Plumbing for CSV import from online logs
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@@ -1,11 +1,8 @@
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import os
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import os
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# import csv
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import logging
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import logging
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from itertools import chain
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from itertools import chain
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import tqdm
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import click
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import click
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# from tabulate import tabulate, SEPARATING_LINE
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from .inference import InferenceContext, load_ucs
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from .inference import InferenceContext, load_ucs
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from .gather import (build_sentence_class_dataset, print_dataset_stats,
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from .gather import (build_sentence_class_dataset, print_dataset_stats,
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@@ -136,6 +133,26 @@ def recommend(ctx, text, paths, interactive, skip_ucs):
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os.rename(path, new_path)
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os.rename(path, new_path)
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break
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break
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@ucsinfer.command('csv')
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@click.option('--filename-col', default="FileName",
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help="Heading or index of the column containing filenames",
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show_default=True)
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@click.option('--description-col', default="TrackDescription",
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help="Heading or index of the column containing descriptions",
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show_default=True)
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@click.option('--out', default='dataset/', show_default=True)
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@click.argument('paths', nargs=-1)
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@click.pass_context
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def csv(ctx, paths, out, filename_col, description_col):
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"""
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Scan training data from CSV files
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`csv` is used to build a training dataset for finetuning the selected
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model, as like the `gather` command, except instead of scanning the
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file system it builds a dataset from descriptions and UCS filenames in
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columns of a CSV file.
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"""
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pass
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@ucsinfer.command('gather')
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@ucsinfer.command('gather')
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@click.option('--out', default='dataset/', show_default=True)
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@click.option('--out', default='dataset/', show_default=True)
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@@ -146,7 +163,7 @@ def recommend(ctx, text, paths, interactive, skip_ucs):
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@click.pass_context
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@click.pass_context
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def gather(ctx, paths, out, ucs_data):
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def gather(ctx, paths, out, ucs_data):
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"""
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"""
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Scan files to build a training dataset
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Scan training data from audio files
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`gather` is used to build a training dataset for finetuning the selected
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`gather` is used to build a training dataset for finetuning the selected
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model. Description sentences and UCS categories are collected from '.wav'
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model. Description sentences and UCS categories are collected from '.wav'
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