added helper scripts to process LibriTTS/LibriLight, detect duplicate speaker+books between them, and script to directly phonemize and quantize LibriTTS
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scripts/deduplicate_librilight_libritts.py
Executable file
45
scripts/deduplicate_librilight_libritts.py
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import os
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import json
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librilight_dir = "LibriLight-6K"
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libritts_dir = "LibriTTS-Train"
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librilight_data = {}
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libritts_data = {}
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for speaker_id in os.listdir(f'./{librilight_dir}/'):
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for filename in os.listdir(f'./{librilight_dir}/{speaker_id}'):
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parts = filename.split("_")
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book_id = parts[1]
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subid = parts[2]
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if speaker_id not in librilight_data:
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librilight_data[speaker_id] = {}
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if book_id not in librilight_data[speaker_id]:
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librilight_data[speaker_id][book_id] = []
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librilight_data[speaker_id][book_id].append(subid)
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for speaker_id in os.listdir(f'./{libritts_dir}/'):
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for filename in os.listdir(f'./{libritts_dir}/{speaker_id}'):
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parts = filename.split("_")
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book_id = parts[1]
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subid = parts[2]
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if speaker_id not in libritts_data:
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libritts_data[speaker_id] = {}
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if book_id not in libritts_data[speaker_id]:
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libritts_data[speaker_id][book_id] = []
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libritts_data[speaker_id][book_id].append(subid)
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duplicates = []
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for speaker_id, books in libritts_data.items():
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if speaker_id not in librilight_data:
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continue
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for book_id, _ in books.items():
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if book_id not in librilight_data[speaker_id]:
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continue
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print(f'Duplicate: {speaker_id}/{book_id}')
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duplicates.append(f'{speaker_id}/{book_id}')
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print("Duplicates:", duplicates)
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#!/bin/bash
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# do not invoke directly in scripts
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if [[ ${PWD##*/} == 'scripts' ]]; then
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cd ..
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fi
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# download training data
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git clone https://huggingface.co/datasets/ecker/libritts-small ./data/libritts-small
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import os
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import json
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for f in os.listdir(f'./data/librispeech_finetuning/1h/'):
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for j in os.listdir(f'./data/librispeech_finetuning/1h/{f}/clean'):
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for z in os.listdir(f'./data/librispeech_finetuning/1h/{f}/clean/{j}'):
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for i in os.listdir(f'./data/librispeech_finetuning/1h/{f}/clean/{j}/{z}'):
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os.rename(f'./data/librispeech_finetuning/1h/{f}/clean/{j}/{z}/{i}', f'./data/librilight-tts/{i}')
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for j in os.listdir('./data/librispeech_finetuning/9h/clean'):
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for z in os.listdir(f'./data/librispeech_finetuning/9h/clean/{j}'):
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for i in os.listdir(f'./data/librispeech_finetuning/9h/clean/{j}/{z}'):
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os.rename(f'./data/librispeech_finetuning/9h/clean/{j}/{z}/{i}', f'./data/librilight-tts/{i}')
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lst = []
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for i in os.listdir('./data/librilight-tts/'):
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try:
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if 'trans' not in i:
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continue
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with open(f'./data/librilight-tts/{i}') as f:
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for row in f:
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z = row.split('-')
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name = z[0]+'-'+z[1]+ '-' + z[2].split(' ')[0]
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text = " ".join(z[2].split(' ')[1:])
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lst.append([name, text])
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except Exception as e:
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pass
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for i in lst:
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try:
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with open(f'./data/librilight-tts/{i[0]}.txt', 'x') as file:
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file.write(i[1])
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except:
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with open(f'./data/librilight-tts/{i[0]}.txt', 'w+') as file:
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file.write(i[1])
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phoneme_map = {}
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phoneme_transcript = {}
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with open('./data/librispeech_finetuning/phones/phones_mapping.json', 'r') as f:
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phoneme_map_rev = json.load(f)
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for k, v in phoneme_map_rev.items():
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phoneme_map[f'{v}'] = k
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with open('./data/librispeech_finetuning/phones/10h_phones.txt', 'r') as f:
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lines = f.readlines()
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for line in lines:
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split = line.strip().split(" ")
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key = split[0]
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tokens = split[1:]
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phonemes = []
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for token in tokens:
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phoneme = phoneme_map[f'{token}']
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phonemes.append( phoneme )
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phoneme_transcript[key] = " ".join(phonemes)
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for filename in sorted(os.listdir('./data/librilight-tts')):
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split = filename.split('.')
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key = split[0]
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extension = split[1] # covers double duty of culling .normalized.txt and .phn.txt
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if extension != 'txt':
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continue
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os.rename(f'./data/librilight-tts/{filename}', f'./data/librilight-tts/{key}.normalized.txt')
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if key in phoneme_transcript:
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with open(f'./data/librilight-tts/{key}.phn.txt', 'w', encoding='utf-8') as f:
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f.write(phoneme_transcript[key])
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#!/bin/bash
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# do not invoke directly in scripts
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if [[ ${PWD##*/} == 'scripts' ]]; then
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cd ..
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fi
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# download training data
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cd data
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mkdir librilight-tts
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if [ ! -e ./librispeech_finetuning.tgz ]; then
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wget https://dl.fbaipublicfiles.com/librilight/data/librispeech_finetuning.tgz
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fi
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tar -xzf librispeech_finetuning.tgz
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cd ..
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# clean it up
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python3 ./scripts/prepare_libri.py
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# convert to wav
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pip3 install AudioConverter
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audioconvert convert ./data/librilight-tts/ ./data/librilight-tts --output-format .wav
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# process data
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ulimit -Sn `ulimit -Hn` # ROCm is a bitch
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python3 -m vall_e.emb.g2p ./data/librilight-tts # phonemizes anything that might have been amiss in the phoneme transcription
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python3 -m vall_e.emb.qnt ./data/librilight-tts
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32
scripts/prepare_librilight.py
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32
scripts/prepare_librilight.py
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import os
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import json
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input_dataset = "small+medium"
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output_dataset = "LibriLight-6K"
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for speaker_id in os.listdir(f'./{input_dataset}/'):
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if not os.path.isdir(f'./{input_dataset}/{speaker_id}/'):
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continue
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for book_name in os.listdir(f'./{input_dataset}/{speaker_id}/'):
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subid = 0
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for filename in os.listdir(f'./{input_dataset}/{speaker_id}/{book_name}'):
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if filename[-5:] != ".json":
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continue
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basename = filename[:-5]
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json_path = f'./{input_dataset}/{speaker_id}/{book_name}/{basename}.json'
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flac_path = f'./{input_dataset}/{speaker_id}/{book_name}/{basename}.flac'
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j = json.load(open(json_path, 'r', encoding="utf-8"))
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id = j['book_meta']['id']
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json_id_path = f'./{output_dataset}/{speaker_id}/{speaker_id}_{id}_{subid}.json'
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flac_id_path = f'./{output_dataset}/{speaker_id}/{speaker_id}_{id}_{subid}.flac'
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os.makedirs(f'./{output_dataset}/{speaker_id}/', exist_ok=True)
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os.rename(json_path, json_id_path)
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os.rename(flac_path, flac_id_path)
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subid += 1
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import os
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import json
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for f in os.listdir(f'./LibriTTS/'):
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if not os.path.isdir(f'./LibriTTS/{f}/'):
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input_dataset = "LibriTTS_R"
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output_dataset = "LibriTTS-Train"
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for dataset_name in os.listdir(f'./{input_dataset}/'):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/'):
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continue
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for j in os.listdir(f'./LibriTTS/{f}/'):
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if not os.path.isdir(f'./LibriTTS/{f}/{j}'):
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for speaker_id in os.listdir(f'./{input_dataset}/{dataset_name}/'):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/{speaker_id}'):
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continue
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for z in os.listdir(f'./LibriTTS/{f}/{j}'):
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if not os.path.isdir(f'./LibriTTS/{f}/{j}/{z}'):
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for book_id in os.listdir(f'./{input_dataset}/{dataset_name}/{speaker_id}'):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}'):
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continue
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for i in os.listdir(f'./LibriTTS/{f}/{j}/{z}'):
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if i[-4:] != ".wav":
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for filename in os.listdir(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}'):
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if filename[-4:] != ".wav":
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continue
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os.makedirs(f'./LibriTTS-Train/{j}/', exist_ok=True)
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os.rename(f'./LibriTTS/{f}/{j}/{z}/{i}', f'./LibriTTS-Train/{j}/{i}')
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os.makedirs(f'./{output_dataset}/{speaker_id}/', exist_ok=True)
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os.rename(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}/{filename}', f'./{output_dataset}/{speaker_id}/{filename}')
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43
scripts/process_libritts.py
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43
scripts/process_libritts.py
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import os
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import json
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import torch
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from tqdm.auto import tqdm
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from pathlib import Path
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from vall_e.emb.g2p import encode as valle_phonemize
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from vall_e.emb.qnt import encode_from_file as valle_quantize, _replace_file_extension
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input_dataset = "LibriTTS_R"
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output_dataset = "LibriTTS-Train"
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device = "cuda"
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txts = []
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wavs = []
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for dataset_name in os.listdir(f'./{input_dataset}/'):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/'):
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continue
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for speaker_id in tqdm(os.listdir(f'./{input_dataset}/{dataset_name}/'), desc="Processing speaker"):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/{speaker_id}'):
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continue
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os.makedirs(f'./{output_dataset}/{speaker_id}/', exist_ok=True)
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for book_id in os.listdir(f'./{input_dataset}/{dataset_name}/{speaker_id}'):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}'):
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continue
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for filename in os.listdir(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}'):
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os.rename(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}/{filename}', f'./{output_dataset}/{speaker_id}/{filename}')
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if ".original.txt" in filename:
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txts.append(Path(f'./{output_dataset}/{speaker_id}/{filename}'))
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if ".wav" in filename:
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wavs.append(Path(f'./{output_dataset}/{speaker_id}/{filename}'))
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for path in tqdm(txts, desc="Phonemizing..."):
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phones = valle_phonemize(open(path, "r", encoding="utf-8").read())
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open(_replace_file_extension(path, ".phn.txt"), "w", encoding="utf-8").write(" ".join(phones))
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for path in tqdm(wavs, desc="Quantizing..."):
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qnt = valle_quantize(path, device=device)
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torch.save(qnt.cpu(), _replace_file_extension(path, ".qnt.pt"))
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