[FastPitch/PyT] Drop parselmouth dependency

PyTorch/SpeechSynthesis/FastPitch/README.md

@@ -363,16 +363,11 @@ FastPitch 1.1 aligns input symbols to output mel-spectrogram frames automaticall
 on any external aligning model. FastPitch training can now be started on raw waveforms
 without any pre-processing: pitch values and mel-spectrograms will be calculated on-line.
 
-For every mel-spectrogram frame, its fundamental frequency in Hz is estimated with either
-the Probabilistic YIN algorithm or [Praat](http://praat.org).
-
-The former is more accurate but time consuming, and we recommend to pre-calculate
-pitch during the data processing step. The latter is suitable for on-line pitch calculation.
-Pitch values are then averaged over every character, in order to provide sparse
-pitch cues for the model.
+For every mel-spectrogram frame, its fundamental frequency in Hz is estimated with
+the Probabilistic YIN algorithm.
 
 <p align="center">
-  <img src="./img/pitch.png" alt="Pitch estimates extracted with Praat" />
+  <img src="./img/pitch.png" alt="Pitch contour estimate" />
 </p>
 <p align="center">
   <em>Figure 2. Pitch estimates for mel-spectrogram frames of phrase "in being comparatively"

PyTorch/SpeechSynthesis/FastPitch/fastpitch/data_function.py

@@ -32,7 +32,6 @@ from pathlib import Path
 
 import librosa
 import numpy as np
-import parselmouth
 import torch
 import torch.nn.functional as F
 from scipy import ndimage
@@ -88,35 +87,7 @@ def estimate_pitch(wav, mel_len, method='pyin', normalize_mean=None,
     if type(normalize_std) is float or type(normalize_std) is list:
         normalize_std = torch.tensor(normalize_std)
 
-    if method == 'praat':
-
-        snd = parselmouth.Sound(wav)
-        pitch_mel = snd.to_pitch(time_step=snd.duration / (mel_len + 3)
-                                 ).selected_array['frequency']
-        assert np.abs(mel_len - pitch_mel.shape[0]) <= 1.0
-
-        pitch_mel = torch.from_numpy(pitch_mel).unsqueeze(0)
-
-        if n_formants > 1:
-            formant = snd.to_formant_burg(
-                time_step=snd.duration / (mel_len + 3))
-            formant_n_frames = formant.get_number_of_frames()
-            assert np.abs(mel_len - formant_n_frames) <= 1.0
-
-            formants_mel = np.zeros((formant_n_frames + 1, n_formants - 1))
-            for i in range(1, formant_n_frames + 1):
-                formants_mel[i] = np.asarray([
-                    formant.get_value_at_time(
-                        formant_number=f,
-                        time=formant.get_time_from_frame_number(i))
-                    for f in range(1, n_formants)
-                ])
-
-            pitch_mel = torch.cat(
-                [pitch_mel, torch.from_numpy(formants_mel).permute(1, 0)],
-                dim=0)
-
-    elif method == 'pyin':
+    if method == 'pyin':
 
         snd, sr = librosa.load(wav)
         pitch_mel, voiced_flag, voiced_probs = librosa.pyin(
@@ -181,7 +152,7 @@ class TTSDataset(torch.utils.data.Dataset):
                  pitch_online_dir=None,
                  betabinomial_online_dir=None,
                  use_betabinomial_interpolator=True,
-                 pitch_online_method='praat',
+                 pitch_online_method='pyin',
                  **ignored):
 
         # Expect a list of filenames
@@ -338,7 +309,7 @@ class TTSDataset(torch.utils.data.Dataset):
             if cached_fpath.is_file():
                 return torch.load(cached_fpath)
 
-        # No luck so far - calculate or replace with praat
+        # No luck so far - calculate
         wav = audiopath
         if not wav.endswith('.wav'):
             wav = re.sub('/mels/', '/wavs/', wav)

PyTorch/SpeechSynthesis/FastPitch/prepare_dataset.py

@@ -73,7 +73,7 @@ def parse_args(parser):
     parser.add_argument('--n-mel-channels', type=int, default=80)
     # Pitch extraction
     parser.add_argument('--f0-method', default='pyin', type=str,
-                        choices=('pyin', 'praat'), help='F0 estimation method')
+                        choices=['pyin'], help='F0 estimation method')
     # Performance
     parser.add_argument('-b', '--batch-size', default=1, type=int)
     parser.add_argument('--n-workers', type=int, default=16)

PyTorch/SpeechSynthesis/FastPitch/requirements.txt

@@ -4,6 +4,5 @@ inflect
 librosa==0.8.0
 scipy
 Unidecode
-praat-parselmouth==0.3.3
 tensorboardX==2.0
 git+git://github.com/NVIDIA/dllogger.git@26a0f8f1958de2c0c460925ff6102a4d2486d6cc#egg=dllogger

PyTorch/SpeechSynthesis/FastPitch/scripts/prepare_dataset.sh

@@ -3,7 +3,6 @@
 set -e
 
 : ${DATA_DIR:=LJSpeech-1.1}
-: ${F0_METHOD:="pyin"}
 : ${ARGS="--extract-mels"}
 
 python prepare_dataset.py \
@@ -12,5 +11,5 @@ python prepare_dataset.py \
     --batch-size 1 \
     --dataset-path $DATA_DIR \
     --extract-pitch \
-    --f0-method $F0_METHOD \
+    --f0-method pyin \
     $ARGS

PyTorch/SpeechSynthesis/FastPitch/scripts/train_benchmark.sh

@@ -6,7 +6,6 @@ set -a
 : ${NUM_GPUS_SEQUENCE:="1 4 8"}
 : ${EPOCHS:=30}
 : ${OUTPUT_DIR:="./output"}
-: ${F0_METHOD:=praat}
 : ${BATCH_SIZE:=16}
 
 for NUM_GPUS in $NUM_GPUS_SEQUENCE ; do

PyTorch/SpeechSynthesis/FastPitch/train.py

@@ -147,8 +147,8 @@ def parse_args(parser):
                            'n_speakers > 1 enables speaker embeddings')
     cond.add_argument('--load-pitch-from-disk', action='store_true',
                       help='Use pitch cached on disk with prepare_dataset.py')
-    cond.add_argument('--pitch-online-method', default='praat',
-                      choices=['praat', 'pyin'],
+    cond.add_argument('--pitch-online-method', default='pyin',
+                      choices=['pyin'],
                       help='Calculate pitch on the fly during trainig')
     cond.add_argument('--pitch-online-dir', type=str, default=None,
                       help='A directory for storing pitch calculated on-line')