forked from mrq/ai-voice-cloning
updating utils
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02e3a46700
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@ -38,7 +38,7 @@ def process_textfile(file_path: str) -> list[str]:
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text = clean_text(text)
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text = clean_text(text)
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return text
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return text
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def tts(paper_name: str):
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def tts(file_path: str):
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# load tts model
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# load tts model
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# ADD PATH
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# ADD PATH
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tts = TextToSpeech(
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tts = TextToSpeech(
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@ -50,7 +50,7 @@ def tts(paper_name: str):
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)
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)
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# process text file
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# process text file
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texts = process_textfile(f"./llm/scripts/{paper_name}.txt")
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texts = process_textfile(file_path)
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# generate audio for each chunk of text
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# generate audio for each chunk of text
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all_audio_chunks = []
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all_audio_chunks = []
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@ -65,6 +65,9 @@ def tts(paper_name: str):
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all_audio_chunks.append(gen)
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all_audio_chunks.append(gen)
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book_name_ext = os.path.basename(file_path)
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paper_name = os.path.splitext(book_name_ext)[0]
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# concatenate all audio chunks
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# concatenate all audio chunks
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full_audio = torch.cat(all_audio_chunks, dim=-1)
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full_audio = torch.cat(all_audio_chunks, dim=-1)
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torchaudio.save(f"./audio/raw/{paper_name}.wav", full_audio, 24000)
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torchaudio.save(f"./audio/raw/{paper_name}.wav", full_audio, 24000)
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