| import csv |
| import os |
| from pathlib import Path |
|
|
| from tqdm import tqdm |
|
|
| from ..core import AudioSignal |
|
|
|
|
| def create_csv( |
| audio_files: list, output_csv: Path, loudness: bool = False, data_path: str = None |
| ): |
| """Converts a folder of audio files to a CSV file. If ``loudness = True``, |
| the output of this function will create a CSV file that looks something |
| like: |
| |
| .. csv-table:: |
| :header: path,loudness |
| |
| daps/produced/f1_script1_produced.wav,-16.299999237060547 |
| daps/produced/f1_script2_produced.wav,-16.600000381469727 |
| daps/produced/f1_script3_produced.wav,-17.299999237060547 |
| daps/produced/f1_script4_produced.wav,-16.100000381469727 |
| daps/produced/f1_script5_produced.wav,-16.700000762939453 |
| daps/produced/f3_script1_produced.wav,-16.5 |
| |
| .. note:: |
| The paths above are written relative to the ``data_path`` argument |
| which defaults to the environment variable ``PATH_TO_DATA`` if |
| it isn't passed to this function, and defaults to the empty string |
| if that environment variable is not set. |
| |
| You can produce a CSV file from a directory of audio files via: |
| |
| >>> import audiotools |
| >>> directory = ... |
| >>> audio_files = audiotools.util.find_audio(directory) |
| >>> output_path = "train.csv" |
| >>> audiotools.data.preprocess.create_csv( |
| >>> audio_files, output_csv, loudness=True |
| >>> ) |
| |
| Note that you can create empty rows in the CSV file by passing an empty |
| string or None in the ``audio_files`` list. This is useful if you want to |
| sync multiple CSV files in a multitrack setting. The loudness of these |
| empty rows will be set to -inf. |
| |
| Parameters |
| ---------- |
| audio_files : list |
| List of audio files. |
| output_csv : Path |
| Output CSV, with each row containing the relative path of every file |
| to ``data_path``, if specified (defaults to None). |
| loudness : bool |
| Compute loudness of entire file and store alongside path. |
| """ |
|
|
| info = [] |
| pbar = tqdm(audio_files) |
| for af in pbar: |
| af = Path(af) |
| pbar.set_description(f"Processing {af.name}") |
| _info = {} |
| if af.name == "": |
| _info["path"] = "" |
| if loudness: |
| _info["loudness"] = -float("inf") |
| else: |
| _info["path"] = af.relative_to(data_path) if data_path is not None else af |
| if loudness: |
| _info["loudness"] = AudioSignal(af).ffmpeg_loudness().item() |
|
|
| info.append(_info) |
|
|
| with open(output_csv, "w") as f: |
| writer = csv.DictWriter(f, fieldnames=list(info[0].keys())) |
| writer.writeheader() |
|
|
| for item in info: |
| writer.writerow(item) |
|
|
