# Copyright      2021  Piotr Żelasko
# Copyright      2022  Xiaomi Corporation     (Author: Mingshuang Luo)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import argparse
import inspect
import logging
from functools import lru_cache
from pathlib import Path
from typing import Any, Dict, Optional

import torch
from datasets import interleave_datasets, load_dataset, Audio, Features, Value, Sequence
from lhotse import (
    CutSet,
    WhisperFbank,
    WhisperFbankConfig,
    load_manifest,
    load_manifest_lazy,
)
from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
    CutConcatenate,
    CutMix,
    DynamicBucketingSampler,
    K2SpeechRecognitionDataset,
    PerturbSpeed,
    PrecomputedFeatures,
    SimpleCutSampler,
    SpecAugment,
)
from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
    AudioSamples,
    OnTheFlyFeatures,
)
from lhotse.utils import fix_random_seed
from torch.utils.data import DataLoader
from utils import get_local_rank, str2bool
import io
import wave
import random

class _SeedWorkers:
    def __init__(self, seed: int):
        self.seed = seed

    def __call__(self, worker_id: int):
        fix_random_seed(self.seed + worker_id)


class AsrDataModule:
    """
    DataModule for k2 ASR experiments.
    It assumes there is always one train and valid dataloader,
    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
    and test-other).

    It contains all the common data pipeline modules used in ASR
    experiments, e.g.:
    - dynamic batch size,
    - bucketing samplers,
    - cut concatenation,
    - augmentation,
    - on-the-fly feature extraction

    This class should be derived for specific corpora used in ASR tasks.
    """

    def __init__(self, args: argparse.Namespace):
        self.args = args

    @classmethod
    def add_arguments(cls, parser: argparse.ArgumentParser):
        group = parser.add_argument_group(
            title="ASR data related options",
            description="These options are used for the preparation of "
            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
            "effective batch sizes, sampling strategies, applied data "
            "augmentations, etc.",
        )
        group.add_argument(
            "--manifest-dir",
            type=Path,
            default=Path("data/fbank"),
            help="Path to directory with train/valid/test cuts.",
        )
        group.add_argument(
            "--max-duration",
            type=int,
            default=300.0,
            help="Maximum pooled recordings duration (seconds) in a "
            "single batch. You can reduce it if it causes CUDA OOM.",
        )
        group.add_argument(
            "--bucketing-sampler",
            type=str2bool,
            default=True,
            help="When enabled, the batches will come from buckets of "
            "similar duration (saves padding frames).",
        )
        group.add_argument(
            "--num-buckets",
            type=int,
            default=30,
            help="The number of buckets for the DynamicBucketingSampler"
            "(you might want to increase it for larger datasets).",
        )
        group.add_argument(
            "--on-the-fly-feats",
            type=str2bool,
            default=False,
            help="When enabled, use on-the-fly cut mixing and feature "
            "extraction. Will drop existing precomputed feature manifests "
            "if available.",
        )
        group.add_argument(
            "--on-the-fly-speed-perturb",
            type=str2bool,
            default=True,
            help="When enabled, use on-the-fly speed perturbation. "
            "Will drop existing precomputed feature manifests "
            "if available.",
        )
        group.add_argument(
            "--shuffle",
            type=str2bool,
            default=True,
            help="When enabled (=default), the examples will be "
            "shuffled for each epoch.",
        )
        group.add_argument(
            "--drop-last",
            type=str2bool,
            default=True,
            help="Whether to drop last batch. Used by sampler.",
        )
        group.add_argument(
            "--return-cuts",
            type=str2bool,
            default=True,
            help="When enabled, each batch will have the "
            "field: batch['supervisions']['cut'] with the cuts that "
            "were used to construct it.",
        )

        group.add_argument(
            "--num-workers",
            type=int,
            default=4,
            help="The number of training dataloader workers that "
            "collect the batches.",
        )

        group.add_argument(
            "--enable-spec-aug",
            type=str2bool,
            default=True,
            help="When enabled, use SpecAugment for training dataset.",
        )

        group.add_argument(
            "--spec-aug-time-warp-factor",
            type=int,
            default=80,
            help="Used only when --enable-spec-aug is True. "
            "It specifies the factor for time warping in SpecAugment. "
            "Larger values mean more warping. "
            "A value less than 1 means to disable time warp.",
        )

        group.add_argument(
            "--enable-musan",
            type=str2bool,
            default=True,
            help="When enabled, select noise from MUSAN and mix it"
            "with training dataset. ",
        )

        group.add_argument(
            "--input-strategy",
            type=str,
            default="PrecomputedFeatures",
            help="AudioSamples or PrecomputedFeatures",
        )

        group.add_argument(
            "--huggingface-dataset-path-or-name",
            type=str,
            default=None,
            help="The path or name of the Huggingface dataset",
        )
        group.add_argument(
            "--audio-key",
            type=str,
            default=None,
            help="The key in the Huggingface dataset containing the audio data",
        )
        group.add_argument(
            "--text-key",
            type=str,
            default=None,
            help="The key in the Huggingface dataset containing the text data",
        )

    def train_dataloaders(
        self,
        cuts_train: CutSet,
        sampler_state_dict: Optional[Dict[str, Any]] = None,
    ) -> DataLoader:
        """
        Args:
          cuts_train:
            CutSet for training.
          sampler_state_dict:
            The state dict for the training sampler.
        """
        transforms = []
        if self.args.enable_musan:
            logging.info("Enable MUSAN")
            logging.info("About to get Musan cuts")
            cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
            transforms.append(
                CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
            )
        else:
            logging.info("Disable MUSAN")
        if self.args.on_the_fly_speed_perturb and self.args.on_the_fly_feats:
            transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2 / 3)] + transforms

        input_transforms = []
        if self.args.enable_spec_aug:
            logging.info("Enable SpecAugment")
            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
            # Set the value of num_frame_masks according to Lhotse's version.
            # In different Lhotse's versions, the default of num_frame_masks is
            # different.
            num_frame_masks = 10
            num_frame_masks_parameter = inspect.signature(
                SpecAugment.__init__
            ).parameters["num_frame_masks"]
            if num_frame_masks_parameter.default == 1:
                num_frame_masks = 2
            logging.info(f"Num frame mask: {num_frame_masks}")
            input_transforms.append(
                SpecAugment(
                    time_warp_factor=self.args.spec_aug_time_warp_factor,
                    num_frame_masks=num_frame_masks,
                    features_mask_size=27,
                    num_feature_masks=2,
                    frames_mask_size=100,
                )
            )
        else:
            logging.info("Disable SpecAugment")

        logging.info("About to create train dataset")
        rank = get_local_rank()

        train = K2SpeechRecognitionDataset(
            input_strategy=OnTheFlyFeatures(
                WhisperFbank(WhisperFbankConfig(num_filters=80, device=f"cuda:{rank}"))
            )
            if self.args.on_the_fly_feats
            else eval(self.args.input_strategy)(),
            cut_transforms=transforms,
            input_transforms=input_transforms,
            return_cuts=self.args.return_cuts,
        )

        if self.args.bucketing_sampler:
            logging.info("Using DynamicBucketingSampler.")
            train_sampler = DynamicBucketingSampler(
                cuts_train,
                max_duration=self.args.max_duration,
                shuffle=self.args.shuffle,
                num_buckets=self.args.num_buckets,
                buffer_size=self.args.num_buckets * 1000,
                drop_last=self.args.drop_last,
            )
        else:
            logging.info("Using SimpleCutSampler.")
            train_sampler = SimpleCutSampler(
                cuts_train,
                max_duration=self.args.max_duration,
                shuffle=self.args.shuffle,
            )
        logging.info("About to create train dataloader")

        if sampler_state_dict is not None:
            logging.info("Loading sampler state dict")
            train_sampler.load_state_dict(sampler_state_dict)

        # 'seed' is derived from the current random state, which will have
        # previously been set in the main process.
        seed = torch.randint(0, 100000, ()).item()
        worker_init_fn = _SeedWorkers(seed)

        train_dl = DataLoader(
            train,
            sampler=train_sampler,
            batch_size=None,
            num_workers=self.args.num_workers,
            persistent_workers=True if self.args.num_workers > 0 else False,
            pin_memory=True,
            worker_init_fn=worker_init_fn,
        )

        return train_dl

    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
        """
        Args:
            cuts_valid:
                CutSet for validation.
        """
        logging.info("About to create dev dataset")
        rank = get_local_rank()
        validate = K2SpeechRecognitionDataset(
            input_strategy=OnTheFlyFeatures(
                WhisperFbank(WhisperFbankConfig(num_filters=80, device=f"cuda:{rank}"))
            )
            if self.args.on_the_fly_feats
            else eval(self.args.input_strategy)(),
            return_cuts=self.args.return_cuts,
        )
        if self.args.bucketing_sampler:
            valid_sampler = DynamicBucketingSampler(
                cuts_valid,
                max_duration=self.args.max_duration,
                shuffle=False,
            )
        else:
            valid_sampler = SimpleCutSampler(
                cuts_valid,
                max_duration=self.args.max_duration,
                shuffle=False,
            )
        logging.info("About to create dev dataloader")
        valid_num_workers = 1
        valid_dl = DataLoader(
            validate,
            sampler=valid_sampler,
            batch_size=None,
            num_workers=valid_num_workers,
            persistent_workers=True if valid_num_workers > 0 else False,
        )

        return valid_dl

    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
        logging.debug("About to create test dataset")
        test = K2SpeechRecognitionDataset(
            input_strategy=OnTheFlyFeatures(
                WhisperFbank(WhisperFbankConfig(num_filters=80, device="cpu"))
            )
            if self.args.on_the_fly_feats
            else eval(self.args.input_strategy)(),
            return_cuts=self.args.return_cuts,
        )
        sampler = DynamicBucketingSampler(
            cuts,
            max_duration=self.args.max_duration,
            shuffle=False,
        )
        logging.debug("About to create test dataloader")
        test_dl = DataLoader(
            test,
            batch_size=None,
            sampler=sampler,
            num_workers=self.args.num_workers,
        )
        return test_dl

    @lru_cache()
    def test_cuts_belle(self) -> CutSet:
        logging.info("About to get test cuts")
        return {
            "test": load_manifest_lazy(
                self.args.manifest_dir / "cuts_belle_test.jsonl.gz"
            )
        }
    @lru_cache()
    def dev_cuts_belle(self) -> CutSet:
        logging.info("About to get test cuts")
        return load_manifest_lazy(
            self.args.manifest_dir / "cuts_belle_test.jsonl.gz"
        )
    @lru_cache()
    def train_cuts_belle(self) -> CutSet:
        logging.info("About to get train cuts")
        slam_omni_zh_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_belle_train.jsonl.gz"
        )
        return slam_omni_zh_cuts

    @lru_cache()
    def train_cuts_en_vocalnet(self) -> CutSet:
        logging.info("About to get train cuts")
        VoiceAssistant_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_voice_assistant_00001-00049.jsonl.gz"
        )
        ultrachat_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_ultrachat_train.jsonl.gz"
        )
        VoiceAssistant_cuts = VoiceAssistant_cuts.resample(16000)
        ultrachat_cuts = ultrachat_cuts.resample(16000)
        return CutSet.mux(
            VoiceAssistant_cuts,
            ultrachat_cuts,
            weights=[
                len(VoiceAssistant_cuts),
                len(ultrachat_cuts),
            ],
        )
    @lru_cache()
    def valid_cuts_en_vocalnet(self) -> CutSet:
        logging.info("About to get valid cuts")
        VoiceAssistant_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_voice_assistant.00000.jsonl.gz"
        )
        VoiceAssistant_cuts = VoiceAssistant_cuts.resample(16000)
        return VoiceAssistant_cuts

    @lru_cache()
    def test_cuts_en_vocalnet(self) -> CutSet:
        logging.info("About to get test cuts")
        VoiceAssistant_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_voice_assistant_small.00000.jsonl.gz"
        )
        VoiceAssistant_cuts = VoiceAssistant_cuts.resample(16000)
        return {"test": VoiceAssistant_cuts}

    @lru_cache()
    def train_cuts_ultravox(self) -> CutSet:
        logging.info("About to get train cuts")
        if self.args.huggingface_dataset_path_or_name is not None:
            librispeech_path = (
                self.args.huggingface_dataset_path_or_name + "/librispeech_asr"
            )
            people_speech_path = (
                self.args.huggingface_dataset_path_or_name + "/peoples_speech"
            )
            gigaspeech_path = self.args.huggingface_dataset_path_or_name + "/gigaspeech"
        else:
            librispeech_path = "fixie-ai/librispeech_asr"
            people_speech_path = "fixie-ai/peoples_speech"
            gigaspeech_path = "fixie-ai/gigaspeech"
        # 148_688
        librispeech_other = load_dataset(
            librispeech_path, "other", split="train.500", streaming=True
        )
        # 104_014
        librispeech_clean_360 = load_dataset(
            librispeech_path, "clean", split="train.360", streaming=True
        )
        # 28_539
        librispeech_clean_100 = load_dataset(
            librispeech_path, "clean", split="train.100", streaming=True
        )

        # 1_501_271
        people_speech_clean = load_dataset(
            people_speech_path, "clean", split="train", streaming=True
        )
        # 548_000
        people_speech_dirty_sa = load_dataset(
            people_speech_path, "dirty_sa", split="train", streaming=True
        )

        # 8_266_422

        gigaspeech = load_dataset(
            gigaspeech_path, "xl-empty-audio-removed", split="train", streaming=True
        )

        librispeech_clean_100_cuts = CutSet.from_huggingface_dataset(
            librispeech_clean_100,
            audio_key="audio",
            text_key="text",
        )

        librispeech_other_cuts = CutSet.from_huggingface_dataset(
            librispeech_other,
            audio_key="audio",
            text_key="text",
        )

        librispeech_clean_360_cuts = CutSet.from_huggingface_dataset(
            librispeech_clean_360,
            audio_key="audio",
            text_key="text",
        )

        gigaspeech_cuts = CutSet.from_huggingface_dataset(
            gigaspeech, audio_key="audio", text_key="text"
        )

        people_speech_clean_cuts = CutSet.from_huggingface_dataset(
            people_speech_clean,
            audio_key="audio",
            text_key="text",
        )

        people_speech_dirty_sa_cuts = CutSet.from_huggingface_dataset(
            people_speech_dirty_sa,
            audio_key="audio",
            text_key="text",
        )

        return CutSet.mux(
            librispeech_clean_100_cuts,
            librispeech_clean_360_cuts,
            librispeech_other_cuts,
            gigaspeech_cuts,
            people_speech_clean_cuts,
            people_speech_dirty_sa_cuts,
            weights=[
                28539,
                104014,
                148688,
                8266422,
                1501271,
                548000,
            ],
        )

    @lru_cache()
    def valid_cuts_ultravox(self) -> CutSet:
        logging.info("About to get valid cuts")
        librispeech_path = "fixie-ai/librispeech_asr"
        librispeech_clean_valid = load_dataset(
            librispeech_path, "clean", split="validation", streaming=True
        )
        librispeech_clean_valid_cuts = CutSet.from_huggingface_dataset(
            librispeech_clean_valid,
            audio_key="audio",
            text_key="text",
        )
        return librispeech_clean_valid_cuts

    @lru_cache()
    def train_cuts_librispeech(self) -> CutSet:
        logging.info("About to get train cuts")
        if self.args.huggingface_dataset_path_or_name is not None:
            librispeech_path = self.args.huggingface_dataset_path_or_name + "/librispeech_asr"
        else:
            librispeech_path = "fixie-ai/librispeech_asr"
        # 148_688
        librispeech_other = load_dataset(
            librispeech_path, "other", split="train.500", streaming=True
        )
        # 104_014
        librispeech_clean_360 = load_dataset(
            librispeech_path, "clean", split="train.360", streaming=True
        )
        # 28_539
        librispeech_clean_100 = load_dataset(
            librispeech_path, "clean", split="train.100", streaming=True
        )

        librispeech_clean_100_cuts = CutSet.from_huggingface_dataset(
            librispeech_clean_100,
            audio_key="audio",
            text_key="text",
        )

        librispeech_other_cuts = CutSet.from_huggingface_dataset(
            librispeech_other,
            audio_key="audio",
            text_key="text",
        )

        librispeech_clean_360_cuts = CutSet.from_huggingface_dataset(
            librispeech_clean_360,
            audio_key="audio",
            text_key="text",
        )

        return CutSet.mux(
            librispeech_clean_100_cuts,
            librispeech_clean_360_cuts,
            librispeech_other_cuts,
            weights=[
                28539,
                104014,
                148688,
            ],
        )

    @lru_cache()
    def train_cuts_gigaspeech(self) -> CutSet:
        logging.info("About to get train cuts")
        gigaspeech_path = "fixie-ai/gigaspeech"
        gigaspeech = load_dataset(
            gigaspeech_path, "xl-empty-audio-removed", split="train", streaming=True
        )

        gigaspeech_cuts = CutSet.from_huggingface_dataset(
            gigaspeech, audio_key="audio", text_key="text"
        )

        return gigaspeech_cuts

    @lru_cache()
    def train_cuts_instruct_s2s(self) -> CutSet:
        logging.info("About to get train cuts")
        if self.args.huggingface_dataset_path_or_name is not None:
            data_path = self.args.huggingface_dataset_path_or_name + "/InstructS2S-200K"
        else:
            data_path = "yuekai/InstructS2S-200K"
        # 148_688
        instruct_s2s_train = load_dataset(
            data_path, split="train", streaming=True
        )

        instruct_s2s_train_cuts = CutSet.from_huggingface_dataset(
            instruct_s2s_train,
            audio_key="question_audio",
            text_key="answer",
        )

        instruct_s2s_train_cuts = instruct_s2s_train_cuts.resample(16000)

        return instruct_s2s_train_cuts

    @lru_cache()
    def train_cuts_en_speech2speech(self) -> CutSet:
        logging.info("About to get train cuts")
        VoiceAssistant_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_voice_assistant_00001-00049.jsonl.gz"
        )
        ultrachat_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_ultrachat_train.jsonl.gz"
        )

        if self.args.huggingface_dataset_path_or_name is not None:
            data_path = self.args.huggingface_dataset_path_or_name + "/InstructS2S-200K"
        else:
            data_path = "yuekai/InstructS2S-200K"
        # 148_688
        instruct_s2s_train = load_dataset(
            data_path, split="train", streaming=True
        )

        instruct_s2s_train_cuts = CutSet.from_huggingface_dataset(
            instruct_s2s_train,
            audio_key="question_audio",
            text_key="answer",
        )

        instruct_s2s_train_cuts = instruct_s2s_train_cuts.resample(16000)


        return CutSet.mux(
            VoiceAssistant_cuts,
            ultrachat_cuts,
            instruct_s2s_train_cuts,
            weights=[
                len(VoiceAssistant_cuts),
                len(ultrachat_cuts),
                423_000,
            ],
        )

    @lru_cache()
    def train_cuts_en_speech2speech_librispeech(self) -> CutSet:
        logging.info("About to get train cuts")
        VoiceAssistant_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_voice_assistant_00001-00049.jsonl.gz"
        )
        ultrachat_cuts = load_manifest_lazy(
            self.args.manifest_dir / "cuts_ultrachat_train.jsonl.gz"
        )

        if self.args.huggingface_dataset_path_or_name is not None:
            data_path = self.args.huggingface_dataset_path_or_name + "/InstructS2S-200K"
        else:
            data_path = "yuekai/InstructS2S-200K"
        # 148_688
        instruct_s2s_train = load_dataset(
            data_path, split="train", streaming=True
        )

        instruct_s2s_train_cuts = CutSet.from_huggingface_dataset(
            instruct_s2s_train,
            audio_key="question_audio",
            text_key="answer",
        )

        instruct_s2s_train_cuts = instruct_s2s_train_cuts.resample(16000)

        if self.args.huggingface_dataset_path_or_name is not None:
            librispeech_path = self.args.huggingface_dataset_path_or_name + "/librispeech_asr"
        else:
            librispeech_path = "fixie-ai/librispeech_asr"
        # 148_688
        librispeech_other = load_dataset(
            librispeech_path, "other", split="train.500", streaming=True
        )
        # 104_014
        librispeech_clean_360 = load_dataset(
            librispeech_path, "clean", split="train.360", streaming=True
        )
        # 28_539
        librispeech_clean_100 = load_dataset(
            librispeech_path, "clean", split="train.100", streaming=True
        )

        librispeech_clean_100_cuts = CutSet.from_huggingface_dataset(
            librispeech_clean_100,
            audio_key="audio",
            text_key="text",
        )

        librispeech_other_cuts = CutSet.from_huggingface_dataset(
            librispeech_other,
            audio_key="audio",
            text_key="text",
        )

        librispeech_clean_360_cuts = CutSet.from_huggingface_dataset(
            librispeech_clean_360,
            audio_key="audio",
            text_key="text",
        )


        return CutSet.mux(
            librispeech_other_cuts,
            VoiceAssistant_cuts,
            ultrachat_cuts,
            librispeech_clean_360_cuts,
            instruct_s2s_train_cuts,
            librispeech_clean_100_cuts,
            weights=[
                148688,
                len(VoiceAssistant_cuts),
                len(ultrachat_cuts),
                104014,
                423_000,
                28539,
            ],
        )

    @lru_cache()
    def train_cuts_emilia_en(self) -> CutSet:
        logging.info("About to get train cuts")
        data_path = "/lustre/fsw/general_sa/yuekaiz/s2s" + "/emilia_en"
        # if self.args.huggingface_dataset_path_or_name is not None:
        #     data_path = self.args.huggingface_dataset_path_or_name + "/emilia_en"
        # else:
        #     data_path = "yuekai/emilia_en"

        emilia_en_data = load_dataset(
            data_path, split="train", streaming=True
        )

        def update_wav_path(example):
            sampling_rate = 16000  # From current_features
            duration = 1  # seconds, arbitrary duration for random audio
            num_channels = 1  # mono
            sample_width = 2  # 2 bytes = 16-bit audio

            num_frames = int(duration * sampling_rate)
            
            # Generate random bytes for the PCM data part
            # This will be random noise, but structurally valid for a WAV file
            pcm_data = bytes([random.randint(0, 255) for _ in range(num_frames * num_channels * sample_width)])

            # Create a WAV file in memory
            audio_buffer = io.BytesIO()
            with wave.open(audio_buffer, 'wb') as wf:
                wf.setnchannels(num_channels)
                wf.setsampwidth(sample_width)
                wf.setframerate(sampling_rate)
                wf.writeframes(pcm_data) # writeframes expects bytes
            
            example["wav"] = audio_buffer.getvalue()
            return example

        emilia_en_data = emilia_en_data.map(update_wav_path)
        current_features = Features({
            'id': Value('string'),
            'text': Value('string'),
            'duration': Value('float'),
            'language': Value('string'),
            'dnsmos': Value('float'),
            'speech_token': Sequence(Value('int32')),
            'wav': Audio(sampling_rate=16000)

        })
        emilia_en_data = emilia_en_data.rename_column("code", "speech_token")
        emilia_en_data = emilia_en_data.cast(current_features)

        emilia_en_train_cuts = CutSet.from_huggingface_dataset(
            emilia_en_data, # Adjusted from instruct_s2s_train
            audio_key="wav",
            text_key="text",
        )
        return emilia_en_train_cuts