encoded.py

# [email protected]:facebookresearch/encodec.git

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""Various utilities."""

from hashlib import sha256
from pathlib import Path
import typing as tp

import torch
import torchaudio


def _linear_overlap_add(frames: tp.List[torch.Tensor], stride: int):
 # Generic overlap add, with linear fade-in/fade-out, supporting complex scenario
 # e.g., more than 2 frames per position.
 # The core idea is to use a weight function that is a triangle,
 # with a maximum value at the middle of the segment.
 # We use this weighting when summing the frames, and divide by the sum of weights
 # for each positions at the end. Thus:
 # - if a frame is the only one to cover a position, the weighting is a no-op.
 # - if 2 frames cover a position:
 # ... ...
 # / \/ \
 # / /\ \
 # S T , i.e. S offset of second frame starts, T end of first frame.
 # Then the weight function for each one is: (t - S), (T - t), with `t` a given offset.
 # After the final normalization, the weight of the second frame at position `t` is
 # (t - S) / (t - S + (T - t)) = (t - S) / (T - S), which is exactly what we want.
 #
 # - if more than 2 frames overlap at a given point, we hope that by induction
 # something sensible happens.
 assert len(frames)
 device = frames[0].device
 dtype = frames[0].dtype
 shape = frames[0].shape[:-1]
 total_size = stride * (len(frames) - 1) + frames[-1].shape[-1]

 frame_length = frames[0].shape[-1]
 t = torch.linspace(0, 1, frame_length + 2, device=device, dtype=dtype)[1: -1]
 weight = 0.5 - (t - 0.5).abs()

 sum_weight = torch.zeros(total_size, device=device, dtype=dtype)
 out = torch.zeros(*shape, total_size, device=device, dtype=dtype)
 offset: int = 0

 for frame in frames:
 frame_length = frame.shape[-1]
 out[..., offset:offset + frame_length] += weight[:frame_length] * frame
 sum_weight[offset:offset + frame_length] += weight[:frame_length]
 offset += stride
 assert sum_weight.min() > 0
 return out / sum_weight


def _get_checkpoint_url(root_url: str, checkpoint: str):
 if not root_url.endswith('/'):
 root_url += '/'
 return root_url + checkpoint


def _check_checksum(path: Path, checksum: str):
 sha = sha256()
 with open(path, 'rb') as file:
 while True:
 buf = file.read(2**20)
 if not buf:
 break
 sha.update(buf)
 actual_checksum = sha.hexdigest()[:len(checksum)]
 if actual_checksum != checksum:
 raise RuntimeError(f'Invalid checksum for file {path}, '
 f'expected {checksum} but got {actual_checksum}')


def convert_audio(wav: torch.Tensor, sr: int, target_sr: int, target_channels: int):
 assert wav.dim() >= 2, "Audio tensor must have at least 2 dimensions"
 assert wav.shape[-2] in [1, 2], "Audio must be mono or stereo."
 *shape, channels, length = wav.shape
 if target_channels == 1:
 wav = wav.mean(-2, keepdim=True)
 elif target_channels == 2:
 wav = wav.expand(*shape, target_channels, length)
 elif channels == 1:
 wav = wav.expand(target_channels, -1)
 else:
 raise RuntimeError(f"Impossible to convert from {channels} to {target_channels}")
 wav = torchaudio.transforms.Resample(sr, target_sr)(wav)
 return wav


def save_audio(wav: torch.Tensor, path: tp.Union[Path, str],
 sample_rate: int, rescale: bool = False):
 limit = 0.99
 mx = wav.abs().max()
 if rescale:
 wav = wav * min(limit / mx, 1)
 else:
 wav = wav.clamp(-limit, limit)
 torchaudio.save(str(path), wav, sample_rate=sample_rate, encoding='PCM_S', bits_per_sample=16)