third_party/demucs/models/apply.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@File    : apply.py
@Time    : 2023/8/8 下午4:22
@Author  : waytan
@Contact : waytan@tencent.com
@License : (C)Copyright 2023, Tencent
@Desc    : Apply
"""

from concurrent.futures import ThreadPoolExecutor
import torch
import os
import random
import typing as tp

import torch as th
from torch import nn
from torch.nn import functional as F
import tqdm

from .htdemucs import HTDemucs
from .audio import load_track, save_audio
from .utils import center_trim, DummyPoolExecutor

Model = tp.Union[HTDemucs]


class BagOfModels(nn.Module):
    def __init__(self, models: tp.List[Model],
                 weights: tp.Optional[tp.List[tp.List[float]]] = None,
                 segment: tp.Optional[float] = None):
        """
        Represents a bag of models with specific weights.
        You should call `apply_model` rather than calling directly the forward here for
        optimal performance.

        Args:
            models (list[nn.Module]): list of Demucs/HDemucs models.
            weights (list[list[float]]): list of weights. If None, assumed to
                be all ones, otherwise it should be a list of N list (N number of models),
                each containing S floats (S number of sources).
            segment (None or float): overrides the `segment` attribute of each model
                (this is performed inplace, be careful is you reuse the models passed).
        """
        super().__init__()
        assert len(models) > 0
        first = models[0]
        for other in models:
            assert other.sources == first.sources
            assert other.samplerate == first.samplerate
            assert other.audio_channels == first.audio_channels
            if segment is not None:
                other.segment = segment

        self.audio_channels = first.audio_channels
        self.samplerate = first.samplerate
        self.sources = first.sources
        self.models = nn.ModuleList(models)

        if weights is None:
            weights = [[1. for _ in first.sources] for _ in models]
        else:
            assert len(weights) == len(models)
            for weight in weights:
                assert len(weight) == len(first.sources)
        self.weights = weights

    @property
    def max_allowed_segment(self) -> float:
        max_allowed_segment = float('inf')
        for model in self.models:
            if isinstance(model, HTDemucs):
                max_allowed_segment = min(max_allowed_segment, float(model.segment))
        return max_allowed_segment

    def forward(self, x):
        raise NotImplementedError("Call `apply_model` on this.")
    
    def separate(self, source_file, output_dir, stem=None, device=None):
        wav, _ = load_track(source_file, self.audio_channels, self.samplerate)
        ref = wav.mean(0)
        wav -= ref.mean()
        wav /= ref.std()
        sources = apply_model(self, wav[None], device=device, shifts=1, split=True, overlap=0.25, 
                              progress=True, num_workers=0, segment=None)[0]
        sources *= ref.std()
        sources += ref.mean()

        output_paths = []
        name, ext = os.path.splitext(os.path.split(source_file)[-1])
        if ext != ".flac":
            ext = ".flac"
        kwargs = {
            'samplerate': self.samplerate,
            'bitrate': 320,
            'clip': "rescale",
            'as_float': False,
            'bits_per_sample': 16,
        }
        if stem is None:
            for source, stem in zip(sources, self.sources):
                output_stem_path = os.path.join(output_dir, f"{name}_{stem}{ext}")
                save_audio(source, output_stem_path, **kwargs)
                output_paths.append(output_stem_path)
        else:
            sources = list(sources)
            output_stem_path = os.path.join(output_dir, f"{name}_{stem}{ext}")
            save_audio(sources.pop(self.sources.index(stem)), output_stem_path, **kwargs)
            other_stem = torch.zeros_like(sources[0])
            for i in sources:
                other_stem += i
            output_no_stem_path = os.path.join(output_dir, f"{name}_no_{stem}{ext}")
            save_audio(other_stem, output_no_stem_path, **kwargs)
            output_paths = [output_stem_path, output_no_stem_path]

        return output_paths


class TensorChunk:
    def __init__(self, tensor, offset=0, length=None):
        total_length = tensor.shape[-1]
        assert offset >= 0
        assert offset < total_length

        if length is None:
            length = total_length - offset
        else:
            length = min(total_length - offset, length)

        if isinstance(tensor, TensorChunk):
            self.tensor = tensor.tensor
            self.offset = offset + tensor.offset
        else:
            self.tensor = tensor
            self.offset = offset
        self.length = length
        self.device = tensor.device

    @property
    def shape(self):
        shape = list(self.tensor.shape)
        shape[-1] = self.length
        return shape

    def padded(self, target_length):
        delta = target_length - self.length
        total_length = self.tensor.shape[-1]
        assert delta >= 0

        start = self.offset - delta // 2
        end = start + target_length

        correct_start = max(0, start)
        correct_end = min(total_length, end)

        pad_left = correct_start - start
        pad_right = end - correct_end

        out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))
        assert out.shape[-1] == target_length
        return out


def tensor_chunk(tensor_or_chunk):
    if isinstance(tensor_or_chunk, TensorChunk):
        return tensor_or_chunk
    else:
        assert isinstance(tensor_or_chunk, th.Tensor)
        return TensorChunk(tensor_or_chunk)


def apply_model(model: tp.Union[BagOfModels, Model],
                mix: tp.Union[th.Tensor, TensorChunk],
                shifts: int = 1, split: bool = True,
                overlap: float = 0.25, transition_power: float = 1.,
                progress: bool = False, device=None,
                num_workers: int = 0, segment: tp.Optional[float] = None,
                pool=None) -> th.Tensor:
    """
    Apply model to a given mixture.

    Args:
        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
            and apply the oppositve shift to the output. This is repeated `shifts` time and
            all predictions are averaged. This effectively makes the model time equivariant
            and improves SDR by up to 0.2 points.
        split (bool): if True, the input will be broken down in 8 seconds extracts
            and predictions will be performed individually on each and concatenated.
            Useful for model with large memory footprint like Tasnet.
        progress (bool): if True, show a progress bar (requires split=True)
        device (torch.device, str, or None): if provided, device on which to
            execute the computation, otherwise `mix.device` is assumed.
            When `device` is different from `mix.device`, only local computations will
            be on `device`, while the entire tracks will be stored on `mix.device`.
        num_workers (int): if non zero, device is 'cpu', how many threads to
            use in parallel.
        segment (float or None): override the model segment parameter.
    """
    if device is None:
        device = mix.device
    else:
        device = th.device(device)
    if pool is None:
        if num_workers > 0 and device.type == 'cpu':
            pool = ThreadPoolExecutor(num_workers)
        else:
            pool = DummyPoolExecutor()
    kwargs: tp.Dict[str, tp.Any] = {
        'shifts': shifts,
        'split': split,
        'overlap': overlap,
        'transition_power': transition_power,
        'progress': progress,
        'device': device,
        'pool': pool,
        'segment': segment,
    }
    out: tp.Union[float, th.Tensor]
    if isinstance(model, BagOfModels):
        # Special treatment for bag of model.
        # We explicitely apply multiple times `apply_model` so that the random shifts
        # are different for each model.
        estimates: tp.Union[float, th.Tensor] = 0.
        totals = [0.] * len(model.sources)
        for sub_model, model_weights in zip(model.models, model.weights):
            original_model_device = next(iter(sub_model.parameters())).device
            sub_model.to(device)

            out = apply_model(sub_model, mix, **kwargs)
            sub_model.to(original_model_device)
            for k, inst_weight in enumerate(model_weights):
                out[:, k, :, :] *= inst_weight
                totals[k] += inst_weight
            estimates += out
            del out

        assert isinstance(estimates, th.Tensor)
        for k in range(estimates.shape[1]):
            estimates[:, k, :, :] /= totals[k]
        return estimates

    model.to(device)
    model.eval()
    assert transition_power >= 1, "transition_power < 1 leads to weird behavior."
    batch, channels, length = mix.shape
    if shifts:
        kwargs['shifts'] = 0
        max_shift = int(0.5 * model.samplerate)
        mix = tensor_chunk(mix)
        assert isinstance(mix, TensorChunk)
        padded_mix = mix.padded(length + 2 * max_shift)
        out = 0.
        for _ in range(shifts):
            offset = random.randint(0, max_shift)
            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)
            shifted_out = apply_model(model, shifted, **kwargs)
            out += shifted_out[..., max_shift - offset:]
        out /= shifts
        assert isinstance(out, th.Tensor)
        return out
    elif split:
        kwargs['split'] = False
        out = th.zeros(batch, len(model.sources), channels, length, device=mix.device)
        sum_weight = th.zeros(length, device=mix.device)
        if segment is None:
            segment = model.segment
        assert segment is not None and segment > 0.
        segment_length: int = int(model.samplerate * segment)
        stride = int((1 - overlap) * segment_length)
        offsets = range(0, length, stride)
        scale = float(format(stride / model.samplerate, ".2f"))
        # We start from a triangle shaped weight, with maximal weight in the middle
        # of the segment. Then we normalize and take to the power `transition_power`.
        # Large values of transition power will lead to sharper transitions.
        weight = th.cat([th.arange(1, segment_length // 2 + 1, device=device),
                         th.arange(segment_length - segment_length // 2, 0, -1, device=device)])
        assert len(weight) == segment_length
        # If the overlap < 50%, this will translate to linear transition when
        # transition_power is 1.
        weight = (weight / weight.max())**transition_power
        futures = []
        for offset in offsets:
            chunk = TensorChunk(mix, offset, segment_length)
            future = pool.submit(apply_model, model, chunk, **kwargs)
            futures.append((future, offset))
            offset += segment_length
        if progress:
            futures = tqdm.tqdm(futures, unit_scale=scale, ncols=120, unit='seconds')
        for future, offset in futures:
            chunk_out = future.result()
            chunk_length = chunk_out.shape[-1]
            out[..., offset:offset + segment_length] += (
                weight[:chunk_length] * chunk_out).to(mix.device)
            sum_weight[offset:offset + segment_length] += weight[:chunk_length].to(mix.device)
        assert sum_weight.min() > 0
        out /= sum_weight
        assert isinstance(out, th.Tensor)
        return out
    else:
        valid_length: int
        if isinstance(model, HTDemucs) and segment is not None:
            valid_length = int(segment * model.samplerate)
        elif hasattr(model, 'valid_length'):
            valid_length = model.valid_length(length)  # type: ignore
        else:
            valid_length = length
        mix = tensor_chunk(mix)
        assert isinstance(mix, TensorChunk)
        padded_mix = mix.padded(valid_length).to(device)
        with th.no_grad():
            out = model(padded_mix)
        assert isinstance(out, th.Tensor)
        return center_trim(out, length)