took out hparams for preprocessing

datasets/mailabs.py

@@ -1,3 +1,8 @@
+"""mailabs dataset is sampled at 16000 kHz with 0.5 seconds of silence
+    in the start and end of the audio data. Make sure to change the
+    sample_size hparams to match this.
+"""
+
 from concurrent.futures import ProcessPoolExecutor
 from functools import partial
 import numpy as np
@@ -5,11 +10,11 @@ import os
 from util import audio
 
 
-def build_from_path(in_dir, out_dir, books, hparams, num_workers=1, tqdm=lambda x: x):
+def build_from_path(in_dir, out_dir, books, num_workers=1, tqdm=lambda x: x):
   '''Preprocesses the mailabs 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
+      in_dir: The directory where you have downloaded the mailabs 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
@@ -35,12 +40,12 @@ def build_from_path(in_dir, out_dir, books, hparams, num_workers=1, tqdm=lambda
         text = parts[2]
         futures.append(
             executor.submit(partial(
-                _process_utterance, out_dir, name, wav_path, text, hparams)
+                _process_utterance, out_dir, name, wav_path, text)
             ))
   return [future.result() for future in tqdm(futures)]
 
 
-def _process_utterance(out_dir, name, wav_path, text, hparams):
+def _process_utterance(out_dir, name, 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
@@ -57,17 +62,17 @@ def _process_utterance(out_dir, name, wav_path, text, hparams):
   '''
 
   # Load the audio to a numpy array:
-  wav = audio.load_wav(wav_path, hparams)
+  wav = audio.load_wav(wav_path)
 
   # trim silences here
-  wav = audio.trim_silence(wav, hparams)
+  wav = audio.trim_silence(wav)
 
   # Compute the linear-scale spectrogram from the wav:
-  spectrogram = audio.spectrogram(wav, hparams).astype(np.float32)
+  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, hparams).astype(np.float32)
+  mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
 
   # Write the spectrograms to disk:
   spectrogram_filename = 'mailabs-spec-{}.npy'.format(name)

preprocess.py

@@ -3,7 +3,7 @@ import os
 from multiprocessing import cpu_count
 from tqdm import tqdm
 from datasets import amy, blizzard, ljspeech, kusal, mailabs
-from hparams import hparams
+from hparams import hparams, hparams_debug_string
 
 
 def preprocess_blizzard(args):
@@ -47,11 +47,11 @@ def preprocess_mailabs(args):
   os.makedirs(out_dir, exist_ok=True)
   books = args.books
   metadata = mailabs.build_from_path(
-      in_dir, out_dir, books, args.hparams, args.num_workers, tqdm)
+      in_dir, out_dir, books, args.num_workers, tqdm)
-  write_metadata(metadata, out_dir, args.hparams)
+  write_metadata(metadata, out_dir)
 
 
-def write_metadata(metadata, out_dir, hparams=hparams):
+def write_metadata(metadata, out_dir):
   with open(os.path.join(out_dir, 'train.txt'), 'w', encoding='utf-8') as f:
     for m in metadata:
       f.write('|'.join([str(x) for x in m]) + '\n')
@@ -76,9 +76,6 @@ def main():
       '--books',
       help='comma-seperated and no space name of books i.e hunter_space,pink_fairy_book,etc.',
   )
-  parser.add_argument(
-      '--hparams', default='',
-      help='Hyperparameter overrides as a comma-separated list of name=value pairs')
   parser.add_argument('--num_workers', type=int, default=cpu_count())
   args = parser.parse_args()
 
@@ -89,7 +86,7 @@ def main():
     parser.error(
         "--mailabs_books_dir required if mailabs is chosen for dataset.")
 
-  args.hparams = hparams.parse(args.hparams)
+  print(hparams_debug_string())
 
   if args.dataset == 'amy':
     preprocess_amy(args)

util/audio.py

@@ -6,30 +6,29 @@ import tensorflow as tf
 from scipy import signal
 from hparams import hparams
 
-
+def load_wav(path):
-def load_wav(path, hparams=hparams):
   return librosa.core.load(path, sr=hparams.sample_rate)[0]
 
 
-def save_wav(wav, path, hparams=hparams):
+def save_wav(wav, path):
   wav *= 32767 / max(0.01, np.max(np.abs(wav)))
   librosa.output.write_wav(path, wav.astype(np.int16), hparams.sample_rate)
 
 
-def trim_silence(wav, hparams=hparams):
+def trim_silence(wav):
  return librosa.effects.trim(
      wav, top_db=hparams.trim_top_db,
      frame_length=hparams.trim_fft_size,
      hop_length=hparams.trim_hop_size)[0]
 
 
-def spectrogram(y, hparams=hparams):
+def spectrogram(y):
   D = _stft(y)
   S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
   return _normalize(S)
 
 
-def inv_spectrogram(spectrogram, hparams=hparams):
+def inv_spectrogram(spectrogram):
   '''Converts spectrogram to waveform using librosa'''
   S = _db_to_amp(_denormalize(spectrogram) +
                  hparams.ref_level_db)  # Convert back to linear
@@ -37,20 +36,20 @@ def inv_spectrogram(spectrogram, hparams=hparams):
   return _griffin_lim(S ** hparams.power)
 
 
-def inv_spectrogram_tensorflow(spectrogram, hparams=hparams):
+def inv_spectrogram_tensorflow(spectrogram):
   '''Builds computational graph to convert spectrogram to waveform using TensorFlow.'''
   S = _db_to_amp_tensorflow(_denormalize_tensorflow(
       spectrogram) + hparams.ref_level_db)
   return _griffin_lim_tensorflow(tf.pow(S, hparams.power))
 
 
-def melspectrogram(y, hparams=hparams):
+def melspectrogram(y):
   D = _stft(y)
   S = _amp_to_db(_linear_to_mel(np.abs(D))) - hparams.ref_level_db
   return _normalize(S)
 
 
-def find_endpoint(wav, threshold_db=-40, min_silence_sec=0.8, hparams=hparams):
+def find_endpoint(wav, threshold_db=-40, min_silence_sec=0.8):
   window_length = int(hparams.sample_rate * min_silence_sec)
   hop_length = int(window_length / 4)
   threshold = _db_to_amp(threshold_db)
@@ -60,7 +59,7 @@ def find_endpoint(wav, threshold_db=-40, min_silence_sec=0.8, hparams=hparams):
   return len(wav)
 
 
-def _griffin_lim(S, hparams=hparams):
+def _griffin_lim(S):
   '''librosa implementation of Griffin-Lim
   Based on https://github.com/librosa/librosa/issues/434
   '''
@@ -73,7 +72,7 @@ def _griffin_lim(S, hparams=hparams):
   return y
 
 
-def _griffin_lim_tensorflow(S, hparams=hparams):
+def _griffin_lim_tensorflow(S):
   '''TensorFlow implementation of Griffin-Lim
   Based on https://github.com/Kyubyong/tensorflow-exercises/blob/master/Audio_Processing.ipynb
   '''
@@ -109,7 +108,7 @@ def _istft_tensorflow(stfts):
   return tf.contrib.signal.inverse_stft(stfts, win_length, hop_length, n_fft)
 
 
-def _stft_parameters(hparams=hparams):
+def _stft_parameters():
   n_fft = (hparams.num_freq - 1) * 2
   hop_length = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
   win_length = int(hparams.frame_length_ms / 1000 * hparams.sample_rate)
@@ -128,7 +127,7 @@ def _linear_to_mel(spectrogram):
   return np.dot(_mel_basis, spectrogram)
 
 
-def _build_mel_basis(hparams=hparams):
+def _build_mel_basis():
   n_fft = (hparams.num_freq - 1) * 2
   return librosa.filters.mel(hparams.sample_rate, n_fft, n_mels=hparams.num_mels,
                              fmin=hparams.min_mel_freq, fmax=hparams.max_mel_freq)
@@ -146,13 +145,13 @@ def _db_to_amp_tensorflow(x):
   return tf.pow(tf.ones(tf.shape(x)) * 10.0, x * 0.05)
 
 
-def _normalize(S, hparams=hparams):
+def _normalize(S):
   return np.clip((S - hparams.min_level_db) / -hparams.min_level_db, 0, 1)
 
 
-def _denormalize(S, hparams=hparams):
+def _denormalize(S):
   return (np.clip(S, 0, 1) * -hparams.min_level_db) + hparams.min_level_db
 
 
-def _denormalize_tensorflow(S, hparams=hparams):
+def _denormalize_tensorflow(S):
   return (tf.clip_by_value(S, 0, 1) * -hparams.min_level_db) + hparams.min_level_db