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import whisper
import os
import json
import torchaudio
import argparse
import torch
lang2token = {
'zh': "[ZH]",
'ja': "[JA]",
"en": "[EN]",
}
def transcribe_one(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(beam_size=5)
result = whisper.decode(model, mel, options)
# print the recognized text
print(result.text)
return lang, result.text
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--languages", default="CJE")
parser.add_argument("--whisper_size", default="medium")
args = parser.parse_args()
if args.languages == "CJE":
lang2token = {
'zh': "[ZH]",
'ja': "[JA]",
"en": "[EN]",
}
elif args.languages == "CJ":
lang2token = {
'zh': "[ZH]",
'ja': "[JA]",
}
elif args.languages == "C":
lang2token = {
'zh': "[ZH]",
}
assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
model = whisper.load_model(args.whisper_size)
parent_dir = "./custom_character_voice/"
speaker_names = list(os.walk(parent_dir))[0][1]
speaker_annos = []
total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
# resample audios
# 2023/4/21: Get the target sampling rate
with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
hps = json.load(f)
target_sr = hps['data']['sampling_rate']
processed_files = 0
for speaker in speaker_names:
for i, wavfile in enumerate(list(os.walk(parent_dir + speaker))[0][2]):
# try to load file as audio
if wavfile.startswith("processed_"):
continue
try:
wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
channels_first=True)
wav = wav.mean(dim=0).unsqueeze(0)
if sr != target_sr:
wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
if wav.shape[1] / sr > 20:
print(f"{wavfile} too long, ignoring\n")
save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
torchaudio.save(save_path, wav, target_sr, channels_first=True)
# transcribe text
lang, text = transcribe_one(save_path)
if lang not in list(lang2token.keys()):
print(f"{lang} not supported, ignoring\n")
continue
text = lang2token[lang] + text + lang2token[lang] + "\n"
speaker_annos.append(save_path + "|" + speaker + "|" + text)
processed_files += 1
print(f"Processed: {processed_files}/{total_files}")
except:
continue
# # clean annotation
# import argparse
# import text
# from utils import load_filepaths_and_text
# for i, line in enumerate(speaker_annos):
# path, sid, txt = line.split("|")
# cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
# cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
# speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
# write into annotation
if len(speaker_annos) == 0:
print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
with open("short_character_anno.txt", 'w', encoding='utf-8') as f:
for line in speaker_annos:
f.write(line)
# import json
# # generate new config
# with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
# hps = json.load(f)
# # modify n_speakers
# hps['data']["n_speakers"] = 1000 + len(speaker2id)
# # add speaker names
# for speaker in speaker_names:
# hps['speakers'][speaker] = speaker2id[speaker]
# # save modified config
# with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
# json.dump(hps, f, indent=2)
# print("finished")
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