Commit
β’
c95b8ef
1
Parent(s):
1c36eff
Update app.py
Browse files
app.py
CHANGED
@@ -5,33 +5,18 @@ import torch
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import pyewts
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import noisereduce as nr
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from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
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for word in words:
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# Check if the word is in the dictionary
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if word in word_count:
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# If it has occurred once before, add it to the list with a count of 2
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if word_count[word] == 1:
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new_words.append(word)
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word_count[word] = 2
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else:
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# If it has not occurred before, add it to the dictionary with a count of 1
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word_count[word] = 1
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new_words.append(word)
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# Join the modified words back into a string
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result = ' '.join(new_words)
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return result
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converter = pyewts.pyewts()
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checkpoint = "TenzinGayche/TTS_run3_ep20_174k_b"
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processor = SpeechT5Processor.from_pretrained(checkpoint)
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@@ -65,6 +50,7 @@ def predict(text, speaker):
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return (16000, np.zeros(0).astype(np.int16))
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text = converter.toWylie(text)
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text=cleanup_text(text)
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inputs = processor(text=text, return_tensors="pt")
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# limit input length
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input_ids = inputs["input_ids"]
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import pyewts
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import noisereduce as nr
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from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
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from num2tib.core import convert
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from num2tib.core import convert2text
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import re
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def replace_numbers_with_convert(sentence, wylie=True):
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pattern = r'\d+'
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def replace(match):
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return convert(match.group(), wylie)
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result = re.sub(pattern, replace, sentence)
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return result
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converter = pyewts.pyewts()
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checkpoint = "TenzinGayche/TTS_run3_ep20_174k_b"
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processor = SpeechT5Processor.from_pretrained(checkpoint)
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return (16000, np.zeros(0).astype(np.int16))
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text = converter.toWylie(text)
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text=cleanup_text(text)
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text=replace_numbers_with_convert(text)
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inputs = processor(text=text, return_tensors="pt")
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# limit input length
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input_ids = inputs["input_ids"]
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