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