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import os
import torch
import torchaudio
import logging
import langid
import whisper
langid.set_languages(['en', 'zh', 'ja'])
import numpy as np
from data.tokenizer import (
AudioTokenizer,
tokenize_audio,
)
from data.collation import get_text_token_collater
from utils.g2p import PhonemeBpeTokenizer
from macros import *
text_tokenizer = PhonemeBpeTokenizer(tokenizer_path="./utils/g2p/bpe_69.json")
text_collater = get_text_token_collater()
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
if torch.backends.mps.is_available():
device = torch.device("mps")
codec = AudioTokenizer(device)
if not os.path.exists("./whisper/"): os.mkdir("./whisper/")
whisper_model = None
@torch.no_grad()
def transcribe_one(model, audio_path):
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(audio_path)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
lang = max(probs, key=probs.get)
# decode the audio
options = whisper.DecodingOptions(temperature=1.0, best_of=5, fp16=False if device == torch.device("cpu") else True, sample_len=150)
result = whisper.decode(model, mel, options)
# print the recognized text
print(result.text)
text_pr = result.text
if text_pr.strip(" ")[-1] not in "?!.,。，？！。、":
text_pr += "."
return lang, text_pr
def make_prompt(name, audio_prompt_path, transcript=None):
global model, text_collater, text_tokenizer, codec
wav_pr, sr = torchaudio.load(audio_prompt_path)
# check length
if wav_pr.size(-1) / sr > 15:
raise ValueError(f"Prompt too long, expect length below 15 seconds, got {wav_pr / sr} seconds.")
if wav_pr.size(0) == 2:
wav_pr = wav_pr.mean(0, keepdim=True)
text_pr, lang_pr = make_transcript(name, wav_pr, sr, transcript)
# tokenize audio
encoded_frames = tokenize_audio(codec, (wav_pr, sr))
audio_tokens = encoded_frames[0][0].transpose(2, 1).cpu().numpy()
# tokenize text
phonemes, langs = text_tokenizer.tokenize(text=f"{text_pr}".strip())
text_tokens, enroll_x_lens = text_collater(
[
phonemes
]
)
message = f"Detected language: {lang_pr}\n Detected text {text_pr}\n"
# save as npz file
save_path = os.path.join("./customs/", f"{name}.npz")
np.savez(save_path, audio_tokens=audio_tokens, text_tokens=text_tokens, lang_code=lang2code[lang_pr])
logging.info(f"Successful. Prompt saved to {save_path}")
def make_transcript(name, wav, sr, transcript=None):
if not isinstance(wav, torch.FloatTensor):
wav = torch.tensor(wav)
if wav.abs().max() > 1:
wav /= wav.abs().max()
if wav.size(-1) == 2:
wav = wav.mean(-1, keepdim=False)
if wav.ndim == 1:
wav = wav.unsqueeze(0)
assert wav.ndim and wav.size(0) == 1
if transcript is None or transcript == "":
logging.info("Transcript not given, using Whisper...")
global whisper_model
if whisper_model is None:
whisper_model = whisper.load_model("medium", download_root=os.path.join(os.getcwd(), "whisper"))
whisper_model.to(device)
torchaudio.save(f"./prompts/{name}.wav", wav, sr)
lang, text = transcribe_one(whisper_model, f"./prompts/{name}.wav")
lang_token = lang2token[lang]
text = lang_token + text + lang_token
os.remove(f"./prompts/{name}.wav")
whisper_model.cpu()
else:
text = transcript
lang, _ = langid.classify(text)
lang_token = lang2token[lang]
text = lang_token + text + lang_token
torch.cuda.empty_cache()
return text, lang
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