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diff --git a/audio/sample_audio/reconstructed_6codes/common_voice_8_0.sample.wav b/audio/sample_audio/reconstructed_6codes/common_voice_8_0.sample.wav
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diff --git a/experiment/chunk_encoder/token_accuracy.ja_asr.jsut_basic5000.3codes.csv b/experiment/chunk_encoder/token_accuracy.ja_asr.jsut_basic5000.3codes.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0bc378718e61ce17b1fde7d6475d183693bd821d
--- /dev/null
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@@ -0,0 +1,5 @@
+,chunk_32000.stride_32000,chunk_32000.stride_28800,chunk_32000.stride_25600,chunk_64000.stride_64000,chunk_64000.stride_60800,chunk_64000.stride_57600
+full,83.78014709614779,87.49814753913637,88.02630003916876,92.6823289048876,94.4784138583423,95.54625432548374
+code_1,94.59811071942353,98.23717637173456,98.15118510694367,97.45612145051129,98.9116143288534,99.3291396121429
+code_2,69.20332527637734,73.39406290905792,73.88416829749825,85.74183126185324,89.04778606513823,91.60529717855339
+code_3,85.94421097992458,88.74661953455418,87.89854121575794,93.58941979116716,94.8251500149043,95.72029024481915
diff --git a/experiment/chunk_encoder/token_accuracy.ja_asr.reazonspeech_test.3codes.csv b/experiment/chunk_encoder/token_accuracy.ja_asr.reazonspeech_test.3codes.csv
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index 0000000000000000000000000000000000000000..1c00a47c8b42c6a46f8ae643d26f1acf067e48ca
--- /dev/null
+++ b/experiment/chunk_encoder/token_accuracy.ja_asr.reazonspeech_test.3codes.csv
@@ -0,0 +1,5 @@
+,chunk_32000.stride_32000,chunk_32000.stride_28800,chunk_32000.stride_25600,chunk_64000.stride_64000,chunk_64000.stride_60800,chunk_64000.stride_57600
+full,78.58263297134313,82.29810882419761,83.50864835373218,89.16632494885282,90.69032251727954,91.74587953539631
+code_1,93.91648496161213,96.69797891602931,97.38407587259886,96.62714070664526,98.14270094883031,98.16118831505192
+code_2,69.3352832743268,72.27684847745196,75.62599108195971,84.53124951396957,84.84464956349304,86.77956266847478
+code_3,82.1598448387522,85.11242350885779,86.17095993691288,89.71275588364725,91.65740827660288,91.0894560822312
diff --git a/ja_asr.jsut_basic5000/test-00000-of-00001.parquet b/ja_asr.jsut_basic5000/test-00000-of-00001.parquet
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diff --git a/ja_asr.reazonspeech_test/test-00000-of-00001.parquet b/ja_asr.reazonspeech_test/test-00000-of-00001.parquet
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diff --git a/multibanddiffusion.py b/multibanddiffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3ac1b44c22705a09350f57c57ad9e0a41157e98
--- /dev/null
+++ b/multibanddiffusion.py
@@ -0,0 +1,270 @@
+"""https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/models/multibanddiffusion.py"""
+import logging
+from typing import List, Optional, Tuple
+from math import ceil
+import torch
+import julius
+
+from tqdm import tqdm
+from audiocraft.modules.diffusion_schedule import NoiseSchedule
+from audiocraft.models.unet import DiffusionUnet
+from audiocraft.models.encodec import CompressionModel
+from audiocraft.models.loaders import load_diffusion_models
+from audiocraft.solvers.compression import CompressionSolver
+from df.enhance import enhance, init_df, load_audio, save_audio  # deepfilternet
+
+
+class DFEnhancer:
+    """Speech enhancer."""
+    def __init__(self):
+        self.model, self.df_state, _ = init_df()
+        self.sample_rate = self.df_state.sr()
+
+    def enhance_audio(self, audio: torch.Tensor, sample_rate: int) -> torch.Tensor:
+        if sample_rate != self.sample_rate:
+            audio = julius.resample_frac(audio, sample_rate, self.sample_rate)
+        enhanced_audio = []
+        for single_audio in audio:
+            enhanced_audio.append(enhance(self.model, self.df_state, single_audio))
+        return torch.stack(enhanced_audio)
+
+
+class DiffusionProcess:
+    """Sampling for a diffusion Model.
+    Args:
+        model (DiffusionUnet): Diffusion U-Net model.
+        noise_schedule (NoiseSchedule): Noise schedule for diffusion process.
+    """
+    def __init__(self, model: DiffusionUnet, noise_schedule: NoiseSchedule) -> None:
+        self.model = model
+        self.schedule = noise_schedule
+
+    def generate(self, condition: torch.Tensor, initial_noise: torch.Tensor, step_size: int = 5) -> torch.Tensor:
+        """Perform one diffusion process to generate one of the bands.
+        Args:
+            condition (torch.Tensor): The embeddings from the compression model.
+            initial_noise (torch.Tensor): The initial noise to start the process.
+            step_size (int): The number of the linearly spaced Markov chain steps.
+        """
+        step_list = list(range(1000))[::-int(1000/step_size)] + [0]
+        return self.schedule.generate_subsampled(
+            model=self.model, initial=initial_noise, step_list=step_list, condition=condition
+        )
+
+
+class BaseMultiBandDiffusion:
+
+    def __init__(self,
+                 diffusion_processes: List[DiffusionProcess],
+                 codec_model: CompressionModel,
+                 sample_per_token: int = 320,
+                 num_codebooks_decoder: int = 3,
+                 num_codebooks_encoder: Optional[int] = None) -> None:
+        """Base class for multi-band diffusion.
+        Args:
+            diffusion_processes (list of DiffusionProcess): Diffusion processes.
+            codec_model (CompressionModel): Underlying compression model used to obtain discrete tokens.
+            sample_per_token (int): Number of sample per token (320 for 24kHz encodec).
+            num_codebooks_decoder (int): Number of codebook to use for decoder.
+            num_codebooks_encoder (int): Number of codebook to use for encoder (default full code).
+        """
+        self.diffusion_processes = diffusion_processes
+        self.codec_model = codec_model
+        self.device = next(self.codec_model.parameters()).device
+        self.sample_per_token = sample_per_token
+        self.num_codebooks_decoder = num_codebooks_decoder
+        self.num_codebooks_encoder = num_codebooks_encoder
+        self.enhancer = DFEnhancer()
+
+    @property
+    def sample_rate(self) -> int:
+        return self.codec_model.sample_rate
+
+    def generate(self, emb: torch.Tensor, size: torch.Size, step_size: int = 5) -> torch.Tensor:
+        """Generate waveform audio from the latent embeddings of the compression model.
+        Args:
+            emb (torch.Tensor): Conditioning embeddings
+            size (None, torch.Size): Size of the output.
+            step_size (int): The number of the linearly spaced Markov chain steps.
+        """
+        assert size[0] == emb.size(0)
+        out = torch.zeros(size).to(self.device)
+        for diffusion_process in self.diffusion_processes:
+            out += diffusion_process.generate(condition=emb, step_size=step_size, initial_noise=torch.randn_like(out))
+        return out
+
+    def re_eq(self, wav: torch.Tensor, ref: torch.Tensor, n_bands: int = 32, strictness: float = 1) -> torch.Tensor:
+        """Match the eq to the encodec output by matching the standard deviation of some frequency bands.
+        Args:
+            wav (torch.Tensor): Audio to equalize.
+            ref (torch.Tensor): Reference audio from which we match the spectrogram.
+            n_bands (int): Number of bands of the eq.
+            strictness (float): How strict the matching. 0 is no matching, 1 is exact matching.
+        """
+        split = julius.SplitBands(n_bands=n_bands, sample_rate=self.codec_model.sample_rate).to(wav.device)
+        bands = split(wav)
+        bands_ref = split(ref)
+        out = torch.zeros_like(ref)
+        for i in range(n_bands):
+            out += bands[i] * (bands_ref[i].std() / bands[i].std()) ** strictness
+        return out
+
+    @torch.no_grad()
+    def wav_to_tokens(self,
+                      wav: torch.Tensor,
+                      sample_rate: int,
+                      cpu_offload: bool = True,
+                      chunk_length: Optional[int] = None,
+                      stride: Optional[int] = None,
+                      concat_strategy: str = "first") -> torch.Tensor:
+        """Get audio tokens from waveform in batch. Note that Encodec generates 75 tokens per second of audio at 24 kHz
+        meaning 320 samples (13.333 msec) per tokens.
+        Args:
+            wav (torch.Tensor): The audio that we want to extract the conditioning from (batch, channel, wav).
+            sample_rate (int): Sample rate of the audio.
+            cpu_offload (bool): Move the output tokens to cpu on the fly to save cuda memory.
+            chunk_length (int): Chunk length to split a long audio (sample size, must be divisible by sample_per_token).
+            stride (int): Stride over chunked audio (sample size, must be divisible by sample_per_token).
+            concat_strategy (str): "first" or "last" to indicate which chunk to use when consolidating the overlap.
+        """
+        # sanity check
+        if wav.ndim != 3:
+            raise ValueError(f"wav should be (batch, channel, time): {wav.ndim} dims")
+        original_device = wav.device
+        # sampling audio
+        if sample_rate != self.sample_rate:
+            wav = julius.resample_frac(wav, sample_rate, self.sample_rate)
+        batch_size, channels, input_length = wav.shape
+        if channels > 1:
+            logging.warning("Audio has more than one channel but encoder takes the first channel only.")
+        # validate chunk length and stride (if None, do one-shot process)
+        if chunk_length:
+            if chunk_length % self.sample_per_token != 0:
+                raise ValueError(f"chunk_length must be divisible by {self.sample_per_token}: {chunk_length}")
+        else:
+            chunk_length = input_length
+        chunk_length_latent = ceil(chunk_length / self.sample_per_token)
+        if stride:
+            if stride % self.sample_per_token != 0:
+                raise ValueError(f"stride must be divisible by {self.sample_per_token}: {stride}")
+        else:
+            stride = chunk_length
+        stride_latent = ceil(stride / self.sample_per_token)
+        # initialize the token tensor
+        num_tokens = ceil(input_length / self.sample_per_token)
+        num_filters = self.codec_model.model.config.num_filters
+        if self.num_codebooks_encoder is not None:
+            if self.num_codebooks_encoder > num_filters:
+                raise ValueError(f"num_codebooks_encoder must be smaller than {num_filters}")
+            num_filters = self.num_codebooks_encoder
+        tokens = torch.zeros(
+            (batch_size, num_filters, num_tokens),
+            device="cpu" if cpu_offload else original_device,
+            dtype=torch.int64
+        )
+        # tokenize by chunk in a sequential manner
+        for offset in tqdm(list(range(0, input_length - chunk_length + stride, stride))):
+            frame = wav[:, :1, offset: offset + chunk_length]
+            tmp_tokens, _ = self.codec_model.encode(frame.to(self.device))
+            offset_latent = int(offset / self.sample_per_token)
+            tmp_tokens = tmp_tokens.to("cpu") if cpu_offload else tmp_tokens.to(original_device)
+            if concat_strategy == "last" or offset == 0:
+                tokens[:, :, offset_latent: offset_latent + chunk_length_latent] = tmp_tokens[:, :num_filters, :]
+            else:
+                overlap_token = chunk_length_latent - stride_latent
+                tokens[:, :, offset_latent + overlap_token: offset_latent + chunk_length_latent] \
+                    = tmp_tokens[:, :num_filters, overlap_token:]
+        return tokens
+
+    @torch.no_grad()
+    def tokens_to_wav(self,
+                      tokens: torch.Tensor,
+                      n_bands: int = 32,
+                      step_size: int = 5,
+                      cpu_offload: bool = True,
+                      chunk_length: Optional[int] = None,
+                      stride: Optional[int] = None,
+                      concat_strategy: str = "crossfade",
+                      skip_enhancer: bool = False) -> Tuple[torch.Tensor, float]:
+        """Generate waveform audio with diffusion from the discrete codes in batch.
+        Args:
+            tokens (torch.Tensor): Discrete codes (batch, num_code, length).
+            n_bands (int): Bands for the eq matching.
+            step_size (int): Number of the linearly spaced Markov chain steps.
+            chunk_length (int): Chunk length to split a long audio.
+            stride (int): Stride over chunked audio.
+            cpu_offload (bool): Move the output tokens to cpu on the fly to save cuda memory.
+            concat_strategy (str): "first" or "last" to indicate which chunk to use when consolidating the overlap.
+            skip_enhancer (bool): Skip applying the enhancer.
+        """
+        batch_size, num_filters, input_length = tokens.shape
+        if num_filters < self.num_codebooks_decoder:
+            raise ValueError(f"num_codebooks_decoder must be smaller than num_filters: {num_filters}")
+        original_device = tokens.device
+        # validate chunk length and stride (if None, do one-shot process)
+        chunk_length = chunk_length if chunk_length else input_length
+        chunk_length_wav = self.sample_per_token * chunk_length
+        stride = stride if stride else chunk_length
+        stride_wav = stride * self.sample_per_token
+        # initialize wav tensor
+        wav = torch.zeros(
+            (batch_size, 1, input_length * self.sample_per_token),
+            device="cpu" if cpu_offload else original_device,
+            dtype=torch.float32
+        )
+        # detokenize by chunk in a sequential manner
+        for offset in tqdm(list(range(0, input_length - chunk_length + stride, stride))):
+            tmp_tokens = tokens[:, :num_filters, offset: offset + chunk_length].to(self.device)
+            wav_encodec = self.codec_model.decode(tmp_tokens)
+            condition = self.codec_model.decode_latent(tmp_tokens)
+            wav_diffusion = self.generate(emb=condition, size=wav_encodec.size(), step_size=step_size)
+            tmp_wav = self.re_eq(wav=wav_diffusion, ref=wav_encodec, n_bands=n_bands)
+            tmp_wav = tmp_wav.to("cpu") if cpu_offload else wav.to(original_device)
+            offset_wav = offset * self.sample_per_token
+            overlap_wav = chunk_length_wav - stride_wav
+            if concat_strategy == "last" or offset == 0:
+                wav[:, :, offset_wav: offset_wav + chunk_length_wav] = tmp_wav
+            elif concat_strategy == "crossfade":
+                fade_out = torch.linspace(1, 0, overlap_wav).unsqueeze(0).to(wav.device)
+                fade_in = torch.linspace(0, 1, overlap_wav).unsqueeze(0).to(wav.device)
+                tmp_wav[:, :, :overlap_wav] = (tmp_wav[:, :, :overlap_wav] * fade_in +
+                                               wav[:, :, offset_wav: offset_wav + overlap_wav] * fade_out)
+                wav[:, :, offset_wav: offset_wav + chunk_length_wav] = tmp_wav
+            else:
+                wav[:, :, offset_wav + overlap_wav: offset_wav + chunk_length_wav] = tmp_wav[:, :, overlap_wav:]
+        if skip_enhancer:
+            return wav, self.sample_rate
+        return self.enhancer.enhance_audio(wav, self.sample_rate), self.enhancer.sample_rate
+
+
+class MultiBandDiffusion:
+
+    @staticmethod
+    def from_pretrained(num_codebooks_decoder: int = 3,
+                        num_codebooks_encoder: Optional[int] = None,
+                        mbd_model_alias: str = "mbd_comp_8.pt",
+                        mbd_model_repo: str = "facebook/multiband-diffusion") -> BaseMultiBandDiffusion:
+        """Get the pretrained Models for MultiBandDiffusion.
+        Args:
+            num_codebooks_decoder (int): Number of codebook to use for decoder.
+            num_codebooks_encoder (int): Number of codebook to use for encoder (default full code).
+            mbd_model_alias (str): Name of the MBD model weight.
+                see here https://huggingface.co/facebook/multiband-diffusion/tree/main
+            mbd_model_repo (str): Name of the MBD model repository.
+        """
+        device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        codec_model = CompressionSolver.model_from_checkpoint(
+            '//pretrained/facebook/encodec_24khz', device=device
+        )
+        codec_model = codec_model.to(device)
+        models, processors, cfgs = load_diffusion_models(mbd_model_repo, filename=mbd_model_alias, device=device)
+        diffusion_processes = []
+        for i in range(len(models)):
+            schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i], device=device)
+            diffusion_processes.append(DiffusionProcess(model=models[i], noise_schedule=schedule))
+        return BaseMultiBandDiffusion(
+            diffusion_processes=diffusion_processes,
+            codec_model=codec_model,
+            num_codebooks_decoder=num_codebooks_decoder,
+            num_codebooks_encoder=num_codebooks_encoder
+        )
diff --git a/push_data.py b/push_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad43bfac8db135790c0b4b131c72fdf4fe4a2d98
--- /dev/null
+++ b/push_data.py
@@ -0,0 +1,20 @@
+import os.path
+from glob import glob
+from os.path import join as p_join
+from datasets import Dataset, Audio, DatasetDict
+
+for root_dir in glob(p_join("audio", "*")):
+    experiment_name = os.path.basename(root_dir)
+    data_dict = {}
+    for audio_dir in sorted(glob(p_join(root_dir, "*"))):
+        data_dict[os.path.basename(audio_dir)] = sorted(glob(p_join(audio_dir, "*")))
+
+    dataset = Dataset.from_dict(data_dict)
+    for key in data_dict:
+        dataset = dataset.cast_column(key, Audio())
+    DatasetDict({"test": dataset}).push_to_hub(
+        "kotoba-speech/streaming-mbd-experiment", config_name=experiment_name
+    )
+    DatasetDict({"test": dataset}).push_to_hub(
+        "kotoba-tech/streaming-mbd-experiment", config_name=experiment_name
+    )
diff --git a/requirement.txt b/requirement.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3611df63338e9c09701fd2db80b70d7d610f3c03
--- /dev/null
+++ b/requirement.txt
@@ -0,0 +1,9 @@
+setuptools
+wheel
+datasets
+julius
+torch
+torchaudio
+av
+audiocraft
+deepfilternet
\ No newline at end of file
diff --git a/sample_audio/test-00000-of-00001.parquet b/sample_audio/test-00000-of-00001.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..53923bae6e38455545bcc33d3dea41e1df68f36e
--- /dev/null
+++ b/sample_audio/test-00000-of-00001.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:91937d4f722d099b98c656b9493942fe1e0efda0a03b6f1976c6f5ac6c38e0fc
+size 1276165
diff --git a/test.py b/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..11c4a978ae19aeed99351c083933a91ebf1d4f5e
--- /dev/null
+++ b/test.py
@@ -0,0 +1,98 @@
+"""Basic audio reconstruction experiment."""
+import os
+from os.path import join as p_join
+import subprocess
+
+from datasets import load_dataset
+import torch
+from audiocraft.data.audio import audio_read, audio_write
+from multibanddiffusion import MultiBandDiffusion
+
+# configure experiment
+cache_dir = "audio"
+os.makedirs(cache_dir, exist_ok=True)
+
+
+def test_files(mbd_model, num_codebooks: int = 8, skip_enhancer: bool = False):
+    """Test with audio files."""
+    # fetch audio
+    output_audio_dir = p_join(cache_dir, "sample_audio", "original")
+    os.makedirs(output_audio_dir, exist_ok=True)
+    sample_audio_urls = {
+        "common_voice_8_0": "https://huggingface.co/datasets/japanese-asr/ja_asr.common_voice_8_0/resolve/main/sample.flac",
+        "jsut_basic5000": "https://huggingface.co/datasets/japanese-asr/ja_asr.jsut_basic5000/resolve/main/sample.flac",
+        "reazonspeech_test": "https://huggingface.co/datasets/japanese-asr/ja_asr.reazonspeech_test/resolve/main/sample.flac"
+    }
+    for file, url in sample_audio_urls.items():
+        subprocess.run(["wget", url, "-O", p_join(output_audio_dir, f"{file}.sample.flac")])
+    # reconstruct audio
+    output_reconstructed_dir = p_join(cache_dir, "sample_audio", f"reconstructed_{num_codebooks}codes")
+    os.makedirs(output_reconstructed_dir, exist_ok=True)
+    for file in sample_audio_urls.keys():
+        # read audio from file
+        single_file = p_join(output_audio_dir, f"{file}.sample.flac")
+        wav, sr = audio_read(single_file)
+        wav = wav.unsqueeze(0)
+        # tokenize audio
+        tokens = mbd_model.wav_to_tokens(wav, sr)
+        # de-tokenize token
+        re_wav, sr = mbd_model.tokens_to_wav(tokens, skip_enhancer=skip_enhancer)
+        # save the reconstructed wav
+        if skip_enhancer:
+            output = p_join(output_reconstructed_dir, f"{file}.sample")
+        else:
+            output = p_join(f"{output_reconstructed_dir}.enhancer", f"{file}.sample")
+        audio_write(output, re_wav[0], sr, strategy="loudness", loudness_compressor=True)
+
+
+def test_hf(mbd_model, hf_dataset: str, num_codebooks: int = 8, sample_size: int = 128, batch_size: int = 32, skip_enhancer: bool = False):
+    """Test with huggingface audio dataset."""
+    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:
+        # get sampling rate (all wav must be the same sampling rate)
+        sr_list = [d["sampling_rate"] for d in data["audio"]]
+        assert len(set(sr_list)) == 1, sr_list
+        sr = sr_list[0]
+        # get wav array (batch, channel, time)
+        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)
+        # save the original wav
+        for idx, one_wav in enumerate(wav):
+            output = p_join(output_dir, "original", str(idx))
+            audio_write(output, one_wav, sr, strategy="loudness", loudness_compressor=True)
+        # tokenize audio
+        tokens = mbd_model.wav_to_tokens(wav, sr)
+        # de-tokenize token
+        re_wav, sr = mbd_model.tokens_to_wav(tokens, skip_enhancer=skip_enhancer)
+        # save the reconstructed wav
+        for idx, one_wav in enumerate(re_wav):
+            if skip_enhancer:
+                output = p_join(output_dir, f"reconstructed_{num_codebooks}codes", str(idx))
+            else:
+                output = p_join(output_dir, f"reconstructed_{num_codebooks}codes.enhancer", str(idx))
+            audio_write(output, one_wav, sr, strategy="loudness", loudness_compressor=True)
+
+
+if __name__ == '__main__':
+    # without enhancer
+    for n_code in [2, 3, 4, 5, 6]:
+        model = MultiBandDiffusion.from_pretrained(num_codebooks_decoder=n_code, num_codebooks_encoder=n_code)
+        test_files(model, n_code, skip_enhancer=True)
+        test_hf(model, "japanese-asr/ja_asr.reazonspeech_test", num_codebooks=n_code, sample_size=64, batch_size=16, skip_enhancer=True)
+        test_hf(model, "japanese-asr/ja_asr.jsut_basic5000", num_codebooks=n_code, sample_size=64, batch_size=16, skip_enhancer=True)
+    # with enhancer
+    n_code = 3
+    model = MultiBandDiffusion.from_pretrained(num_codebooks_decoder=n_code, num_codebooks_encoder=n_code)
+    test_files(model, n_code)
+    test_hf(model, "japanese-asr/ja_asr.reazonspeech_test", num_codebooks=n_code, sample_size=64, batch_size=16)
+    test_hf(model, "japanese-asr/ja_asr.jsut_basic5000", num_codebooks=n_code, sample_size=64, batch_size=16)
diff --git a/test_chunk_decoder.py b/test_chunk_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a94dbf20fe84324c12872e2098912987624ecdf4
--- /dev/null
+++ b/test_chunk_decoder.py
@@ -0,0 +1,60 @@
+"""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)
diff --git a/test_chunk_encoder.py b/test_chunk_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..40207980013a543ac9b588c85c3cf60d5618feff
--- /dev/null
+++ b/test_chunk_encoder.py
@@ -0,0 +1,59 @@
+"""Chunked tokenization experiment."""
+import os
+from os.path import join as p_join
+
+
+from datasets import load_dataset
+import torch
+import pandas as pd
+from multibanddiffusion import MultiBandDiffusion
+
+# configure experiment
+cache_dir = p_join("experiment", "chunk_encoder")
+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 = [
+    [32000, 32000],  # 1.3 sec chunk, 1.3 sec stride
+    [32000, 28800],  # 1.3 sec chunk, 1.15 sec stride (32000 - 320 * 10)
+    [32000, 25600],  # 1.3 sec chunk, 1 sec stride (32000 - 320 * 20)
+    [64000, 64000],  # 2.6 sec chunk, 2.6 sec stride
+    [64000, 60800],  # 2.6 sec chunk, 2.45 sec stride (64000 - 320 * 10)
+    [64000, 57600],  # 2.6 sec chunk, 2.3 sec stride (64000 - 320 * 20)
+]
+
+
+def test_hf(hf_dataset: str, sample_size: int = 128, batch_size: int = 32):
+    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
+    )
+    full_accuracy_table = []
+    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_original = mbd_model.wav_to_tokens(wav, sr)
+        total_vars = tokens_original.shape.numel()
+        accuracy_table = {}
+        for chunk, stride in configs:
+            tokens = mbd_model.wav_to_tokens(wav, sr, chunk_length=chunk, stride=stride)
+            assert tokens_original.shape == tokens.shape, f"{tokens_original.shape} != {tokens.shape}"
+            accuracy = {"full": (tokens_original == tokens).sum().item() / total_vars * 100}
+            accuracy.update({f"code_{c + 1}": (tokens_original[0, c, :] == tokens[0, c, :]).sum().item() / tokens_original.shape[2] * 100 for c in range(num_codes)})
+            accuracy_table[f"chunk_{chunk}.stride_{stride}"] = accuracy
+        full_accuracy_table.append(accuracy_table)
+    df_accuracy = sum(pd.DataFrame(accuracy_table) for accuracy_table in full_accuracy_table)/len(full_accuracy_table)
+    df_accuracy.to_csv(p_join(cache_dir, f"token_accuracy.{os.path.basename(hf_dataset)}.{num_codes}codes.csv"))
+
+
+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)