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Merge branch 'dev'

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This commit is contained in:
erogol
2020-05-20 18:31:58 +02:00
parent 2e2221f146 46522e213e
commit 9d7cb1e3b2
57 changed files with 2637 additions and 1599 deletions
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server/README.md
+1 -1
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@@ -7,7 +7,7 @@ Instructions below are based on a Ubuntu 18.04 machine, but it should be simple
#### Development server:
##### Using server.py
If you have the environment set already for TTS, then you can directly call ```setup.py```.
If you have the environment set already for TTS, then you can directly call ```server.py```.
##### Using .whl
1. apt-get install -y espeak libsndfile1 python3-venv
server/synthesizer.py
+14 -7
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@@ -7,7 +7,8 @@ import torch
import yaml
from TTS.utils.audio import AudioProcessor
from TTS.utils.generic_utils import load_config, setup_model
from TTS.utils.io import load_config
from TTS.utils.generic_utils import setup_model
from TTS.utils.speakers import load_speaker_mapping
# pylint: disable=unused-wildcard-import
# pylint: disable=wildcard-import
@@ -164,19 +165,25 @@ class Synthesizer(object):
sentences = list(filter(None, [s.strip() for s in sentences])) # remove empty sentences
return sentences
def tts(self, text):
def tts(self, text, speaker_id=None):
wavs = []
sens = self.split_into_sentences(text)
print(sens)
speaker_id = id_to_torch(speaker_id)
if speaker_id is not None and self.use_cuda:
speaker_id = speaker_id.cuda()
for sen in sens:
# preprocess the given text
inputs = text_to_seqvec(sen, self.tts_config, self.use_cuda)
inputs = text_to_seqvec(sen, self.tts_config)
inputs = numpy_to_torch(inputs, torch.long, cuda=self.use_cuda)
inputs = inputs.unsqueeze(0)
# synthesize voice
decoder_output, postnet_output, alignments, _ = run_model(
self.tts_model, inputs, self.tts_config, False, None, None)
decoder_output, postnet_output, alignments, stop_tokens = run_model_torch(
self.tts_model, inputs, self.tts_config, False, speaker_id, None)
# convert outputs to numpy
postnet_output, decoder_output, _ = parse_outputs(
postnet_output, decoder_output, alignments)
postnet_output, decoder_output, _, _ = parse_outputs_torch(
postnet_output, decoder_output, alignments, stop_tokens)
if self.pwgan:
vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)