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import os
import ffmpeg
import librosa
import numpy as np
import soundfile as sf
import tempfile
from .vad import VoiceActivityDetection
class PostProcessor:
def __init__(self, target_sr:int):
self.target_sr = target_sr
self.vad = VoiceActivityDetection()
def set_tempo(self, wav:np.ndarray, atempo:str ='1'):
with tempfile.TemporaryDirectory() as tmpdirname:
inpath = os.path.join(tmpdirname, 'input.wav')
outpath = inpath.replace('input.wav', 'output.wav')
sf.write(inpath, wav, self.target_sr)
in_stream = ffmpeg.input(inpath)
audio_stream = ffmpeg.filter_(in_stream, 'atempo', atempo)
audio_stream = audio_stream.output(outpath)
ffmpeg.run(audio_stream, overwrite_output=True)
wav, _ = librosa.load(outpath, sr=self.target_sr)
return wav
def trim_silence(self, wav:np.ndarray):
return self.vad.process(wav, sc_threshold=40)
def process(self, wav, lang:str, gender:str):
if type(wav) != np.ndarray:
wav = np.array(wav)
if (lang == "te") and (gender=='female'): # Telugu female speaker slow down
wav = self.set_tempo(wav, '0.85')
wav = self.trim_silence(wav)
elif (lang == 'mr') and (gender=='female'): # Marathi female speaker speed up
wav = self.trim_silence(wav)
wav = self.set_tempo(wav, '1.15')
elif (lang == 'gu'): # Gujarati speaker speed up
# wav = trim_silence(wav)
wav = self.set_tempo(wav, '1.20')
return wav
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