mirror of https://github.com/MycroftAI/mimic2.git
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
from concurrent.futures import ProcessPoolExecutor
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from functools import partial
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import numpy as np
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import os
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from util import audio
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import sqlite3
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import sys
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def build_from_path(in_dir, out_dir, username, num_workers=1, tqdm=lambda x: x):
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'''Preprocesses the recordings from mimic-recording-studio (based on sqlite db) into a given output directory.
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Args:
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in_dir: The root directory of mimic-recording-studio
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out_dir: The directory to write the output into
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num_workers: Optional number of worker processes to parallelize across
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tqdm: You can optionally pass tqdm to get a nice progress bar
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Returns:
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A list of tuples describing the training examples. This should be written to train.txt
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'''
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# We use ProcessPoolExecutor to parallize across processes. This is just an optimization and you
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# can omit it and just call _process_utterance on each input if you want.
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executor = ProcessPoolExecutor(max_workers=num_workers)
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futures = []
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index = 1
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# Query sqlite db of mimic-recording-studio
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dbfile = os.path.join(in_dir,"backend","db","mimicstudio.db")
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print("Reading data from mimic-recording-studio sqlite db file: " + dbfile)
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conn = sqlite3.connect(dbfile)
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c = conn.cursor()
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uid = ''
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sql_get_guid = "SELECT uuid FROM usermodel LIMIT 1;"
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if username:
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print("Query user guid for " + username + " in sqlite db")
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sql_get_guid = "SELECT uuid FROM usermodel WHERE UPPER(user_name) = '" + username.upper() + "' LIMIT 1;"
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for row in c.execute(sql_get_guid):
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uid = row[0]
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if uid == '':
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print("No userid could be found in sqlite db.")
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sys.exit()
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print("Found speaker user guid in sqlite: " + uid)
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wav_dir = os.path.join(in_dir,"backend","audio_files",uid)
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print("Search for wav files in " + wav_dir)
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for row in c.execute('SELECT DISTINCT audio_id, lower(prompt) FROM audiomodel ORDER BY length(prompt)'):
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wav_path = os.path.join(wav_dir, '%s.wav' % row[0])
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if os.path.isfile(wav_path):
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text = row[1]
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futures.append(executor.submit(partial(_process_utterance, out_dir, index, wav_path, text)))
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index += 1
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else:
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print("File " + wav_path + " no found. Skipping.")
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return [future.result() for future in tqdm(futures)]
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def _process_utterance(out_dir, index, wav_path, text):
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'''Preprocesses a single utterance audio/text pair.
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This writes the mel and linear scale spectrograms to disk and returns a tuple to write
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to the train.txt file.
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Args:
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out_dir: The directory to write the spectrograms into
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index: The numeric index to use in the spectrogram filenames.
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wav_path: Path to the audio file containing the speech input
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text: The text spoken in the input audio file
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Returns:
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A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
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'''
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# Load the audio to a numpy array:
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wav = audio.load_wav(wav_path)
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# Compute the linear-scale spectrogram from the wav:
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spectrogram = audio.spectrogram(wav).astype(np.float32)
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n_frames = spectrogram.shape[1]
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# Compute a mel-scale spectrogram from the wav:
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mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
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# Write the spectrograms to disk:
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spectrogram_filename = 'mrs-spec-%05d.npy' % index
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mel_filename = 'mrs-mel-%05d.npy' % index
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np.save(os.path.join(out_dir, spectrogram_filename), spectrogram.T, allow_pickle=False)
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np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
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# Return a tuple describing this training example:
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return (spectrogram_filename, mel_filename, n_frames, text)
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