init
This commit is contained in:
smthemex
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import math
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Union
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import numpy as np
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import torch
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import tqdm
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from audiotools import AudioSignal
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from torch import nn
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SUPPORTED_VERSIONS = ["1.0.0"]
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@dataclass
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class DACFile:
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codes: torch.Tensor
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# Metadata
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chunk_length: int
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original_length: int
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input_db: float
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channels: int
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sample_rate: int
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padding: bool
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dac_version: str
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def save(self, path):
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artifacts = {
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"codes": self.codes.numpy().astype(np.uint16),
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"metadata": {
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"input_db": self.input_db.numpy().astype(np.float32),
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"original_length": self.original_length,
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"sample_rate": self.sample_rate,
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"chunk_length": self.chunk_length,
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"channels": self.channels,
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"padding": self.padding,
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"dac_version": SUPPORTED_VERSIONS[-1],
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},
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}
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path = Path(path).with_suffix(".dac")
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with open(path, "wb") as f:
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np.save(f, artifacts)
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return path
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@classmethod
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def load(cls, path):
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artifacts = np.load(path, allow_pickle=True)[()]
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codes = torch.from_numpy(artifacts["codes"].astype(int))
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if artifacts["metadata"].get("dac_version", None) not in SUPPORTED_VERSIONS:
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raise RuntimeError(
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f"Given file {path} can't be loaded with this version of descript-audio-codec."
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)
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return cls(codes=codes, **artifacts["metadata"])
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class CodecMixin:
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@property
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def padding(self):
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if not hasattr(self, "_padding"):
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self._padding = True
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return self._padding
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@padding.setter
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def padding(self, value):
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assert isinstance(value, bool)
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layers = [
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l for l in self.modules() if isinstance(l, (nn.Conv1d, nn.ConvTranspose1d))
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]
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for layer in layers:
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if value:
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if hasattr(layer, "original_padding"):
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layer.padding = layer.original_padding
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else:
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layer.original_padding = layer.padding
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layer.padding = tuple(0 for _ in range(len(layer.padding)))
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self._padding = value
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def get_delay(self):
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# Any number works here, delay is invariant to input length
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l_out = self.get_output_length(0)
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L = l_out
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layers = []
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for layer in self.modules():
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if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
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layers.append(layer)
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for layer in reversed(layers):
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d = layer.dilation[0]
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k = layer.kernel_size[0]
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s = layer.stride[0]
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if isinstance(layer, nn.ConvTranspose1d):
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L = ((L - d * (k - 1) - 1) / s) + 1
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elif isinstance(layer, nn.Conv1d):
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L = (L - 1) * s + d * (k - 1) + 1
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L = math.ceil(L)
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l_in = L
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return (l_in - l_out) // 2
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def get_output_length(self, input_length):
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L = input_length
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# Calculate output length
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for layer in self.modules():
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if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
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d = layer.dilation[0]
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k = layer.kernel_size[0]
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s = layer.stride[0]
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if isinstance(layer, nn.Conv1d):
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L = ((L - d * (k - 1) - 1) / s) + 1
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elif isinstance(layer, nn.ConvTranspose1d):
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L = (L - 1) * s + d * (k - 1) + 1
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L = math.floor(L)
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return L
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@torch.no_grad()
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def compress(
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self,
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audio_path_or_signal: Union[str, Path, AudioSignal],
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win_duration: float = 1.0,
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verbose: bool = False,
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normalize_db: float = -16,
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n_quantizers: int = None,
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) -> DACFile:
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"""Processes an audio signal from a file or AudioSignal object into
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discrete codes. This function processes the signal in short windows,
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using constant GPU memory.
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Parameters
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----------
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audio_path_or_signal : Union[str, Path, AudioSignal]
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audio signal to reconstruct
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win_duration : float, optional
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window duration in seconds, by default 5.0
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verbose : bool, optional
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by default False
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normalize_db : float, optional
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normalize db, by default -16
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Returns
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-------
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DACFile
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Object containing compressed codes and metadata
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required for decompression
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"""
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audio_signal = audio_path_or_signal
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if isinstance(audio_signal, (str, Path)):
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audio_signal = AudioSignal.load_from_file_with_ffmpeg(str(audio_signal))
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self.eval()
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original_padding = self.padding
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original_device = audio_signal.device
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audio_signal = audio_signal.clone()
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original_sr = audio_signal.sample_rate
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resample_fn = audio_signal.resample
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loudness_fn = audio_signal.loudness
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# If audio is > 10 minutes long, use the ffmpeg versions
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if audio_signal.signal_duration >= 10 * 60 * 60:
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resample_fn = audio_signal.ffmpeg_resample
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loudness_fn = audio_signal.ffmpeg_loudness
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original_length = audio_signal.signal_length
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resample_fn(self.sample_rate)
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input_db = loudness_fn()
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if normalize_db is not None:
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audio_signal.normalize(normalize_db)
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audio_signal.ensure_max_of_audio()
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nb, nac, nt = audio_signal.audio_data.shape
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audio_signal.audio_data = audio_signal.audio_data.reshape(nb * nac, 1, nt)
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win_duration = (
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audio_signal.signal_duration if win_duration is None else win_duration
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)
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if audio_signal.signal_duration <= win_duration:
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# Unchunked compression (used if signal length < win duration)
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self.padding = True
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n_samples = nt
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hop = nt
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else:
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# Chunked inference
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self.padding = False
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# Zero-pad signal on either side by the delay
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audio_signal.zero_pad(self.delay, self.delay)
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n_samples = int(win_duration * self.sample_rate)
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# Round n_samples to nearest hop length multiple
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n_samples = int(math.ceil(n_samples / self.hop_length) * self.hop_length)
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hop = self.get_output_length(n_samples)
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codes = []
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range_fn = range if not verbose else tqdm.trange
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for i in range_fn(0, nt, hop):
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x = audio_signal[..., i : i + n_samples]
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x = x.zero_pad(0, max(0, n_samples - x.shape[-1]))
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audio_data = x.audio_data.to(self.device)
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audio_data = self.preprocess(audio_data, self.sample_rate)
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_, c, _, _, _ = self.encode(audio_data, n_quantizers)
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codes.append(c.to(original_device))
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chunk_length = c.shape[-1]
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codes = torch.cat(codes, dim=-1)
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dac_file = DACFile(
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codes=codes,
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chunk_length=chunk_length,
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original_length=original_length,
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input_db=input_db,
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channels=nac,
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sample_rate=original_sr,
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padding=self.padding,
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dac_version=SUPPORTED_VERSIONS[-1],
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)
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if n_quantizers is not None:
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codes = codes[:, :n_quantizers, :]
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self.padding = original_padding
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return dac_file
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@torch.no_grad()
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def decompress(
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self,
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obj: Union[str, Path, DACFile],
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verbose: bool = False,
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) -> AudioSignal:
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"""Reconstruct audio from a given .dac file
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Parameters
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----------
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obj : Union[str, Path, DACFile]
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.dac file location or corresponding DACFile object.
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verbose : bool, optional
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Prints progress if True, by default False
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Returns
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-------
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AudioSignal
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Object with the reconstructed audio
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"""
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self.eval()
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if isinstance(obj, (str, Path)):
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obj = DACFile.load(obj)
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original_padding = self.padding
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self.padding = obj.padding
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range_fn = range if not verbose else tqdm.trange
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codes = obj.codes
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original_device = codes.device
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chunk_length = obj.chunk_length
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recons = []
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for i in range_fn(0, codes.shape[-1], chunk_length):
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c = codes[..., i : i + chunk_length].to(self.device)
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z = self.quantizer.from_codes(c)[0]
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r = self.decode(z)
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recons.append(r.to(original_device))
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recons = torch.cat(recons, dim=-1)
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recons = AudioSignal(recons, self.sample_rate)
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resample_fn = recons.resample
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loudness_fn = recons.loudness
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# If audio is > 10 minutes long, use the ffmpeg versions
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if recons.signal_duration >= 10 * 60 * 60:
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resample_fn = recons.ffmpeg_resample
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loudness_fn = recons.ffmpeg_loudness
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recons.normalize(obj.input_db)
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resample_fn(obj.sample_rate)
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recons = recons[..., : obj.original_length]
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loudness_fn()
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recons.audio_data = recons.audio_data.reshape(
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-1, obj.channels, obj.original_length
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)
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self.padding = original_padding
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return recons
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@@ -0,0 +1,369 @@
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import math
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from typing import List
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from typing import Union
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import numpy as np
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import torch
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from audiotools import AudioSignal
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from audiotools.ml import BaseModel
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from torch import nn
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from .base import CodecMixin
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from dac.nn.layers import Snake1d
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from dac.nn.layers import WNConv1d
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from dac.nn.layers import WNConvTranspose1d
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from dac.nn.quantize import ResidualVectorQuantize
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def init_weights(m):
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if isinstance(m, nn.Conv1d):
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nn.init.trunc_normal_(m.weight, std=0.02)
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nn.init.constant_(m.bias, 0)
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class ResidualUnit(nn.Module):
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def __init__(self, dim: int = 16, dilation: int = 1):
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super().__init__()
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pad = ((7 - 1) * dilation) // 2
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self.block = nn.Sequential(
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Snake1d(dim),
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WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
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Snake1d(dim),
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WNConv1d(dim, dim, kernel_size=1),
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)
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def forward(self, x):
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y = self.block(x)
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pad = (x.shape[-1] - y.shape[-1]) // 2
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if pad > 0:
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x = x[..., pad:-pad]
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return x + y
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class EncoderBlock(nn.Module):
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def __init__(self, dim: int = 16, stride: int = 1):
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super().__init__()
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self.block = nn.Sequential(
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ResidualUnit(dim // 2, dilation=1),
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ResidualUnit(dim // 2, dilation=3),
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ResidualUnit(dim // 2, dilation=9),
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Snake1d(dim // 2),
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WNConv1d(
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dim // 2,
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dim,
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kernel_size=2 * stride,
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stride=stride,
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padding=math.ceil(stride / 2),
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),
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)
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def forward(self, x):
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return self.block(x)
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class Encoder(nn.Module):
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def __init__(
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self,
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d_model: int = 64,
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strides: list = [2, 4, 8, 8],
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d_latent: int = 256,
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):
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super().__init__()
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# Create first convolution
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self.block = [WNConv1d(1, d_model, kernel_size=7, padding=3)]
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# Create EncoderBlocks that double channels as they downsample by `stride`
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for stride in strides:
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d_model *= 2
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self.block += [EncoderBlock(d_model, stride=stride)]
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# Create last convolution
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self.block += [
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Snake1d(d_model),
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WNConv1d(d_model, d_latent, kernel_size=3, padding=1),
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]
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# Wrap black into nn.Sequential
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self.block = nn.Sequential(*self.block)
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self.enc_dim = d_model
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def forward(self, x):
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return self.block(x)
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class DecoderBlock(nn.Module):
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def __init__(self, input_dim: int = 16, output_dim: int = 8, stride: int = 1,out_pad=0):
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super().__init__()
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self.block = nn.Sequential(
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Snake1d(input_dim),
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WNConvTranspose1d(
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input_dim,
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output_dim,
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kernel_size=2 * stride,
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stride=stride,
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padding=math.ceil(stride / 2),
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output_padding=out_pad,
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),
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ResidualUnit(output_dim, dilation=1),
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ResidualUnit(output_dim, dilation=3),
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ResidualUnit(output_dim, dilation=9),
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)
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def forward(self, x):
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return self.block(x)
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class Decoder(nn.Module):
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def __init__(
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self,
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input_channel,
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channels,
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rates,
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d_out: int = 1,
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):
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super().__init__()
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# Add first conv layer
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layers = [WNConv1d(input_channel, channels, kernel_size=7, padding=3)]
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# Add upsampling + MRF blocks
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for i, stride in enumerate(rates):
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input_dim = channels // 2**i
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output_dim = channels // 2 ** (i + 1)
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if i==1:
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out_pad=1
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else:
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out_pad=0
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layers += [DecoderBlock(input_dim, output_dim, stride,out_pad)]
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# Add final conv layer
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layers += [
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Snake1d(output_dim),
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WNConv1d(output_dim, d_out, kernel_size=7, padding=3),
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# nn.Tanh(),
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]
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self.model = nn.Sequential(*layers)
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def forward(self, x):
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return self.model(x)
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class DAC(BaseModel, CodecMixin):
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def __init__(
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self,
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encoder_dim: int = 64,
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encoder_rates: List[int] = [2, 4, 8, 8],
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latent_dim: int = None,
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decoder_dim: int = 1536,
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decoder_rates: List[int] = [8, 8, 4, 2],
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n_codebooks: int = 9,
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codebook_size: int = 1024,
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codebook_dim: Union[int, list] = 8,
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quantizer_dropout: bool = False,
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sample_rate: int = 44100,
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):
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super().__init__()
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self.encoder_dim = encoder_dim
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self.encoder_rates = encoder_rates
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self.decoder_dim = decoder_dim
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self.decoder_rates = decoder_rates
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self.sample_rate = sample_rate
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if latent_dim is None:
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latent_dim = encoder_dim * (2 ** len(encoder_rates))
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self.latent_dim = latent_dim
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self.hop_length = np.prod(encoder_rates)
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self.encoder = Encoder(encoder_dim, encoder_rates, latent_dim)
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self.n_codebooks = n_codebooks
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self.codebook_size = codebook_size
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self.codebook_dim = codebook_dim
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self.quantizer = ResidualVectorQuantize(
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input_dim=latent_dim,
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n_codebooks=n_codebooks,
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codebook_size=codebook_size,
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codebook_dim=codebook_dim,
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quantizer_dropout=quantizer_dropout,
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)
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self.decoder = Decoder(
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latent_dim,
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decoder_dim,
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decoder_rates,
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)
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self.sample_rate = sample_rate
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self.apply(init_weights)
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self.delay = self.get_delay()
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def preprocess(self, audio_data, sample_rate):
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if sample_rate is None:
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sample_rate = self.sample_rate
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assert sample_rate == self.sample_rate
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length = audio_data.shape[-1]
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right_pad = math.ceil(length / self.hop_length) * self.hop_length - length
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audio_data = nn.functional.pad(audio_data, (0, right_pad))
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return audio_data
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def encode(
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self,
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||||
audio_data: torch.Tensor,
|
||||
n_quantizers: int = None,
|
||||
):
|
||||
"""Encode given audio data and return quantized latent codes
|
||||
|
||||
Parameters
|
||||
----------
|
||||
audio_data : Tensor[B x 1 x T]
|
||||
Audio data to encode
|
||||
n_quantizers : int, optional
|
||||
Number of quantizers to use, by default None
|
||||
If None, all quantizers are used.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
A dictionary with the following keys:
|
||||
"z" : Tensor[B x D x T]
|
||||
Quantized continuous representation of input
|
||||
"codes" : Tensor[B x N x T]
|
||||
Codebook indices for each codebook
|
||||
(quantized discrete representation of input)
|
||||
"latents" : Tensor[B x N*D x T]
|
||||
Projected latents (continuous representation of input before quantization)
|
||||
"vq/commitment_loss" : Tensor[1]
|
||||
Commitment loss to train encoder to predict vectors closer to codebook
|
||||
entries
|
||||
"vq/codebook_loss" : Tensor[1]
|
||||
Codebook loss to update the codebook
|
||||
"length" : int
|
||||
Number of samples in input audio
|
||||
"""
|
||||
z = self.encoder(audio_data)
|
||||
z, codes, latents, commitment_loss, codebook_loss = self.quantizer(
|
||||
z, n_quantizers
|
||||
)
|
||||
return z, codes, latents, commitment_loss, codebook_loss
|
||||
|
||||
def decode(self, z: torch.Tensor):
|
||||
"""Decode given latent codes and return audio data
|
||||
|
||||
Parameters
|
||||
----------
|
||||
z : Tensor[B x D x T]
|
||||
Quantized continuous representation of input
|
||||
length : int, optional
|
||||
Number of samples in output audio, by default None
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
A dictionary with the following keys:
|
||||
"audio" : Tensor[B x 1 x length]
|
||||
Decoded audio data.
|
||||
"""
|
||||
return self.decoder(z)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
audio_data: torch.Tensor,
|
||||
sample_rate: int = None,
|
||||
n_quantizers: int = None,
|
||||
):
|
||||
"""Model forward pass
|
||||
|
||||
Parameters
|
||||
----------
|
||||
audio_data : Tensor[B x 1 x T]
|
||||
Audio data to encode
|
||||
sample_rate : int, optional
|
||||
Sample rate of audio data in Hz, by default None
|
||||
If None, defaults to `self.sample_rate`
|
||||
n_quantizers : int, optional
|
||||
Number of quantizers to use, by default None.
|
||||
If None, all quantizers are used.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
A dictionary with the following keys:
|
||||
"z" : Tensor[B x D x T]
|
||||
Quantized continuous representation of input
|
||||
"codes" : Tensor[B x N x T]
|
||||
Codebook indices for each codebook
|
||||
(quantized discrete representation of input)
|
||||
"latents" : Tensor[B x N*D x T]
|
||||
Projected latents (continuous representation of input before quantization)
|
||||
"vq/commitment_loss" : Tensor[1]
|
||||
Commitment loss to train encoder to predict vectors closer to codebook
|
||||
entries
|
||||
"vq/codebook_loss" : Tensor[1]
|
||||
Codebook loss to update the codebook
|
||||
"length" : int
|
||||
Number of samples in input audio
|
||||
"audio" : Tensor[B x 1 x length]
|
||||
Decoded audio data.
|
||||
"""
|
||||
length = audio_data.shape[-1]
|
||||
audio_data = self.preprocess(audio_data, sample_rate)
|
||||
z, codes, latents, commitment_loss, codebook_loss = self.encode(
|
||||
audio_data, n_quantizers
|
||||
)
|
||||
|
||||
x = self.decode(z)
|
||||
return {
|
||||
"audio": x[..., :length],
|
||||
"z": z,
|
||||
"codes": codes,
|
||||
"latents": latents,
|
||||
"vq/commitment_loss": commitment_loss,
|
||||
"vq/codebook_loss": codebook_loss,
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import numpy as np
|
||||
from functools import partial
|
||||
|
||||
model = DAC().to("cpu")
|
||||
|
||||
for n, m in model.named_modules():
|
||||
o = m.extra_repr()
|
||||
p = sum([np.prod(p.size()) for p in m.parameters()])
|
||||
fn = lambda o, p: o + f" {p/1e6:<.3f}M params."
|
||||
setattr(m, "extra_repr", partial(fn, o=o, p=p))
|
||||
print(model)
|
||||
print("Total # of params: ", sum([np.prod(p.size()) for p in model.parameters()]))
|
||||
|
||||
length = 88200 * 2
|
||||
x = torch.randn(1, 1, length).to(model.device)
|
||||
x.requires_grad_(True)
|
||||
x.retain_grad()
|
||||
|
||||
# Make a forward pass
|
||||
out = model(x)["audio"]
|
||||
print("Input shape:", x.shape)
|
||||
print("Output shape:", out.shape)
|
||||
|
||||
# Create gradient variable
|
||||
grad = torch.zeros_like(out)
|
||||
grad[:, :, grad.shape[-1] // 2] = 1
|
||||
|
||||
# Make a backward pass
|
||||
out.backward(grad)
|
||||
|
||||
# Check non-zero values
|
||||
gradmap = x.grad.squeeze(0)
|
||||
gradmap = (gradmap != 0).sum(0) # sum across features
|
||||
rf = (gradmap != 0).sum()
|
||||
|
||||
print(f"Receptive field: {rf.item()}")
|
||||
|
||||
x = AudioSignal(torch.randn(1, 1, 44100 * 60), 44100)
|
||||
model.decompress(model.compress(x, verbose=True), verbose=True)
|
||||
@@ -0,0 +1,228 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from audiotools import AudioSignal
|
||||
from audiotools import ml
|
||||
from audiotools import STFTParams
|
||||
from einops import rearrange
|
||||
from torch.nn.utils import weight_norm
|
||||
|
||||
|
||||
def WNConv1d(*args, **kwargs):
|
||||
act = kwargs.pop("act", True)
|
||||
conv = weight_norm(nn.Conv1d(*args, **kwargs))
|
||||
if not act:
|
||||
return conv
|
||||
return nn.Sequential(conv, nn.LeakyReLU(0.1))
|
||||
|
||||
|
||||
def WNConv2d(*args, **kwargs):
|
||||
act = kwargs.pop("act", True)
|
||||
conv = weight_norm(nn.Conv2d(*args, **kwargs))
|
||||
if not act:
|
||||
return conv
|
||||
return nn.Sequential(conv, nn.LeakyReLU(0.1))
|
||||
|
||||
|
||||
class MPD(nn.Module):
|
||||
def __init__(self, period):
|
||||
super().__init__()
|
||||
self.period = period
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
WNConv2d(1, 32, (5, 1), (3, 1), padding=(2, 0)),
|
||||
WNConv2d(32, 128, (5, 1), (3, 1), padding=(2, 0)),
|
||||
WNConv2d(128, 512, (5, 1), (3, 1), padding=(2, 0)),
|
||||
WNConv2d(512, 1024, (5, 1), (3, 1), padding=(2, 0)),
|
||||
WNConv2d(1024, 1024, (5, 1), 1, padding=(2, 0)),
|
||||
]
|
||||
)
|
||||
self.conv_post = WNConv2d(
|
||||
1024, 1, kernel_size=(3, 1), padding=(1, 0), act=False
|
||||
)
|
||||
|
||||
def pad_to_period(self, x):
|
||||
t = x.shape[-1]
|
||||
x = F.pad(x, (0, self.period - t % self.period), mode="reflect")
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
x = self.pad_to_period(x)
|
||||
x = rearrange(x, "b c (l p) -> b c l p", p=self.period)
|
||||
|
||||
for layer in self.convs:
|
||||
x = layer(x)
|
||||
fmap.append(x)
|
||||
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
|
||||
return fmap
|
||||
|
||||
|
||||
class MSD(nn.Module):
|
||||
def __init__(self, rate: int = 1, sample_rate: int = 44100):
|
||||
super().__init__()
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
WNConv1d(1, 16, 15, 1, padding=7),
|
||||
WNConv1d(16, 64, 41, 4, groups=4, padding=20),
|
||||
WNConv1d(64, 256, 41, 4, groups=16, padding=20),
|
||||
WNConv1d(256, 1024, 41, 4, groups=64, padding=20),
|
||||
WNConv1d(1024, 1024, 41, 4, groups=256, padding=20),
|
||||
WNConv1d(1024, 1024, 5, 1, padding=2),
|
||||
]
|
||||
)
|
||||
self.conv_post = WNConv1d(1024, 1, 3, 1, padding=1, act=False)
|
||||
self.sample_rate = sample_rate
|
||||
self.rate = rate
|
||||
|
||||
def forward(self, x):
|
||||
x = AudioSignal(x, self.sample_rate)
|
||||
x.resample(self.sample_rate // self.rate)
|
||||
x = x.audio_data
|
||||
|
||||
fmap = []
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
|
||||
return fmap
|
||||
|
||||
|
||||
BANDS = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)]
|
||||
|
||||
|
||||
class MRD(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
window_length: int,
|
||||
hop_factor: float = 0.25,
|
||||
sample_rate: int = 44100,
|
||||
bands: list = BANDS,
|
||||
):
|
||||
"""Complex multi-band spectrogram discriminator.
|
||||
Parameters
|
||||
----------
|
||||
window_length : int
|
||||
Window length of STFT.
|
||||
hop_factor : float, optional
|
||||
Hop factor of the STFT, defaults to ``0.25 * window_length``.
|
||||
sample_rate : int, optional
|
||||
Sampling rate of audio in Hz, by default 44100
|
||||
bands : list, optional
|
||||
Bands to run discriminator over.
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
self.window_length = window_length
|
||||
self.hop_factor = hop_factor
|
||||
self.sample_rate = sample_rate
|
||||
self.stft_params = STFTParams(
|
||||
window_length=window_length,
|
||||
hop_length=int(window_length * hop_factor),
|
||||
match_stride=True,
|
||||
)
|
||||
|
||||
n_fft = window_length // 2 + 1
|
||||
bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
|
||||
self.bands = bands
|
||||
|
||||
ch = 32
|
||||
convs = lambda: nn.ModuleList(
|
||||
[
|
||||
WNConv2d(2, ch, (3, 9), (1, 1), padding=(1, 4)),
|
||||
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
|
||||
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
|
||||
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
|
||||
WNConv2d(ch, ch, (3, 3), (1, 1), padding=(1, 1)),
|
||||
]
|
||||
)
|
||||
self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
|
||||
self.conv_post = WNConv2d(ch, 1, (3, 3), (1, 1), padding=(1, 1), act=False)
|
||||
|
||||
def spectrogram(self, x):
|
||||
x = AudioSignal(x, self.sample_rate, stft_params=self.stft_params)
|
||||
x = torch.view_as_real(x.stft())
|
||||
x = rearrange(x, "b 1 f t c -> (b 1) c t f")
|
||||
# Split into bands
|
||||
x_bands = [x[..., b[0] : b[1]] for b in self.bands]
|
||||
return x_bands
|
||||
|
||||
def forward(self, x):
|
||||
x_bands = self.spectrogram(x)
|
||||
fmap = []
|
||||
|
||||
x = []
|
||||
for band, stack in zip(x_bands, self.band_convs):
|
||||
for layer in stack:
|
||||
band = layer(band)
|
||||
fmap.append(band)
|
||||
x.append(band)
|
||||
|
||||
x = torch.cat(x, dim=-1)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
|
||||
return fmap
|
||||
|
||||
|
||||
class Discriminator(ml.BaseModel):
|
||||
def __init__(
|
||||
self,
|
||||
rates: list = [],
|
||||
periods: list = [2, 3, 5, 7, 11],
|
||||
fft_sizes: list = [2048, 1024, 512],
|
||||
sample_rate: int = 44100,
|
||||
bands: list = BANDS,
|
||||
):
|
||||
"""Discriminator that combines multiple discriminators.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
rates : list, optional
|
||||
sampling rates (in Hz) to run MSD at, by default []
|
||||
If empty, MSD is not used.
|
||||
periods : list, optional
|
||||
periods (of samples) to run MPD at, by default [2, 3, 5, 7, 11]
|
||||
fft_sizes : list, optional
|
||||
Window sizes of the FFT to run MRD at, by default [2048, 1024, 512]
|
||||
sample_rate : int, optional
|
||||
Sampling rate of audio in Hz, by default 44100
|
||||
bands : list, optional
|
||||
Bands to run MRD at, by default `BANDS`
|
||||
"""
|
||||
super().__init__()
|
||||
discs = []
|
||||
discs += [MPD(p) for p in periods]
|
||||
discs += [MSD(r, sample_rate=sample_rate) for r in rates]
|
||||
discs += [MRD(f, sample_rate=sample_rate, bands=bands) for f in fft_sizes]
|
||||
self.discriminators = nn.ModuleList(discs)
|
||||
|
||||
def preprocess(self, y):
|
||||
# Remove DC offset
|
||||
y = y - y.mean(dim=-1, keepdims=True)
|
||||
# Peak normalize the volume of input audio
|
||||
y = 0.8 * y / (y.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
|
||||
return y
|
||||
|
||||
def forward(self, x):
|
||||
x = self.preprocess(x)
|
||||
fmaps = [d(x) for d in self.discriminators]
|
||||
return fmaps
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
disc = Discriminator()
|
||||
x = torch.zeros(1, 1, 44100)
|
||||
results = disc(x)
|
||||
for i, result in enumerate(results):
|
||||
print(f"disc{i}")
|
||||
for i, r in enumerate(result):
|
||||
print(r.shape, r.mean(), r.min(), r.max())
|
||||
print()
|
||||
Reference in New Issue
Block a user