Shokoufeh
commited on
Commit
•
2aa6704
1
Parent(s):
a2cde0e
Add custom pipeline file
Browse files- custom_pipeline.py +82 -0
custom_pipeline.py
ADDED
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import torch
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import torchaudio
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from transformers import Pipeline
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from librosa import resample
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from soundfile import write
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from sgmse.model import ScoreModel
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from sgmse.util.other import pad_spec
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class CustomSpeechEnhancementPipeline(Pipeline):
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def __init__(self, model, target_sr=16000, pad_mode="zero_pad", args=None):
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"""
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Custom pipeline for speech enhancement using ScoreModel.
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Args:
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model: The speech enhancement model loaded from a checkpoint (ScoreModel).
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target_sr: Target sample rate for the input audio (default is 16 kHz).
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pad_mode: Padding mode for spectrogram (default is "zero_pad").
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args: Parsed arguments (device, corrector, corrector_steps, snr, etc.).
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"""
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super().__init__(model=model)
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self.target_sr = target_sr
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self.pad_mode = pad_mode
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self.args = args
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def preprocess(self, audio_path):
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# Load the audio file
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y, sr = torchaudio.load(audio_path)
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# Resample if necessary
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if sr != self.target_sr:
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y = torch.tensor(resample(y.numpy(), orig_sr=sr, target_sr=self.target_sr))
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# Normalize the audio
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norm_factor = y.abs().max()
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y = y / norm_factor
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# Prepare the input for the model by transforming to the frequency domain
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Y = torch.unsqueeze(self.model._forward_transform(self.model._stft(y.to(self.args.device))), 0)
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Y = pad_spec(Y, mode=self.pad_mode)
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return Y, norm_factor, y.size(1) # Return input spec, normalization factor, and original length
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def _forward(self, model_inputs):
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Y, norm_factor, T_orig = model_inputs
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# Perform reverse sampling using the model's PC sampler
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sampler = self.model.get_pc_sampler(
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'reverse_diffusion',
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self.args.corrector,
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Y.to(self.args.device),
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N=self.args.N,
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corrector_steps=self.args.corrector_steps,
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snr=self.args.snr
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)
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# Get the enhanced speech sample
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sample, _ = sampler()
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# Convert back to time domain
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x_hat = self.model.to_audio(sample.squeeze(), T_orig)
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# Renormalize the audio
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x_hat = x_hat * norm_factor
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return x_hat
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def postprocess(self, model_outputs):
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# Convert the enhanced output back to NumPy for further processing or saving
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return model_outputs.cpu().numpy()
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def pad_spec(self, Y):
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"""
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Apply padding to the spectrogram as per the model's required padding mode.
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Args:
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Y: Input spectrogram tensor.
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Returns:
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Padded spectrogram.
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"""
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# Implement padding as per the provided mode
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return torch.nn.functional.pad(Y, (0, 0, 0, 1), mode=self.pad_mode)
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