1. create a class for the tts api

2. implement a grpc server for tts
3. add a demo client for the grpc service
4. update gitignore and requriements
5. cleanup

.gitignore

@@ -0,0 +1,166 @@
+
+# Created by https://www.gitignore.io/api/python
+# Edit at https://www.gitignore.io/?templates=python
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# End of https://www.gitignore.io/api/python
+
+# Created by https://www.gitignore.io/api/macos
+# Edit at https://www.gitignore.io/?templates=macos
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+# End of https://www.gitignore.io/api/macos
+
+*.pkl

demo_client.py

@@ -0,0 +1,19 @@
+import grpc
+from sia.proto import tts_pb2
+from sia.proto import tts_pb2_grpc
+from tts import player_gen
+
+
+def main():
+    channel = grpc.insecure_channel('localhost:50060')
+    stub = tts_pb2_grpc.ServerStub(channel)
+    test_text = tts_pb2.TextInput(text='How may I help you today?')
+    speech = stub.TextToSpeechAPI(test_text)
+    player = player_gen()
+    player(speech.response)
+    import pdb
+    pdb.set_trace()
+
+
+if __name__ == '__main__':
+    main()

final.py

@@ -22,16 +22,17 @@ import pyaudio
 import klepto
 import IPython.display as ipd
 import time
+from sia.file_utils import cached_model_path
 
 sys.path.append('waveglow/')
 hparams = create_hparams()
 hparams.sampling_rate = 22050
-checkpoint_path = "checkpoint_15000"
 model = load_model(hparams)
+tacotron2_path = cached_model_path("tacotron2_model")
 model.load_state_dict(
-    torch.load(checkpoint_path, map_location='cpu')['state_dict'])
+    torch.load(tacotron2_path, map_location='cpu')['state_dict'])
 model.eval()
-waveglow_path = 'waveglow_256channels.pt'
+waveglow_path = cached_model_path('waveglow_model')
 waveglow = torch.load(waveglow_path, map_location='cpu')['model']
 waveglow.eval()
 for k in waveglow.convinv:
@@ -93,9 +94,10 @@ def player_gen():
 
 def synthesize_corpus():
     all_data = []
-    for line in open('corpus.txt').readlines():
+    for (i, line) in enumerate(open('corpus.txt').readlines()):
         print('synthesizing... "{}"'.format(line.strip()))
         data = speech(line.strip())
+        sf.write('tts_{}.wav'.format(i), data, 16000)
         all_data.append(data)
     return all_data
 

hparams.py

@@ -1,7 +1,8 @@
 import tensorflow as tf
 from text import symbols
 
-#changed path, sampling rate and batch size
+
+# changed path, sampling rate and batch size
 def create_hparams(hparams_string=None, verbose=False):
     """Create model hyperparameters. Parse nondefault from given string."""
 

requirements_dev.txt

@@ -0,0 +1,15 @@
+pip==18.1
+bumpversion==0.5.3
+wheel==0.32.1
+watchdog==0.9.0
+flake8==3.5.0
+tox==3.5.2
+coverage==4.5.1
+Sphinx==1.8.1
+twine==1.12.1
+
+pytest==3.8.2
+pytest-runner==4.2
+pre-commit==1.16.1
+python-language-server[all]
+ipdb

server.py

@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+import grpc
+import time
+from sia.proto import tts_pb2
+from sia.proto import tts_pb2_grpc
+from concurrent import futures
+from sia.instruments import do_time
+from tts import TTSModel
+
+
+class TTSServer():
+    def __init__(self):
+        self.tts_model = TTSModel()
+
+    def TextToSpeechAPI(self, request, context):
+        while (True):
+            input_text = request.text
+            speech_response = self.tts_model.synth_speech(input_text)
+            return tts_pb2.SpeechResponse(response=speech_response)
+
+
+def main():
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    tts_server = TTSServer()
+    tts_pb2_grpc.add_ServerServicer_to_server(tts_server, server)
+    server.add_insecure_port('localhost:50060')
+    server.start()
+    print('TTSServer started!')
+
+    try:
+        while True:
+            time.sleep(10000)
+    except KeyboardInterrupt:
+        server.start()
+        # server.stop(0)
+
+
+if __name__ == "__main__":
+    main()

tts.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# import matplotlib
+# import matplotlib.pylab as plt
+
+# import IPython.display as ipd
+
+import sys
+import numpy as np
+import torch
+from hparams import create_hparams
+from model import Tacotron2
+from layers import TacotronSTFT, STFT
+# from audio_processing import griffin_lim
+from train import load_model
+from text import text_to_sequence
+# from denoiser import Denoiser
+import os
+import soundfile as sf
+import pyaudio
+import klepto
+import IPython.display as ipd
+import time
+from sia.file_utils import cached_model_path
+
+sys.path.append('waveglow/')
+
+
+class TTSModel(object):
+    """docstring for TTSModel."""
+
+    def __init__(self):
+        super(TTSModel, self).__init__()
+        hparams = create_hparams()
+        hparams.sampling_rate = 22050
+        self.model = load_model(hparams)
+        tacotron2_path = cached_model_path("tacotron2_model")
+        self.model.load_state_dict(
+            torch.load(tacotron2_path, map_location='cpu')['state_dict'])
+        self.model.eval()
+        waveglow_path = cached_model_path('waveglow_model')
+        self.waveglow = torch.load(waveglow_path, map_location='cpu')['model']
+        self.waveglow.eval()
+        for k in self.waveglow.convinv:
+            k.float()
+        self.k_cache = klepto.archives.file_archive(cached=False)
+        self.synth_speech = klepto.safe.inf_cache(cache=self.k_cache)(
+            self.synth_speech)
+
+        # https://github.com/NVIDIA/waveglow/issues/127
+        for m in self.waveglow.modules():
+            if 'Conv' in str(type(m)):
+                setattr(m, 'padding_mode', 'zeros')
+
+    def synth_speech(self, t):
+        start = time.time()
+        text = t
+        sequence = np.array(text_to_sequence(text,
+                                             ['english_cleaners']))[None, :]
+        sequence = torch.autograd.Variable(torch.from_numpy(sequence)).long()
+        mel_outputs, mel_outputs_postnet, _, alignments = self.model.inference(
+            sequence)
+        with torch.no_grad():
+            audio = self.waveglow.infer(mel_outputs_postnet, sigma=0.666)
+        # import ipdb; ipdb.set_trace()
+        data = convert(audio[0].data.cpu().numpy())
+        # _audio_stream.write(data.astype('float32'))
+        # _audio_stream.write(data)
+        end = time.time()
+        print(end - start)
+        return data.tobytes()
+
+
+def convert(array):
+    sf.write('sample.wav', array, 22050)
+    os.system('ffmpeg -i {0} -filter:a "atempo=0.80" -ar 16k {1}'.format(
+        'sample.wav', 'sample0.wav'))
+    data, rate = sf.read('sample0.wav', dtype='int16')
+    os.remove('sample.wav')
+    os.remove('sample0.wav')
+    return data
+
+
+def display(data):
+    aud = ipd.Audio(data, rate=16000)
+    return aud
+
+
+def player_gen():
+    audio_interface = pyaudio.PyAudio()
+    _audio_stream = audio_interface.open(format=pyaudio.paInt16,
+                                         channels=1,
+                                         rate=16000,
+                                         output=True)
+
+    def play_device(data):
+        _audio_stream.write(data)
+        # _audio_stream.close()
+
+    return play_device
+
+
+def synthesize_corpus():
+    tts_model = TTSModel()
+    all_data = []
+    for (i, line) in enumerate(open('corpus.txt').readlines()):
+        print('synthesizing... "{}"'.format(line.strip()))
+        data = tts_model.synth_speech(line.strip())
+        all_data.append(data)
+    return all_data
+
+
+def play_corpus(corpus_synths):
+    player = player_gen()
+    for d in corpus_synths:
+        player(d)
+
+
+def main():
+    corpus_synth_data = synthesize_corpus()
+    play_corpus(corpus_synth_data)
+    import ipdb
+    ipdb.set_trace()
+
+
+if __name__ == '__main__':
+    main()