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Update app.py
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app.py
CHANGED
@@ -136,33 +136,44 @@ E2TTS_ema_model2 = load_custom(
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"hf://Gregniuki/F5-tts_English_German_Polish/Polish/model_500000.pt", "", F5TTS_model_cfg
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)
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def chunk_text(text, max_chars=100):
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"""
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-
Splits the input text into chunks, each with a maximum number of characters
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Args:
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text (str): The text to be split.
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max_chars (int): The maximum number of characters per chunk.
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Returns:
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List[str]: A list of text chunks.
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"""
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chunks = []
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current_chunk = ""
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# Split the text into sentences based on punctuation followed by whitespace
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sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[;:,。!?])", text)
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for sentence in sentences:
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if
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else:
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if current_chunk:
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chunks.append(current_chunk.strip())
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if current_chunk:
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chunks.append(current_chunk.strip())
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return chunks
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@gpu_decorator
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def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, cross_fade_duration=0.15, progress=gr.Progress()):
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if exp_name == "English":
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"hf://Gregniuki/F5-tts_English_German_Polish/Polish/model_500000.pt", "", F5TTS_model_cfg
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)
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+
import re
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def chunk_text(text, max_chars=100):
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"""
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Splits the input text into chunks, each with a maximum number of characters
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(but splits only after a space if the chunk exceeds the character limit).
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Args:
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text (str): The text to be split.
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max_chars (int): The maximum number of characters per chunk.
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Returns:
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List[str]: A list of text chunks.
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"""
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chunks = []
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current_chunk = ""
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# Split the text into sentences based on punctuation followed by whitespace
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sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[;:,。!?])", text)
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for sentence in sentences:
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# Check if adding this sentence to the current chunk will exceed the max_chars
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if len(current_chunk) + len(sentence) + 1 <= max_chars: # +1 for the space
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current_chunk += sentence + " "
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else:
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if current_chunk:
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# Add the current chunk to the list and reset it
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chunks.append(current_chunk.strip())
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# Start a new chunk with the current sentence
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current_chunk = sentence + " "
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# Append any remaining text in current_chunk to chunks
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if current_chunk:
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chunks.append(current_chunk.strip())
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return chunks
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+
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@gpu_decorator
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def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, cross_fade_duration=0.15, progress=gr.Progress()):
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if exp_name == "English":
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