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tacotron2
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return audio stream from speech

master
Malar Kannan 2019-06-28 10:25:37 +05:30
parent acf1b444a9
commit 81d15abb4d
1 changed files with 7 additions and 6 deletions
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13
final.py
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@ -18,7 +18,7 @@ from text import text_to_sequence
# from denoiser import Denoiser # from denoiser import Denoiser
import os import os
import soundfile as sf import soundfile as sf
import pyaudio # import pyaudio
import time import time
sys.path.append('waveglow/') sys.path.append('waveglow/')
@ -35,9 +35,9 @@ waveglow.eval()
for k in waveglow.convinv: for k in waveglow.convinv:
k.float() k.float()
audio_interface = pyaudio.PyAudio() # audio_interface = pyaudio.PyAudio()
# _audio_stream = audio_interface.open(format=pyaudio.paFloat32,channels=1, rate=22050,output=True) # # _audio_stream = audio_interface.open(format=pyaudio.paFloat32,channels=1, rate=22050,output=True)
_audio_stream = audio_interface.open(format=pyaudio.paInt16,channels=1, rate=16000,output=True) # _audio_stream = audio_interface.open(format=pyaudio.paInt16,channels=1, rate=16000,output=True)
# https://github.com/NVIDIA/waveglow/issues/127 # https://github.com/NVIDIA/waveglow/issues/127
for m in waveglow.modules(): for m in waveglow.modules():
@ -62,12 +62,13 @@ def speech(t):
mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence) mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
with torch.no_grad(): with torch.no_grad():
audio = waveglow.infer(mel_outputs_postnet, sigma=0.666) audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)
import ipdb; ipdb.set_trace() # import ipdb; ipdb.set_trace()
data = convert(audio[0].data.cpu().numpy()) data = convert(audio[0].data.cpu().numpy())
# _audio_stream.write(data.astype('float32')) # _audio_stream.write(data.astype('float32'))
_audio_stream.write(data) # _audio_stream.write(data)
end = time.time() end = time.time()
print(end - start) print(end - start)
return data
def main(): def main():