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import whisper
import os
import json
import torchaudio
import argparse
import torch
from tqdm import tqdm
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--whisper_size", default="large")
args = parser.parse_args()
#assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
model = whisper.load_model(args.whisper_size, device="cpu")
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/amitaro_jp_base.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:
filelist = (list(os.walk(parent_dir + speaker))[0][2])
for i, wavfile in tqdm(enumerate(filelist), desc="Processing Audio:", total=len(filelist)):
# 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)
audio = whisper.load_audio(save_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)
options = whisper.DecodingOptions(beam_size=5, language="ja", fp16 = False)
result = whisper.decode(model, mel, options)
text = "[JA]"+ result.text + "[JA]\n"
speaker_annos.append(save_path + "|" + speaker + "|" + text)
processed_files += 1
#print(f"Processed: {processed_files}/{total_files}")
#except:
# print(f"Error occurred: {wavfile}")
# 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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