| 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'): |
| wav = self.set_tempo(wav, '0.85') |
| wav = self.trim_silence(wav) |
| elif (lang == 'mr') and (gender=='female'): |
| wav = self.trim_silence(wav) |
| wav = self.set_tempo(wav, '1.15') |
| elif (lang == 'gu'): |
| |
| wav = self.set_tempo(wav, '1.20') |
|
|
| return wav |
|
|
