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Author: xcmyz

data/ljspeech.py

@@ -1,77 +1,34 @@
-from concurrent.futures import ProcessPoolExecutor
-from functools import partial
 import numpy as np
 import os
-import audio
+import Audio
 
 
-def build_from_path(in_dir, out_dir, num_workers=16, tqdm=lambda x: x):
-    '''Preprocesses the LJ Speech dataset from a given input path into a given output directory.
-
-      Args:
-        in_dir: The directory where you have downloaded the LJ Speech dataset
-        out_dir: The directory to write the output into
-        num_workers: Optional number of worker processes to parallelize across
-        tqdm: You can optionally pass tqdm to get a nice progress bar
-
-      Returns:
-        A list of tuples describing the training examples. This should be written to train.txt
-    '''
-
-    # We use ProcessPoolExecutor to parallelize across processes. This is just an optimization and you
-    # can omit it and just call _process_utterance on each input if you want.
-
-    executor = ProcessPoolExecutor(max_workers=num_workers)
-    futures = []
+def build_from_path(in_dir, out_dir):
     index = 1
+    out = list()
 
     with open(os.path.join(in_dir, 'metadata.csv'), encoding='utf-8') as f:
         for line in f:
             parts = line.strip().split('|')
             wav_path = os.path.join(in_dir, 'wavs', '%s.wav' % parts[0])
             text = parts[2]
-            futures.append(executor.submit(
-                partial(_process_utterance, out_dir, index, wav_path, text)))
+            out.append(_process_utterance(out_dir, index, wav_path, text))
 
             if index % 100 == 0:
                 print("Done %d" % index)
             index = index + 1
 
-    return [future.result() for future in tqdm(futures)]
+    return out
 
 
 def _process_utterance(out_dir, index, wav_path, text):
-    '''Preprocesses a single utterance audio/text pair.
-
-    This writes the mel and linear scale spectrograms to disk and returns a tuple to write
-    to the train.txt file.
-
-    Args:
-      out_dir: The directory to write the spectrograms into
-      index: The numeric index to use in the spectrogram filenames.
-      wav_path: Path to the audio file containing the speech input
-      text: The text spoken in the input audio file
-
-    Returns:
-      A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
-    '''
-
-    # Load the audio to a numpy array:
-    wav = audio.load_wav(wav_path)
-
-    # Compute the linear-scale spectrogram from the wav:
-    spectrogram = audio.spectrogram(wav).astype(np.float32)
-    n_frames = spectrogram.shape[1]
-
     # Compute a mel-scale spectrogram from the wav:
-    mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
+    mel_spectrogram = Audio.tools.get_mel(wav_path).numpy().astype(np.float32)
+    # print(mel_spectrogram)
 
     # Write the spectrograms to disk:
-    # spectrogram_filename = 'ljspeech-spec-%05d.npy' % index
     mel_filename = 'ljspeech-mel-%05d.npy' % index
     np.save(os.path.join(out_dir, mel_filename),
             mel_spectrogram.T, allow_pickle=False)
 
-    # Return a tuple describing this training example:
-    # return (spectrogram_filename, mel_filename, n_frames, text)
-    return (mel_filename, n_frames, text)
+    return text