forked from mrq/ai-voice-cloning
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
import re
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from tortoise.api import TextToSpeech
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from tortoise.utils.audio import load_voice
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def clean_text(text: str, target_len: int = 200, max_len: int = 300) -> list[str]:
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# remove double new line, redundant whitespace, convert non-ascii quotes to ascii quotes
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text = re.sub(r"\n\n+", r"\n", text)
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text = re.sub(r"\s+", r" ", text)
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text = re.sub(r"[“”]", '"', text)
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# split text into sentences, keep quotes together
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sentences = re.split(r'(?<=[.!?])\s+(?=(?:[^"]*"[^"]*")*[^"]*$)', text)
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# recombine sentences into chunks of desired length
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chunks = []
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chunk = ""
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for sentence in sentences:
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if len(chunk) + len(sentence) > target_len:
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chunks.append(chunk)
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chunk = ""
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chunk += sentence + " "
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if len(chunk) > max_len:
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chunks.append(chunk)
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chunk = ""
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if chunk:
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chunks.append(chunk)
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# clean up chunks, remove leading/trailing whitespace, remove empty/unless chunks
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chunks = [s.strip() for s in chunks]
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chunks = [s for s in chunks if s and not re.match(r"^[\s\.,;:!?]*$", s)]
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return chunks
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def process_textfile(file_path: str) -> list[str]:
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with open(file_path, "r", encoding="utf-8") as f:
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text = " ".join([l for l in f.readlines()])
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text = clean_text(text)
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return text
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def tts(paper_name: str):
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# load tts model
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# ADD PATH
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tts = TextToSpeech(
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autoregressive_model_path="./ai-voice-cloning/training/"
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)
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voice = "Lex"
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voice_samples, conditioning_latents = load_voice(
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voice, extra_voice_dirs="./ai-voice-cloning/voices"
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)
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# process text file
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texts = process_textfile(f"./llm/scripts/{paper_name}.txt")
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# generate audio for each chunk of text
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all_audio_chunks = []
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for i, text in enumerate(texts):
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gen = tts.tts(
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text=text,
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voice=voice,
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voice_samples=voice_samples,
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conditioning_latents=conditioning_latents,
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)
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torchaudio.save(f"./audio/raw/{i}.wav", gen.squeeze(0).cpu(), 24000)
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all_audio_chunks.append(gen)
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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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torchaudio.save(f"./audio/raw/{paper_name}.wav", full_audio, 24000)
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