tokenize_dataset_s2s.py

import os
from os.path import expanduser

import shutil
import torch
from soundfile import LibsndfileError
from datasets import load_dataset, DatasetDict, Audio
from tokenizer_encodec import EncodecTokenizer


direction = os.getenv("DIRECTION", "enA-jaA")
sides = set(direction.split("-"))
dataset_id = os.getenv("DATASET_ID", 0)
num_proc = int(os.getenv("NUM_PROC", 1))
hf_org = os.getenv("HF_ORG", "asahi417")
hf_dataset = f"seamless-align-{direction}"
dataset = load_dataset(f"{hf_org}/{hf_dataset}", f"subset_{dataset_id}", split="train")
tokenizer = EncodecTokenizer.from_pretrained()
max_seq_length = 10000000
audio_loader = Audio()


def error_file(example):
    for side in sides:
        try:
            wav = audio_loader.decode_example(example[f"{side}.audio"])
            if len(wav["array"]) == 0 or len(wav["array"]) > max_seq_length:
                return False
        except LibsndfileError:
            return False
    return True


print(f"Num examples: {len(dataset)}")
for s in sides:
    dataset = dataset.cast_column(f"{s}.audio", Audio(decode=False))
dataset = dataset.filter(error_file, num_proc=num_proc, desc="drop broken audio")
for s in sides:
    dataset = dataset.cast_column(f"{s}.audio", Audio())
print(f"Num examples (after filtering): {len(dataset)}")


def tokenize(example):
    for side in sides:
        wav = torch.as_tensor(example[f"{side}.audio"]["array"].reshape(1, 1, -1), dtype=torch.float32)
        if len(wav) == 0:
            return None
        example[f"{side}.audio.tokens"] = tokenizer.wav_to_tokens(
            wav=wav, sample_rate=example[f"{side}.audio"]["sampling_rate"]
        ).numpy().tolist()[0]
    return example


dataset = dataset.map(
    function=tokenize,
    remove_columns=[f"{s}.audio" for s in sides] + [f"{s}.url" for s in sides] + [f"{s}.duration_start" for s in sides] + [f"{s}.duration_end" for s in sides],
    num_proc=num_proc,
    desc="tokenize dataset"
)
DatasetDict({"train": dataset}).push_to_hub(f"{hf_org}/{hf_dataset}.tokenized", config_name=f"subset_{dataset_id}")
# DatasetDict({"train": dataset}).push_to_hub(f"{hf_org}/{hf_dataset}.tokenized.encodec", config_name=f"subset_{dataset_id}")
cache_dir = f"{expanduser('~')}/.cache/huggingface/datasets/{hf_org}___{hf_dataset}/subset_{dataset_id}"
if os.path.exists(cache_dir):
    shutil.rmtree(cache_dir)