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"""Chunked decoder experiment."""
import os
from os.path import join as p_join
from audiocraft.data.audio import audio_write
from datasets import load_dataset
import torch
from multibanddiffusion import MultiBandDiffusion
# configure experiment
cache_dir = "audio"
os.makedirs(cache_dir, exist_ok=True)
num_codes = 3
mbd_model = MultiBandDiffusion.from_pretrained(num_codebooks_decoder=num_codes, num_codebooks_encoder=num_codes)
configs = [
[75, 55], # 1 sec chunk, 0.65 sec stride
[75, 65], # 1 sec chunk, 0.8 sec stride
[150, 120], # 2 sec chunk, 0.65 sec stride
[150, 140], # 2 sec chunk, 0.8 sec stride
]
concat_strategy = ["first", "crossfade", "last"]
def test_hf(hf_dataset: str, sample_size: int = 128, batch_size: int = 32, skip_enhancer: bool = False):
output_dir = p_join(cache_dir, os.path.basename(hf_dataset))
os.makedirs(output_dir, exist_ok=True)
dataset = load_dataset(hf_dataset, split="test")
dataset = dataset.select(range(sample_size))
dataset = dataset.map(
lambda batch: {k: [v] for k, v in batch.items()},
batched=True,
batch_size=batch_size
)
for data in dataset:
sr_list = [d["sampling_rate"] for d in data["audio"]]
assert len(set(sr_list)) == 1, sr_list
sr = sr_list[0]
array = [d["array"] for d in data["audio"]]
max_length = max([len(a) for a in array])
array = [a + [0] * (max_length - len(a)) for a in array]
wav = torch.as_tensor(array, dtype=torch.float32).unsqueeze_(1)
tokens = mbd_model.wav_to_tokens(wav, sr)
for chunk, stride in configs:
for s in concat_strategy:
re_wav, sr = mbd_model.tokens_to_wav(
tokens, chunk_length=chunk, stride=stride, concat_strategy=s, skip_enhancer=skip_enhancer
)
for idx, one_wav in enumerate(re_wav):
if skip_enhancer:
output = p_join(output_dir, f"reconstructed_{num_codes}codes.{chunk}chunks.{stride}strides.{s}", str(idx))
else:
output = p_join(output_dir, f"reconstructed_{num_codes}codes.{chunk}chunks.{stride}strides.{s}.enhancer", str(idx))
audio_write(output, one_wav, sr, strategy="loudness", loudness_compressor=True)
if __name__ == '__main__':
test_hf("japanese-asr/ja_asr.reazonspeech_test", sample_size=64, batch_size=16)
test_hf("japanese-asr/ja_asr.jsut_basic5000", sample_size=64, batch_size=16)
test_hf("japanese-asr/ja_asr.reazonspeech_test", sample_size=64, batch_size=16, skip_enhancer=True)
test_hf("japanese-asr/ja_asr.jsut_basic5000", sample_size=64, batch_size=16, skip_enhancer=True)
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