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Update app.py
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app.py
CHANGED
@@ -12,7 +12,6 @@ import numpy as np
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import torch
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from InferenceInterfaces.UtteranceCloner import UtteranceCloner
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from Preprocessing.TextFrontend import ArticulatoryCombinedTextFrontend
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from TrainingInterfaces.Text_to_Spectrogram.AutoAligner.Aligner import Aligner
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from TrainingInterfaces.Text_to_Spectrogram.FastSpeech2.DurationCalculator import DurationCalculator
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@@ -38,11 +37,6 @@ class TTS_Interface:
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def __init__(self):
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.utterance_cloner = UtteranceCloner(model_id="Meta", device=self.device)
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# for simplicity, since we are using an oracle for this demo, and we have seen enough German data to get by without word boundaries
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self.utterance_cloner.tf.use_word_boundaries = False
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self.utterance_cloner.tts.text2phone.use_word_boundaries = False
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self.utterance_cloner.tts.set_language("de")
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self.acoustic_model = Aligner()
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self.acoustic_model.load_state_dict(torch.load("Models/Aligner/aligner.pt", map_location='cpu')["asr_model"])
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@@ -52,6 +46,9 @@ class TTS_Interface:
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reference_audio = "reference_audios/2.wav"
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self.duration, self.pitch, self.energy, _, _ = self.utterance_cloner.extract_prosody(self.text, reference_audio, lang="de", on_line_fine_tune=True)
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self.phones = self.utterance_cloner.tts.text2phone.get_phone_string(self.text)
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#######
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self.utterance_cloner.tts.set_utterance_embedding("reference_audios/german_male.wav")
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@@ -95,35 +92,35 @@ class TTS_Interface:
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duration = self.duration.clone()
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# lengthening
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lenghtening_candidates = [
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# ('f',
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# ('l',
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('ʏ',
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('ç',
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# ('t',
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('ɪ',
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# ('ɡ',
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('ə',
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('n',
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# ('z',
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('ɑ',
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# ('ə',
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('n',
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# ('b',
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('e',
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# ('p',
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# ('t',
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('ə',
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]
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for lenghtening_candidate in lenghtening_candidates:
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duration[lenghtening_candidate[1]] = duration[lenghtening_candidate[1]] + lengthening
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# pauses
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pause_candidates = [('~',
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('~',
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('~',
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for pause_candidate in pause_candidates:
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duration[pause_candidate[1]] = duration[pause_candidate[1]] + pause_dur
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@@ -132,38 +129,38 @@ class TTS_Interface:
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# pitch raise
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pitch_candidates = [
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# ('k',
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('y',
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('l',
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('ə',
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('ʃ',
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('a',
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('t',
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# ('ə',
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# ('n',
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('a',
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('l',
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('v',
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('ɛ',
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('l',
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# ('ə',
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# ('n',
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]
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for pitch_candidate in pitch_candidates:
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pitch[pitch_candidate[1]] = pitch[pitch_candidate[1]] + pitch_up
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fixme = [('f',
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('l',
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('ʏ',
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('ç',
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('t',
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('ɪ',
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('ɡ',
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('ə',
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('n',
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]
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for pitch_candidate in fixme:
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pitch[pitch_candidate[1]] = pitch[pitch_candidate[1]] - abs(pitch_up)
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import torch
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from InferenceInterfaces.UtteranceCloner import UtteranceCloner
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from TrainingInterfaces.Text_to_Spectrogram.AutoAligner.Aligner import Aligner
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from TrainingInterfaces.Text_to_Spectrogram.FastSpeech2.DurationCalculator import DurationCalculator
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def __init__(self):
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.utterance_cloner = UtteranceCloner(model_id="Meta", device=self.device)
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self.utterance_cloner.tts.set_language("de")
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self.acoustic_model = Aligner()
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self.acoustic_model.load_state_dict(torch.load("Models/Aligner/aligner.pt", map_location='cpu')["asr_model"])
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reference_audio = "reference_audios/2.wav"
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self.duration, self.pitch, self.energy, _, _ = self.utterance_cloner.extract_prosody(self.text, reference_audio, lang="de", on_line_fine_tune=True)
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self.phones = self.utterance_cloner.tts.text2phone.get_phone_string(self.text)
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print(self.phones)
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for index, phone in enumerate(self.phones):
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print(index, phone)
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#######
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self.utterance_cloner.tts.set_utterance_embedding("reference_audios/german_male.wav")
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duration = self.duration.clone()
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# lengthening
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lenghtening_candidates = [
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# ('f', 33),
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# ('l', 34),
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('ʏ', 35),
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('ç', 36),
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# ('t', 37),
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('ɪ', 38),
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# ('ɡ', 39),
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('ə', 40),
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('n', 41),
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# ('z', 79),
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('ɑ', 80),
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# ('ə', 81),
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('n', 82),
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# ('b', 103),
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('e', 104),
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# ('p', 105),
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# ('t', 106),
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('ə', 107)
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]
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for lenghtening_candidate in lenghtening_candidates:
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duration[lenghtening_candidate[1]] = duration[lenghtening_candidate[1]] + lengthening
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# pauses
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pause_candidates = [('~', 42),
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('~', 83),
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('~', 108)]
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for pause_candidate in pause_candidates:
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duration[pause_candidate[1]] = duration[pause_candidate[1]] + pause_dur
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# pitch raise
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pitch_candidates = [
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# ('k', 44),
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('y', 45),
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('l', 46),
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('ə', 47),
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('ʃ', 49),
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('a', 50),
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('t', 51),
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# ('ə', 52),
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# ('n', 53),
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('a', 85),
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('l', 86),
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('v', 118),
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('ɛ', 119),
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('l', 120),
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# ('ə', 121),
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# ('n', 122)
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]
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for pitch_candidate in pitch_candidates:
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pitch[pitch_candidate[1]] = pitch[pitch_candidate[1]] + pitch_up
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fixme = [('f', 33),
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('l', 34),
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('ʏ', 35),
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('ç', 36),
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('t', 37),
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('ɪ', 38),
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('ɡ', 39),
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('ə', 40),
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('n', 41)
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]
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for pitch_candidate in fixme:
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pitch[pitch_candidate[1]] = pitch[pitch_candidate[1]] - abs(pitch_up)
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