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108 lines
3.5 KiB
108 lines
3.5 KiB
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""DAC model inference implementation.
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This module contains the inference implementation for the DAC model.
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"""
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import os
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import logging
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import numpy as np
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from typing import Dict, List, Union
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import paddle
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from paddlespeech.audio.codec.dac.model import DACModel, EncoderModel, DecoderModel
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from paddlespeech.audio.codec.dac.processor import DACProcessor
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class DACInferencer:
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"""Inference class for DAC model."""
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def __init__(self,
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checkpoint_path,
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model_config=None,
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device=paddle.get_device()):
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"""Initialize DAC inferencer.
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Args:
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checkpoint_path (str): Path to model checkpoint
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model_config (dict, optional): Model configuration. Defaults to None.
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device (str, optional): Device to run inference on. Defaults to paddle.get_device().
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"""
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paddle.set_device(device)
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self.checkpoint_path = checkpoint_path
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self.model_config = model_config or {}
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# Initialize model and processor
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self._init_model()
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self.processor = DACProcessor(sample_rate=self.model.sample_rate)
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def _init_model(self):
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"""Initialize the DAC model from checkpoint."""
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# TODO: Implement model loading from checkpoint
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self.model = DACModel(**self.model_config)
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# Load model parameters
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if os.path.isfile(self.checkpoint_path):
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state_dict = paddle.load(self.checkpoint_path)
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self.model.set_state_dict(state_dict)
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self.model.eval()
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else:
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raise FileNotFoundError(f"Model checkpoint not found: {self.checkpoint_path}")
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def encode(self, audio, **kwargs):
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"""Encode audio to latent representation.
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Args:
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audio (numpy.ndarray): Input audio array
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Returns:
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paddle.Tensor: Encoded latent representation
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"""
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# TODO: Implement encoding logic
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pass
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def decode(self, latent, **kwargs):
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"""Decode latent representation to audio.
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Args:
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latent (paddle.Tensor): Encoded latent representation
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Returns:
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numpy.ndarray: Decoded audio
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"""
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# TODO: Implement decoding logic
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pass
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def reconstruct(self, audio, **kwargs):
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"""Reconstruct audio by encoding and decoding.
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Args:
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audio (numpy.ndarray): Input audio array
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Returns:
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numpy.ndarray: Reconstructed audio
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"""
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# Preprocess audio
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audio_tensor = self.processor.preprocess(audio)
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# Run inference
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with paddle.no_grad():
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output, _ = self.model(audio_tensor.unsqueeze(0))
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# Postprocess output
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return self.processor.postprocess(output.squeeze(0))
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