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102 lines
4.0 KiB
Python
102 lines
4.0 KiB
Python
import numpy as np
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import os
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import pandas
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import tables
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from functools import partial
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from multiprocessing.dummy import Pool
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from util.audio import audiofile_to_input_vector
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from util.text import text_to_char_array
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def pmap(fun, iterable, threads=8):
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pool = Pool(threads)
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results = pool.map(fun, iterable)
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pool.close()
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return results
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def process_single_file(row, numcep, numcontext, alphabet):
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# row = index, Series
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_, file = row
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features = audiofile_to_input_vector(file.wav_filename, numcep, numcontext)
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transcript = text_to_char_array(file.transcript, alphabet)
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if (2*numcontext + len(features)) < len(transcript):
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raise ValueError('Error: Audio file {} is too short for transcription.'.format(file.wav_filename))
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return features, len(features), transcript, len(transcript)
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# load samples from CSV, compute features, optionally cache results on disk
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def preprocess(csv_files, batch_size, numcep, numcontext, alphabet, hdf5_cache_path=None):
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COLUMNS = ('features', 'features_len', 'transcript', 'transcript_len')
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print('Preprocessing', csv_files)
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if hdf5_cache_path and os.path.exists(hdf5_cache_path):
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with tables.open_file(hdf5_cache_path, 'r') as file:
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features = file.root.features[:]
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features_len = file.root.features_len[:]
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transcript = file.root.transcript[:]
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transcript_len = file.root.transcript_len[:]
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# features are stored flattened, so reshape into
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# [n_steps, (n_input + 2*n_context*n_input)]
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for i in range(len(features)):
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features[i] = np.reshape(features[i], [features_len[i], -1])
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in_data = list(zip(features, features_len,
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transcript, transcript_len))
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print('Loaded from cache at', hdf5_cache_path)
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return pandas.DataFrame(data=in_data, columns=COLUMNS)
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source_data = None
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for csv in csv_files:
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file = pandas.read_csv(csv, encoding='utf-8', na_filter=False)
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#FIXME: not cross-platform
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csv_dir = os.path.dirname(os.path.abspath(csv))
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file['wav_filename'] = file['wav_filename'].str.replace(r'(^[^/])', lambda m: os.path.join(csv_dir, m.group(1)))
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if source_data is None:
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source_data = file
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else:
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source_data = source_data.append(file)
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step_fn = partial(process_single_file,
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numcep=numcep,
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numcontext=numcontext,
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alphabet=alphabet)
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out_data = pmap(step_fn, source_data.iterrows())
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if hdf5_cache_path:
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print('Saving to', hdf5_cache_path)
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# list of tuples -> tuple of lists
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features, features_len, transcript, transcript_len = zip(*out_data)
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with tables.open_file(hdf5_cache_path, 'w') as file:
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features_dset = file.create_vlarray(file.root,
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'features',
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tables.Float32Atom(),
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filters=tables.Filters(complevel=1))
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# VLArray atoms need to be 1D, so flatten feature array
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for f in features:
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features_dset.append(np.reshape(f, -1))
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features_len_dset = file.create_array(file.root,
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'features_len',
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features_len)
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transcript_dset = file.create_vlarray(file.root,
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'transcript',
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tables.Int32Atom(),
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filters=tables.Filters(complevel=1))
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for t in transcript:
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transcript_dset.append(t)
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transcript_len_dset = file.create_array(file.root,
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'transcript_len',
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transcript_len)
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print('Preprocessing done')
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return pandas.DataFrame(data=out_data, columns=COLUMNS)
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