You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
90 lines
3.0 KiB
90 lines
3.0 KiB
import math
|
|
import warnings
|
|
from pathlib import Path
|
|
|
|
import argbind
|
|
import paddle
|
|
from tqdm import tqdm
|
|
|
|
from dac.utils import load_model
|
|
from paddlespeech.audiotools import AudioSignal
|
|
from paddlespeech.audiotools.core import util
|
|
|
|
warnings.filterwarnings("ignore", category=UserWarning)
|
|
|
|
|
|
@argbind.bind(group="encode", positional=True, without_prefix=True)
|
|
@paddle.no_grad()
|
|
def encode(
|
|
input: str,
|
|
output: str="",
|
|
weights_path: str="",
|
|
model_tag: str="latest",
|
|
model_bitrate: str="8kbps",
|
|
n_quantizers: int=None,
|
|
model_type: str="44khz",
|
|
win_duration: float=5.0,
|
|
verbose: bool=False, ):
|
|
"""Encode audio files in input path to .dac format.
|
|
|
|
Parameters
|
|
----------
|
|
input : str
|
|
Path to input audio file or directory
|
|
output : str, optional
|
|
Path to output directory, by default "". If `input` is a directory, the directory sub-tree relative to `input` is re-created in `output`.
|
|
weights_path : str, optional
|
|
Path to weights file, by default "". If not specified, the weights file will be downloaded from the internet using the
|
|
model_tag and model_type.
|
|
model_tag : str, optional
|
|
Tag of the model to use, by default "latest". Ignored if `weights_path` is specified.
|
|
model_bitrate: str
|
|
Bitrate of the model. Must be one of "8kbps", or "16kbps". Defaults to "8kbps".
|
|
n_quantizers : int, optional
|
|
Number of quantizers to use, by default None. If not specified, all the quantizers will be used and the model will compress at maximum bitrate.
|
|
device : str, optional
|
|
Device to use, by default "cuda"
|
|
model_type : str, optional
|
|
The type of model to use. Must be one of "44khz", "24khz", or "16khz". Defaults to "44khz". Ignored if `weights_path` is specified.
|
|
"""
|
|
generator = load_model(
|
|
model_type=model_type,
|
|
model_bitrate=model_bitrate,
|
|
tag=model_tag,
|
|
load_path=weights_path, )
|
|
generator.eval()
|
|
kwargs = {"n_quantizers": n_quantizers}
|
|
|
|
# Find all audio files in input path
|
|
input = Path(input)
|
|
audio_files = util.find_audio(input)
|
|
|
|
output = Path(output)
|
|
output.mkdir(parents=True, exist_ok=True)
|
|
|
|
for i in tqdm(range(len(audio_files)), desc="Encoding files"):
|
|
# Load file
|
|
signal = AudioSignal(audio_files[i])
|
|
|
|
# Encode audio to .dac format
|
|
artifact = generator.compress(
|
|
signal, win_duration, verbose=verbose, **kwargs)
|
|
|
|
# Compute output path
|
|
relative_path = audio_files[i].relative_to(input)
|
|
output_dir = output / relative_path.parent
|
|
if not relative_path.name:
|
|
output_dir = output
|
|
relative_path = audio_files[i]
|
|
output_name = relative_path.with_suffix(".dac").name
|
|
output_path = output_dir / output_name
|
|
output_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
artifact.save(output_path)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = argbind.parse_args()
|
|
with argbind.scope(args):
|
|
encode()
|