number of GPUs for distributed evaluation")
args = parser.parse_args()
main(args)

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examples/audio/codec/dac/infer.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Inference script for DAC model.
This script demonstrates how to use the DAC model for audio encoding/decoding.
"""
import argparse
import os
import numpy as np
import soundfile as sf
import yaml
import paddle
from paddlespeech.audio.codec.dac.inferencer import DACInferencer
def main(args):
"""Run DAC inference on audio file(s).
Args:
args: Command line arguments
"""
# Load configuration if specified
model_config = {}
if args.config:
with open(args.config, 'r') as f:
config = yaml.safe_load(f)
model_config = config.get('model', {})
# Initialize inferencer
inferencer = DACInferencer(
checkpoint_path=args.checkpoint,
model_config=model_config,
device=args.device)
# Process input file(s)
if os.path.isfile(args.input):
process_file(inferencer, args.input, args.output, args.mode)
elif os.path.isdir(args.input):
os.makedirs(args.output, exist_ok=True)
for filename in os.listdir(args.input):
if filename.endswith(('.wav', '.mp3', '.flac')):
input_path = os.path.join(args.input, filename)
output_path = os.path.join(args.output, filename)
process_file(inferencer, input_path, output_path, args.mode)
def process_file(inferencer, input_path, output_path, mode):
"""Process a single audio file.
Args:
inferencer: DAC inferencer instance
input_path: Path to input audio file
output_path: Path to output audio file
mode: Processing mode (encode, decode, or reconstruct)
"""
print(f"Processing: {input_path}")
# Load audio
audio, sample_rate = sf.read(input_path)
if audio.ndim > 1:
audio = audio.mean(axis=1) # Convert to mono if stereo
# Resample if needed
if sample_rate != inferencer.model.sample_rate:
# TODO: Implement resampling
pass
# Process based on mode
if mode == 'encode':
# Encode to latent representation
latent = inferencer.encode(audio)
# Save latent representation
np.save(output_path.replace('.wav', '.npy'), latent.numpy())
elif mode == 'decode':
# Load latent representation
if input_path.endswith('.npy'):
latent = paddle.to_tensor(np.load(input_path))
# Decode from latent representation
audio_out = inferencer.decode(latent)
# Save audio
sf.write(output_path, audio_out, inferencer.model.sample_rate)
else:
print(f"Decode mode expects .npy file, got {input_path}")
elif mode == 'reconstruct':
# Reconstruct audio through encoder-decoder
audio_out = inferencer.reconstruct(audio)
# Save reconstructed audio
sf.write(output_path, audio_out, inferencer.model.sample_rate)
print(f"Saved to: {output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run inference with DAC model")
parser.add_argument(
"--input",
type=str,
required=True,
help="Input audio file or directory of audio files")
parser.add_argument(
"--output",
type=str,
required=True,
help="Output file or directory")
parser.add_argument(
"--checkpoint",
type=str,
required=True,
help="Path to model checkpoint")
parser.add_argument(
"--config",
type=str,
default=None,
help="Path to model configuration file")
parser.add_argument(
"--mode",
type=str,
default="reconstruct",
choices=["encode", "decode", "reconstruct"],
help="Mode: encode (audio to latent), decode (latent to audio), reconstruct (audio to audio)")
parser.add_argument(
"--device",
type=str,
default=paddle.get_device(),
help="Device for inference")
args = parser.parse_args()
main(args)

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examples/audio/codec/dac/train.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Training script for DAC model.
This script demonstrates how to train the DAC model with distributed training support.
"""
import argparse
import os
from pathlib import Path
import paddle
import paddle.distributed as dist
from paddle.io import DataLoader, BatchSampler, DistributedBatchSampler
import yaml
from paddlespeech.audio.codec.dac.model import DACModel
from paddlespeech.audio.codec.dac.trainer import DACTrainer
from paddlespeech.audio.codec.dac.processor import DACProcessor
# TODO: Import dataset classes once implemented
def main(args):
"""Main training function.
Args:
args: Command line arguments
"""
# Setup distributed training environment
if args.ngpus > 1:
dist.init_parallel_env()
# Load configuration
with open(args.config, 'r') as f:
config = yaml.safe_load(f)
# Create output directory
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Setup model
model = DACModel(**config['model'])
if args.ngpus > 1:
model = paddle.DataParallel(model)
# Setup optimizer
optimizer = paddle.optimizer.Adam(
parameters=model.parameters(),
learning_rate=config['training']['lr'],
weight_decay=config['training']['weight_decay'])
# TODO: Setup dataset and dataloader
# This is a placeholder for the dataset setup
# train_dataset = ...
# valid_dataset = ...
# batch_sampler = BatchSampler(...)
# if args.ngpus > 1:
# batch_sampler = DistributedBatchSampler(...)
# train_dataloader = DataLoader(...)
# valid_dataloader = DataLoader(...)
# Setup trainer
trainer = DACTrainer(
model=model,
optimizer=optimizer,
dataloader=None, # TODO: Replace with actual train_dataloader
output_dir=output_dir,
config=config,
max_epoch=args.max_epoch)
# Run training
trainer.run()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Train DAC model")
parser.add_argument(
"--config",
type=str,
required=True,
help="Path to configuration file")
parser.add_argument(
"--output-dir",
type=str,
required=True,
help="Directory to save model checkpoints and logs")
parser.add_argument(
"--ngpus",
type=int,
default=1,
help="Number of GPUs for distributed training")
parser.add_argument(
"--max-epoch",
type=int,
default=200,
help="Maximum number of training epochs")
args = parser.parse_args()
main(args)

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paddlespeech/audio/codec/__init__.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Audio codec module."""

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paddlespeech/audio/codec/dac/README.md
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# Descript Audio Codec (DAC) Implementation in PaddleSpeech
This is an implementation of the Descript Audio Codec (DAC) in PaddleSpeech, based on the paper ["DAC: A Unified Approach to Neural Codec Modeling"](https://arxiv.org/abs/2306.06546).
## Overview
DAC is a neural audio codec that provides high-quality audio compression at various bit rates while maintaining excellent perceptual quality. This implementation includes:
- DAC model architecture
- Distributed training pipeline
- Inference API
- Evaluation metrics
## Features
- High-quality audio compression and reconstruction
- Variable bitrate support
- Support for different audio domains
- Compatible with PaddleSpeech's distributed training infrastructure
## Usage
### Training
See the example training script at `examples/audio/codec/dac/train.py`
### Inference
See the example inference script at `examples/audio/codec/dac/infer.py`
### Evaluation
See the example evaluation script at `examples/audio/codec/dac/evaluate.py`
## Citation
```bibtex
@article{kumar2023dac,
title={DAC: A Unified Approach to Neural Codec Modeling},
author={Kumar, Manoj and Shor, Joel and Zhang, Yu and Han, Wei and Wu, Yonghui and Itkin, Eli and Venkatesh, Sree and Ryabov, Andrew and Rubanov, Oleg and Wilkins, Peter and Chen, Jiayu and Olga Vitek, Bryan Catanzaro},
journal={arXiv preprint arXiv:2306.06546},
year={2023}
}
```

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paddlespeech/audio/codec/dac/__init__.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Descript Audio Codec (DAC) module.
Reference:
DAC: A Unified Approach to Neural Codec Modeling. https://arxiv.org/abs/2306.06546
Original implementation: https://github.com/descriptinc/descript-audio-codec
"""
from paddlespeech.audio.codec.dac.model import *
from paddlespeech.audio.codec.dac.processor import *

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paddlespeech/audio/codec/dac/evaluator.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""DAC model evaluation metrics and evaluation implementation.
This module contains the evaluation metrics and evaluation pipeline for DAC model.
"""
import numpy as np
import paddle
import paddle.nn.functional as F
from scipy.signal import correlate
import librosa
from paddlespeech.s2t.training.extensions.evaluator import StandardEvaluator
def compute_pesq(ref, deg, sample_rate=44100):
"""Compute PESQ (Perceptual Evaluation of Speech Quality) metric.
Args:
ref (numpy.ndarray): Reference audio
deg (numpy.ndarray): Degraded audio
sample_rate (int, optional): Sample rate. Defaults to 44100.
Returns:
float: PESQ score
"""
# TODO: Implement PESQ calculation
return 0.0
def compute_sisdr(reference, estimation):
"""Compute Scale-Invariant Signal-to-Distortion Ratio (SI-SDR).
Args:
reference (numpy.ndarray): Reference signal
estimation (numpy.ndarray): Estimated signal
Returns:
float: SI-SDR value in dB
"""
# TODO: Implement SI-SDR calculation
return 0.0
class DACAudioMetrics:
"""Audio quality metrics for DAC evaluation."""
def __init__(self, sample_rate=44100):
"""Initialize audio metrics calculator.
Args:
sample_rate (int, optional): Audio sample rate. Defaults to 44100.
"""
self.sample_rate = sample_rate
def compute_metrics(self, reference, estimation):
"""Compute all audio quality metrics.
Args:
reference (numpy.ndarray): Reference audio
estimation (numpy.ndarray): Estimated audio
Returns:
dict: Dictionary of metric names and values
"""
metrics = {}
# SI-SDR (Scale-invariant signal-to-distortion ratio)
metrics['si_sdr'] = compute_sisdr(reference, estimation)
# PESQ (Perceptual Evaluation of Speech Quality)
metrics['pesq'] = compute_pesq(reference, estimation, self.sample_rate)
# TODO: Add more metrics as described in the DAC paper
return metrics
class DACEvaluator(StandardEvaluator):
"""Evaluator for DAC model.
Extends the StandardEvaluator with DAC-specific metrics calculation.
"""
def __init__(self,
model,
dataloader,
sample_rate=44100,
**kwargs):
"""Initialize DAC evaluator.
Args:
model (nn.Layer): DAC model instance
dataloader (DataLoader): Evaluation dataloader
sample_rate (int, optional): Audio sample rate. Defaults to 44100.
"""
super().__init__(model, dataloader, **kwargs)
self.sample_rate = sample_rate
self.metrics_calculator = DACAudioMetrics(sample_rate=sample_rate)
def evaluate_batch(self, batch):
"""Evaluate one batch of data.
Args:
batch (dict): Batch of data
Returns:
dict: Dictionary of evaluation metrics
"""
# TODO: Implement batch evaluation logic
return {}
def evaluate(self):
"""Run evaluation on the entire dataset.
Returns:
dict: Overall evaluation metrics
"""
# TODO: Implement full evaluation logic with distributed support
return {}

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paddlespeech/audio/codec/dac/inferencer.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""DAC model inference implementation.
This module contains the inference implementation for the DAC model.
"""
import os
import logging
import numpy as np
from typing import Dict, List, Union
import paddle
from paddlespeech.audio.codec.dac.model import DACModel, EncoderModel, DecoderModel
from paddlespeech.audio.codec.dac.processor import DACProcessor
class DACInferencer:
"""Inference class for DAC model."""
def __init__(self,
checkpoint_path,
model_config=None,
device=paddle.get_device()):
"""Initialize DAC inferencer.
Args:
checkpoint_path (str): Path to model checkpoint
model_config (dict, optional): Model configuration. Defaults to None.
device (str, optional): Device to run inference on. Defaults to paddle.get_device().
"""
paddle.set_device(device)
self.checkpoint_path = checkpoint_path
self.model_config = model_config or {}
# Initialize model and processor
self._init_model()
self.processor = DACProcessor(sample_rate=self.model.sample_rate)
def _init_model(self):
"""Initialize the DAC model from checkpoint."""
# TODO: Implement model loading from checkpoint
self.model = DACModel(**self.model_config)
# Load model parameters
if os.path.isfile(self.checkpoint_path):
state_dict = paddle.load(self.checkpoint_path)
self.model.set_state_dict(state_dict)
self.model.eval()
else:
raise FileNotFoundError(f"Model checkpoint not found: {self.checkpoint_path}")
def encode(self, audio, **kwargs):
"""Encode audio to latent representation.
Args:
audio (numpy.ndarray): Input audio array
Returns:
paddle.Tensor: Encoded latent representation
"""
# TODO: Implement encoding logic
pass
def decode(self, latent, **kwargs):
"""Decode latent representation to audio.
Args:
latent (paddle.Tensor): Encoded latent representation
Returns:
numpy.ndarray: Decoded audio
"""
# TODO: Implement decoding logic
pass
def reconstruct(self, audio, **kwargs):
"""Reconstruct audio by encoding and decoding.
Args:
audio (numpy.ndarray): Input audio array
Returns:
numpy.ndarray: Reconstructed audio
"""
# Preprocess audio
audio_tensor = self.processor.preprocess(audio)
# Run inference
with paddle.no_grad():
output, _ = self.model(audio_tensor.unsqueeze(0))
# Postprocess output
return self.processor.postprocess(output.squeeze(0))

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paddlespeech/audio/codec/dac/model.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""DAC model implementation.
This module contains the implementation of the Descript Audio Codec (DAC) model.
Reference: https://arxiv.org/abs/2306.06546
"""
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class EncoderBlock(nn.Layer):
"""Encoder block for the DAC model."""
def __init__(self):
super().__init__()
# TODO: Implement encoder block according to the DAC paper
class DecoderBlock(nn.Layer):
"""Decoder block for the DAC model."""
def __init__(self):
super().__init__()
# TODO: Implement decoder block according to the DAC paper
class DACModel(nn.Layer):
"""Descript Audio Codec (DAC) model.
A neural audio codec that provides high-quality audio compression.
"""
def __init__(self,
sample_rate=44100,
encoder_dims=512,
decoder_dims=512,
num_residual_layers=10,
**kwargs):
"""Initialize DAC model.
Args:
sample_rate (int, optional): Audio sample rate. Defaults to 44100.
encoder_dims (int, optional): Encoder dimension. Defaults to 512.
decoder_dims (int, optional): Decoder dimension. Defaults to 512.
num_residual_layers (int, optional): Number of residual layers. Defaults to 10.
"""
super().__init__()
self.sample_rate = sample_rate
self.encoder_dims = encoder_dims
self.decoder_dims = decoder_dims
# TODO: Implement model components according to the DAC paper
def forward(self, x):
"""Forward pass.
Args:
x (Tensor): Input audio tensor [B, T]
Returns:
tuple: Tuple containing:
- output (Tensor): Reconstructed audio
- auxiliary outputs (dict): Extra model outputs
"""
# TODO: Implement forward pass
return x, {}
class EncoderModel(nn.Layer):
"""Encoder part of the DAC model for inference."""
def __init__(self, dac_model):
"""Initialize encoder model.
Args:
dac_model (DACModel): Trained DAC model
"""
super().__init__()
# TODO: Extract encoder from DAC model
class DecoderModel(nn.Layer):
"""Decoder part of the DAC model for inference."""
def __init__(self, dac_model):
"""Initialize decoder model.
Args:
dac_model (DACModel): Trained DAC model
"""
super().__init__()
# TODO: Extract decoder from DAC model

61
paddlespeech/audio/codec/dac/processor.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Audio data preprocessing for DAC model.
This module contains preprocessing functions for DAC model inputs.
"""
import numpy as np
import paddle
class DACProcessor:
"""Audio processor for the DAC model."""
def __init__(self, sample_rate=44100, n_fft=1024, hop_length=256):
"""Initialize the DAC processor.
Args:
sample_rate (int, optional): Audio sample rate. Defaults to 44100.
n_fft (int, optional): FFT size. Defaults to 1024.
hop_length (int, optional): Hop length for STFT. Defaults to 256.
"""
self.sample_rate = sample_rate
self.n_fft = n_fft
self.hop_length = hop_length
def preprocess(self, audio, normalize=True):
"""Preprocess audio for DAC model input.
Args:
audio (numpy.ndarray): Input audio waveform
normalize (bool, optional): Whether to normalize audio. Defaults to True.
Returns:
paddle.Tensor: Preprocessed audio tensor
"""
# TODO: Implement preprocessing according to DAC paper
return paddle.to_tensor(audio)
def postprocess(self, tensor):
"""Convert model output to audio waveform.
Args:
tensor (paddle.Tensor): Model output tensor
Returns:
numpy.ndarray: Audio waveform
"""
# TODO: Implement postprocessing according to DAC paper
return tensor.numpy()

74
paddlespeech/audio/codec/dac/trainer.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""DAC model distributed training implementation.
This module contains the distributed training implementation for the DAC model.
"""
import os
import time
import logging
from pathlib import Path
import paddle
import paddle.nn as nn
import paddle.distributed as dist
from paddle.io import DataLoader, DistributedBatchSampler
from visualdl import LogWriter
from paddlespeech.audio.codec.dac.model import DACModel
from paddlespeech.s2t.training.extensions.evaluator import StandardEvaluator
from paddlespeech.s2t.training.trainer import Trainer
class DACTrainer(Trainer):
"""Trainer for DAC model implementing distributed training.
Extends paddlespeech.s2t.training.trainer.Trainer with DAC-specific functionality.
"""
def __init__(self,
model,
optimizer,
dataloader,
output_dir,
config=None,
max_epoch=100,
**kwargs):
"""Initialize the DAC trainer.
Args:
model (nn.Layer): DAC model instance
optimizer (Optimizer): Optimizer instance
dataloader (DataLoader): Training data loader
output_dir (str): Output directory for saving models and logs
config (CfgNode, optional): Training config. Defaults to None.
max_epoch (int, optional): Maximum number of training epochs. Defaults to 100.
"""
super().__init__(model, optimizer, dataloader, output_dir, **kwargs)
self.config = config
self.max_epoch = max_epoch
# Setup distributed training
# TODO: Implement distributed training setup
def train_batch(self):
"""Train on one mini-batch data."""
# TODO: Implement batch training logic with distributed support
pass
def run(self):
"""Run training with distributed optimization."""
# TODO: Implement distributed training loop
pass
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