forked from mrq/tortoise-tts
re-enable redaction
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9151650559
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@ -165,7 +165,7 @@ class TextToSpeech:
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Main entry point into Tortoise.
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Main entry point into Tortoise.
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"""
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"""
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def __init__(self, autoregressive_batch_size=16, models_dir='.models', enable_redaction=False):
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def __init__(self, autoregressive_batch_size=16, models_dir='.models', enable_redaction=True):
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"""
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"""
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Constructor
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Constructor
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:param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
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:param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
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@ -50,7 +50,7 @@ class Wav2VecAlignment:
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def __init__(self):
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def __init__(self):
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self.model = Wav2Vec2ForCTC.from_pretrained("jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli").cpu()
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self.model = Wav2Vec2ForCTC.from_pretrained("jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli").cpu()
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self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"facebook/wav2vec2-large-960h")
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self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"facebook/wav2vec2-large-960h")
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self.tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron_symbols')
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self.tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron-symbols')
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def align(self, audio, expected_text, audio_sample_rate=24000):
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def align(self, audio, expected_text, audio_sample_rate=24000):
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orig_len = audio.shape[-1]
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orig_len = audio.shape[-1]
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