74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
import re
|
|
from tortoise.api import TextToSpeech
|
|
from tortoise.utils.audio import load_voice
|
|
|
|
def clean_text(text: str, target_len: int = 200, max_len: int = 300) -> list[str]:
|
|
# remove double new line, redundant whitespace, convert non-ascii quotes to ascii quotes
|
|
text = re.sub(r"\n\n+", r"\n", text)
|
|
text = re.sub(r"\s+", r" ", text)
|
|
text = re.sub(r"[“”]", '"', text)
|
|
|
|
# split text into sentences, keep quotes together
|
|
sentences = re.split(r'(?<=[.!?])\s+(?=(?:[^"]*"[^"]*")*[^"]*$)', text)
|
|
|
|
# recombine sentences into chunks of desired length
|
|
chunks = []
|
|
chunk = ""
|
|
for sentence in sentences:
|
|
if len(chunk) + len(sentence) > target_len:
|
|
chunks.append(chunk)
|
|
chunk = ""
|
|
chunk += sentence + " "
|
|
if len(chunk) > max_len:
|
|
chunks.append(chunk)
|
|
chunk = ""
|
|
if chunk:
|
|
chunks.append(chunk)
|
|
|
|
# clean up chunks, remove leading/trailing whitespace, remove empty/unless chunks
|
|
chunks = [s.strip() for s in chunks]
|
|
chunks = [s for s in chunks if s and not re.match(r"^[\s\.,;:!?]*$", s)]
|
|
|
|
return chunks
|
|
|
|
|
|
def process_textfile(file_path: str) -> list[str]:
|
|
with open(file_path, "r", encoding="utf-8") as f:
|
|
text = " ".join([l for l in f.readlines()])
|
|
text = clean_text(text)
|
|
return text
|
|
|
|
def tts(file_path: str):
|
|
# load tts model
|
|
# ADD PATH
|
|
tts = TextToSpeech(
|
|
autoregressive_model_path="./ai-voice-cloning/training/"
|
|
)
|
|
voice = "Lex"
|
|
voice_samples, conditioning_latents = load_voice(
|
|
voice, extra_voice_dirs="./ai-voice-cloning/voices"
|
|
)
|
|
|
|
# process text file
|
|
texts = process_textfile(file_path)
|
|
|
|
# generate audio for each chunk of text
|
|
all_audio_chunks = []
|
|
for i, text in enumerate(texts):
|
|
gen = tts.tts(
|
|
text=text,
|
|
voice=voice,
|
|
voice_samples=voice_samples,
|
|
conditioning_latents=conditioning_latents,
|
|
)
|
|
torchaudio.save(f"./audio/raw/{i}.wav", gen.squeeze(0).cpu(), 24000)
|
|
|
|
all_audio_chunks.append(gen)
|
|
|
|
book_name_ext = os.path.basename(file_path)
|
|
paper_name = os.path.splitext(book_name_ext)[0]
|
|
|
|
# concatenate all audio chunks
|
|
full_audio = torch.cat(all_audio_chunks, dim=-1)
|
|
torchaudio.save(f"./audio/raw/{paper_name}.wav", full_audio, 24000)
|