diff --git a/.github/workflows/cloudFunctionTests.yml b/.github/workflows/cloudFunctionTests.yml
index b394e8b..9dab1d7 100644
--- a/.github/workflows/cloudFunctionTests.yml
+++ b/.github/workflows/cloudFunctionTests.yml
@@ -24,7 +24,7 @@ jobs:
       - name: Setup poetry
         uses: abatilo/actions-poetry@v2
         with:
-          poetry-version: "1.1.13"
+          poetry-version: "1.2.2"
 
       - name: Poetry install
         run: |
diff --git a/functions/functions/datasets/delete_datasets.py b/functions/functions/datasets/delete_datasets.py
index 6efc2f5..bf6a05f 100644
--- a/functions/functions/datasets/delete_datasets.py
+++ b/functions/functions/datasets/delete_datasets.py
@@ -4,7 +4,7 @@
 from functions_framework import Context
 from humps.main import decamelize
 from loguru import logger
-from models import Dataset
+from models.dataset import CloudDataset
 from utils.cloud_storage import delete_folder_blob
 from utils.firestore_event_converter import unpack
 
@@ -27,7 +27,7 @@ def delete_dataset_from_bucket(data: Dict, context: Context) -> None:
     logger.info(f"Raw firestore dataset event: {data}")
 
     dataset = decamelize(unpack(data["oldValue"]))
-    dataset = Dataset.from_dict(dataset)
+    dataset = CloudDataset.from_dict(dataset)
 
     delete_folder_blob(
         bucket_name=DATASET_BUCKET_NAME,
diff --git a/functions/functions/datasets/process_dataset.py b/functions/functions/datasets/process_dataset.py
index a8bcb9a..1a82002 100644
--- a/functions/functions/datasets/process_dataset.py
+++ b/functions/functions/datasets/process_dataset.py
@@ -4,7 +4,7 @@
 from functions_framework import Context
 from humps.main import decamelize
 from loguru import logger
-from models.dataset import Dataset, ProcessingJob
+from models.dataset import CloudDataset, ProcessingJob
 from utils.firestore_event_converter import unpack
 from utils.pubsub import publish_to_topic
 
@@ -23,9 +23,9 @@ def process_dataset(data: Dict, context: Context) -> None:
     """
     # Convert the firestore event into a dataset object.
     dataset = decamelize(unpack(data["value"]))
-    dataset = Dataset.from_dict(dataset)
+    dataset = CloudDataset.from_dict(dataset)
 
     logger.info(f"Firestore newly-created dataset information: {dataset}")
 
-    jobs = map(ProcessingJob.to_dict, dataset.to_batch())
+    jobs = map(ProcessingJob.to_dict, dataset.to_jobs())
     publish_to_topic(topic_name=TOPIC_NAME, data=jobs)
diff --git a/functions/functions/datasets/process_file.py b/functions/functions/datasets/process_file.py
index d436453..1ee3d4f 100644
--- a/functions/functions/datasets/process_file.py
+++ b/functions/functions/datasets/process_file.py
@@ -1,18 +1,14 @@
 import base64
 import json
 import os
-from copy import copy
-from itertools import chain
 from pathlib import Path
 from typing import List, Tuple
 
-import utils.audio as audio
+from elpis.datasets import process_batch
 from firebase_admin import firestore
 from loguru import logger
-from models import Annotation, DatasetOptions, ProcessingJob
-from utils.clean_text import clean_text
+from models import ProcessingJob
 from utils.cloud_storage import download_blob, list_blobs_with_prefix, upload_blob
-from utils.extract_annotations import extract_annotations
 from utils.firebase import get_firestore_client
 
 DEFAULT_DIR = Path("/tmp/")
@@ -36,29 +32,18 @@ def process_dataset_file(event, context) -> None:
     # Decode and deserialize the processing job
     data = base64.b64decode(event["data"]).decode("utf-8")
     data = json.loads(data)
-    logger.info(f"Event data: {data}")
-    job = ProcessingJob.from_dict(data)
+    try:
+        job = ProcessingJob.from_dict(data)
+    except Exception as error:
+        logger.error(f"Failed to deserialize job: {data}")
+        logger.error(error)
+        return
 
     transcription_file, audio_file = download_files(job)
-    annotations = extract_annotations(transcription_file, job.options.elan_options)
-
-    # Resample audio to standardise for training
-    audio.resample(
-        audio_path=audio_file, destination=audio_file, sample_rate=TARGET_SAMPLE_RATE
-    )
+    job.transcription_file = transcription_file
+    job.audio_file = audio_file
 
-    # Clean the annotations
-    annotations = map(
-        lambda annotation: clean_annotation(annotation, job.options), annotations
-    )
-
-    # Generate training files from the annotations
-    processed_files = chain(
-        *map(
-            lambda annotation: generate_training_files(annotation, audio_file),
-            annotations,
-        )
-    )
+    processed_files = process_batch(job, output_dir=DEFAULT_DIR)
 
     # Upload all the training files
     for file in processed_files:
@@ -73,7 +58,7 @@ def download_files(job: ProcessingJob, dir: Path = DEFAULT_DIR) -> Tuple[Path, P
     """Download the required transcription and audio files for the job.
 
     Parameters:
-        job: The processing job.
+        job: The job to process.
         dir: The directory in which to store the files.
 
     Returns:
@@ -81,83 +66,23 @@ def download_files(job: ProcessingJob, dir: Path = DEFAULT_DIR) -> Tuple[Path, P
         and audio files.
     """
     # Download transcription file
-    transcription_file = dir / job.transcription_file_name
+    transcription_file = dir / job.transcription_file
     download_blob(
         bucket_name=FILES_BUCKET,
-        source_blob_name=f"{job.user_id}/{job.transcription_file_name}",
+        source_blob_name=f"{job.user_id}/{job.transcription_file}",
         destination_file_name=transcription_file,
     )
 
     # Download audio file
-    audio_file = dir / job.audio_file_name
+    audio_file = dir / job.audio_file
     download_blob(
         bucket_name=FILES_BUCKET,
-        source_blob_name=f"{job.user_id}/{job.audio_file_name}",
+        source_blob_name=f"{job.user_id}/{job.audio_file}",
         destination_file_name=audio_file,
     )
     return transcription_file, audio_file
 
 
-def clean_annotation(annotation: Annotation, options: DatasetOptions) -> Annotation:
-    """Cleans the text within an annotation.
-
-    Parameters:
-        annotation: The annotation to clean.
-        options: The cleaning options for the dataset.
-
-    Returns:
-        A new annotation whose transcript has been cleaned.
-    """
-    transcript = clean_text(annotation.transcript, options)
-    result = copy(annotation)
-    result.transcript = transcript
-    return result
-
-
-def generate_training_files(
-    annotation: Annotation, audio_file: Path, dir: Path = DEFAULT_DIR
-) -> Tuple[Path, Path]:
-    """Generates a transcript and audio file pairing for this annotation.
-
-    If the annotation is timed (has a start and stop time), we return a path
-    to a new audio file, which is constrained to the given times. Otherwise,
-    the annotation spans the entire audio path, and so we return this path,
-    unmodified.
-
-    Parameters:
-        annotation: The annotation for a given section of audio within the
-            supplied audio_file.
-        audio_file: The file which the annotation references.
-
-    Returns:
-        A tuple containing a transcription and audio file path for the given
-            annotation.
-    """
-    # Get a unique name prefix based on annotation start time
-    name = audio_file.stem
-    if annotation.start_ms is not None:
-        name = f"{name}_{annotation.start_ms}"
-
-    # Save transcription_file
-    transcription_file = dir / f"{name}.json"
-    with open(transcription_file, "w") as f:
-        json.dump(annotation.to_dict(), f)
-
-    # Save audio file.
-    # Type ignoring is because is_timed ensures sample_rate, start_ms and stop_ms exist
-    if annotation.is_timed():
-        cut_audio_file = dir / f"{name}.wav"
-        audio.cut(
-            audio_path=audio_file,
-            destination=cut_audio_file,
-            start_ms=annotation.start_ms,  # type: ignore
-            stop_ms=annotation.stop_ms,  # type: ignore
-        )
-        audio_file = cut_audio_file
-
-    return transcription_file, audio_file
-
-
 def post_processing_hook(job: ProcessingJob) -> None:
     """Determine if all the files in a dataset have been processed, and if so,
     updates the document accordingly in firestore.
diff --git a/functions/models/__init__.py b/functions/models/__init__.py
index 25eace6..d03450a 100644
--- a/functions/models/__init__.py
+++ b/functions/models/__init__.py
@@ -1,14 +1,8 @@
-from models.annotation import Annotation
-from models.dataset import Dataset, DatasetOptions, ElanOptions, ProcessingJob
-from models.elan_tier_selector import TierSelector
+from models.dataset import CloudDataset, ProcessingJob
 from models.model import Model
 
 __all__ = [
-    "Annotation",
-    "Dataset",
-    "DatasetOptions",
-    "ElanOptions",
+    "CloudDataset",
     "ProcessingJob",
-    "TierSelector",
     "Model",
 ]
diff --git a/functions/models/annotation.py b/functions/models/annotation.py
deleted file mode 100644
index 9936ba5..0000000
--- a/functions/models/annotation.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from copy import copy
-from dataclasses import dataclass
-from typing import Any, Dict, Optional
-
-
-@dataclass
-class Annotation:
-    """A class which represents a section of speech for a given audio file and
-    sample rate. If start_ms and end_ms aren't specified, it is assumed that the
-    Annotation spans the entire audio file.
-    """
-
-    audio_file_name: str
-    transcript: str
-    speaker_id: Optional[str] = None  # Currently unused
-    start_ms: Optional[int] = None
-    stop_ms: Optional[int] = None
-
-    def is_timed(self) -> bool:
-        """Returns true iff the annotation exists between a start and stop time for
-        the given recording.
-        """
-        return self.start_ms is not None and self.stop_ms is not None
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Converts an annotation to a serializable dictionary"""
-        return dict(self.__dict__)
-
-    @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "Annotation":
-        """Builds an annotation from a serializable dictionary
-
-        Throws an error if the required keys are not found.
-        """
-        return cls(
-            audio_file_name=data["audio_file_name"],
-            transcript=data["transcript"],
-            start_ms=data.get("start_ms"),
-            stop_ms=data.get("stop_ms"),
-            speaker_id=data.get("speaker_id"),
-        )
diff --git a/functions/models/dataset.py b/functions/models/dataset.py
index 802345b..4dd0ef8 100644
--- a/functions/models/dataset.py
+++ b/functions/models/dataset.py
@@ -1,195 +1,76 @@
-from dataclasses import dataclass, field, fields
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Set
+from __future__ import annotations
 
-from models.elan_tier_selector import TierSelector
+from dataclasses import dataclass, fields
+from typing import Any, Dict, Iterable
 
-TRANSCRIPTION_EXTENSIONS = {".eaf", ".txt"}
+from elpis.datasets import Dataset, ProcessingBatch
 
 
 @dataclass
-class ElanOptions:
-    """A class representing options for how to extract utterance information
-    from an elan file."""
-
-    selection_mechanism: TierSelector
-    selection_value: str
-
-    @classmethod
-    def from_dict(cls, data: Dict[str, str]) -> "ElanOptions":
-        return cls(
-            selection_mechanism=TierSelector(data["selection_mechanism"]),
-            selection_value=data["selection_value"],
-        )
-
-    def to_dict(self) -> Dict[str, str]:
-        result = dict(self.__dict__)
-        result["selection_mechanism"] = self.selection_mechanism.value
-        return result
-
-
-@dataclass
-class DatasetOptions:
-    """A class representing processing options for a dataset."""
-
-    punctuation_to_remove: str = ""
-    punctuation_to_explode: str = ""
-    text_to_remove: List[str] = field(default_factory=list)
-    elan_options: Optional[ElanOptions] = None
-
-    @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "DatasetOptions":
-        kwargs = {
-            field.name: data[field.name]
-            for field in fields(DatasetOptions)
-            if field.name != "elan_options"
-        }
-
-        if "elan_options" in data:
-            elan_options = ElanOptions.from_dict(data["elan_options"])
-        else:
-            elan_options = None
-
-        return cls(elan_options=elan_options, **kwargs)
-
-    def to_dict(self) -> Dict[str, Any]:
-        result = dict(self.__dict__)
-        if self.elan_options is not None:
-            result["elan_options"] = self.elan_options.to_dict()
-        return result
-
-
-@dataclass
-class ProcessingJob:
+class ProcessingJob(ProcessingBatch):
     """A class encapsulating the data needed for an individual processing job"""
 
     user_id: str
-    transcription_file_name: str
-    audio_file_name: str
     dataset_name: str
-    options: DatasetOptions
 
     def to_dict(self) -> Dict[str, Any]:
-        result = dict(self.__dict__)
-        result["options"] = self.options.to_dict()
+        result = super().to_dict()
+        result["user_id"] = self.user_id
+        result["dataset_name"] = self.dataset_name
         return result
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "ProcessingJob":
-        kwargs = {
-            field.name: data[field.name]
-            for field in fields(ProcessingJob)
-            if field.name != "options"
-        }
-        options = DatasetOptions.from_dict(data["options"])
-        return cls(options=options, **kwargs)
+    def from_batch(cls, batch: ProcessingBatch, user_id: str, dataset_name: str):
+        return cls(
+            user_id=user_id,
+            dataset_name=dataset_name,
+            **dict((field.name, getattr(batch, field.name)) for field in fields(batch)),
+        )
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> ProcessingJob:
+        batch = super().from_dict(data)
+        return cls.from_batch(
+            batch, user_id=data["user_id"], dataset_name=data["dataset_name"]
+        )
 
 
 @dataclass
-class Dataset:
+class CloudDataset(Dataset):
     """A class representing an unprocessed dataset within firestore."""
 
-    name: str
     user_id: str
-    files: List[str]
-    options: DatasetOptions
     processed: bool
 
-    def is_empty(self) -> bool:
-        """Returns true iff the dataset contains no files."""
-        return len(self.files) == 0
-
-    def has_elan(self) -> bool:
-        """Returns true iff any of the files in the dataset is an elan file."""
-        return any(map((lambda file_name: file_name.endswith(".eaf")), self.files))
-
-    def is_valid(self) -> bool:
-        """Returns true iff this dataset is valid for processing."""
-        return (
-            not self.is_empty()
-            and len(self.files) % 2 == 0
-            and len(self.mismatched_files()) == 0
-            and len(self.colliding_files()) == 0
-        )
+    def to_jobs(self) -> Iterable[ProcessingJob]:
+        """Create a list of processing jobs for this cloud dataset."""
 
-    @staticmethod
-    def corresponding_audio_file(transcript_file: str) -> str:
-        """Gets the corresponding audio file name for a given transcript file."""
-        return Path(transcript_file).stem + ".wav"
-
-    def mismatched_files(self) -> Set[str]:
-        """Returns the list of transcript file names with no corresponding
-        audio files and vice versa.
-
-        Corresponding in this case means that for every transcript file with
-        name x.some_extension, there is a corresponding file x.wav
-
-        Returns:
-            A list of the mismatched file names.
-        """
-        transcripts_with_audio = set(
-            filter(
-                lambda file: Dataset.corresponding_audio_file(file) in self.files,
-                self._transcript_files(),
+        def batch_to_job(batch: ProcessingBatch) -> ProcessingJob:
+            return ProcessingJob.from_batch(
+                batch, user_id=self.user_id, dataset_name=self.name
             )
-        )
-        matched_files = transcripts_with_audio | set(
-            Dataset.corresponding_audio_file(file) for file in transcripts_with_audio
-        )
-
-        return set(self.files).difference(matched_files)
 
-    def colliding_files(self) -> Set[str]:
-        """Returns the list of transcript file names that collide.
+        return map(batch_to_job, self.to_batches())
 
-        Collide means that two transcript files would be for the same .wav
-        file.
-
-        Returns:
-            A list of the colliding file names.
-        """
-
-        def would_collide(transcript_file: str) -> bool:
-            other_files = self._transcript_files().difference({transcript_file})
-            other_file_names = map(lambda file: Path(file).stem, other_files)
-            return Path(transcript_file).stem in other_file_names
-
-        return set(filter(would_collide, self._transcript_files()))
-
-    def _transcript_files(self) -> Set[str]:
-        """Returns a set of all transcription files within the dataset."""
-        return set(
-            filter(
-                lambda file: Path(file).suffix in TRANSCRIPTION_EXTENSIONS, self.files
-            )
+    @classmethod
+    def from_dataset(cls, dataset: Dataset, user_id: str, processed: bool):
+        return cls(
+            user_id=user_id,
+            processed=processed,
+            **dict(
+                (field.name, getattr(dataset, field.name)) for field in fields(dataset)
+            ),
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "Dataset":
-        kwargs = {
-            field.name: data[field.name]
-            for field in fields(Dataset)
-            if field.name != "options"
-        }
-        options = DatasetOptions.from_dict(data["options"])
-        return cls(options=options, **kwargs)
-
-    def to_batch(self) -> List["ProcessingJob"]:
-        """Converts a valid dataset to a list of processing jobs, matching
-        transcript and audio files.
-        """
-        return [
-            ProcessingJob(
-                dataset_name=self.name,
-                transcription_file_name=transcription_file_name,
-                audio_file_name=self.corresponding_audio_file(transcription_file_name),
-                options=self.options,
-                user_id=self.user_id,
-            )
-            for transcription_file_name in self._transcript_files()
-        ]
+    def from_dict(cls, data: Dict[str, Any]) -> CloudDataset:
+        dataset = Dataset.from_dict(data)
+        return cls.from_dataset(
+            dataset, user_id=data["user_id"], processed=data["processed"]
+        )
 
     def to_dict(self) -> Dict[str, Any]:
-        result = dict(self.__dict__)
-        result["options"] = self.options.to_dict()
+        result = super().to_dict()
+        result["user_id"] = self.user_id
+        result["processed"] = self.processed
         return result
diff --git a/functions/models/elan_tier_selector.py b/functions/models/elan_tier_selector.py
deleted file mode 100644
index 02c06c5..0000000
--- a/functions/models/elan_tier_selector.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from enum import Enum
-
-
-class TierSelector(Enum):
-    """A class representing a method of selecting elan tiers"""
-
-    ORDER = "tier_order"
-    TYPE = "tier_type"
-    NAME = "tier_name"
diff --git a/functions/poetry.lock b/functions/poetry.lock
index 1f0ac1c..ba7360c 100644
--- a/functions/poetry.lock
+++ b/functions/poetry.lock
@@ -1,20 +1,75 @@
+[[package]]
+name = "aiohttp"
+version = "3.8.3"
+description = "Async http client/server framework (asyncio)"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+aiosignal = ">=1.1.2"
+async-timeout = ">=4.0.0a3,<5.0"
+attrs = ">=17.3.0"
+charset-normalizer = ">=2.0,<3.0"
+frozenlist = ">=1.1.1"
+multidict = ">=4.5,<7.0"
+yarl = ">=1.0,<2.0"
+
+[package.extras]
+speedups = ["Brotli", "aiodns", "cchardet"]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+description = "aiosignal: a list of registered asynchronous callbacks"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+frozenlist = ">=1.1.0"
+
+[[package]]
+name = "appdirs"
+version = "1.4.4"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "async-timeout"
+version = "4.0.2"
+description = "Timeout context manager for asyncio programs"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "attrs"
 version = "22.1.0"
 description = "Classes Without Boilerplate"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.5"
 
 [package.extras]
-dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"]
-docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
-tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"]
+dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"]
+docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"]
+tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"]
+tests-no-zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"]
+
+[[package]]
+name = "audioread"
+version = "3.0.0"
+description = "multi-library, cross-platform audio decoding"
+category = "main"
+optional = false
+python-versions = ">=3.6"
 
 [[package]]
 name = "cachecontrol"
-version = "0.12.12"
+version = "0.12.11"
 description = "httplib2 caching for requests"
 category = "main"
 optional = false
@@ -25,7 +80,7 @@ msgpack = ">=0.5.2"
 requests = "*"
 
 [package.extras]
-filecache = ["filelock (>=3.8.0)"]
+filecache = ["lockfile (>=0.9)"]
 redis = ["redis (>=2.10.5)"]
 
 [[package]]
@@ -38,12 +93,23 @@ python-versions = "~=3.7"
 
 [[package]]
 name = "certifi"
-version = "2022.9.14"
+version = "2022.9.24"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "cffi"
+version = "1.15.1"
+description = "Foreign Function Interface for Python calling C code."
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+pycparser = "*"
+
 [[package]]
 name = "charset-normalizer"
 version = "2.1.1"
@@ -53,7 +119,7 @@ optional = false
 python-versions = ">=3.6.0"
 
 [package.extras]
-unicode_backport = ["unicodedata2"]
+unicode-backport = ["unicodedata2"]
 
 [[package]]
 name = "click"
@@ -68,7 +134,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cloudevents"
-version = "1.6.1"
+version = "1.7.1"
 description = ""
 category = "main"
 optional = false
@@ -78,15 +144,61 @@ python-versions = "*"
 deprecation = ">=2.0,<3.0"
 
 [package.extras]
-pydantic = ["pydantic (>=1.0.0)", "pydantic (>=1.0.0)"]
+pydantic = ["pydantic (>=1.0.0,<1.9.0)", "pydantic (>=1.0.0,<2.0)"]
 
 [[package]]
 name = "colorama"
-version = "0.4.5"
+version = "0.4.6"
 description = "Cross-platform colored terminal text."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+
+[[package]]
+name = "datasets"
+version = "2.7.1"
+description = "HuggingFace community-driven open-source library of datasets"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+aiohttp = "*"
+dill = "<0.3.7"
+fsspec = {version = ">=2021.11.1", extras = ["http"]}
+huggingface-hub = ">=0.2.0,<1.0.0"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+pyarrow = ">=6.0.0"
+pyyaml = ">=5.1"
+requests = ">=2.19.0"
+responses = "<0.19"
+tqdm = ">=4.62.1"
+xxhash = "*"
+
+[package.extras]
+apache-beam = ["apache-beam (>=2.26.0)"]
+audio = ["librosa"]
+benchmarks = ["numpy (==1.18.5)", "tensorflow (==2.3.0)", "torch (==1.7.1)", "transformers (==3.0.2)"]
+dev = ["Pillow (>=6.2.1)", "Werkzeug (>=1.0.1)", "absl-py", "aiobotocore (>=2.0.1)", "apache-beam (>=2.26.0)", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "boto3 (>=1.19.8)", "botocore (>=1.22.8)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "flake8 (>=3.8.3)", "fsspec[s3]", "isort (>=5.0.0)", "jiwer", "langdetect", "librosa", "lz4", "mauve-text", "moto[s3,server] (==2.0.4)", "nltk", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "requests-file (>=1.5.1)", "rouge-score", "s3fs (>=2021.11.1)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "soundfile", "spacy (>=3.0.0)", "sqlalchemy", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "torchaudio (<0.12.0)", "transformers", "typer (<0.5.0)", "zstandard"]
+docs = ["s3fs"]
+quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"]
+s3 = ["boto3", "botocore", "fsspec", "s3fs"]
+tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
+tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tests = ["Pillow (>=6.2.1)", "Werkzeug (>=1.0.1)", "absl-py", "aiobotocore (>=2.0.1)", "apache-beam (>=2.26.0)", "bert-score (>=0.3.6)", "boto3 (>=1.19.8)", "botocore (>=1.22.8)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "fsspec[s3]", "jiwer", "langdetect", "librosa", "lz4", "mauve-text", "moto[s3,server] (==2.0.4)", "nltk", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "requests-file (>=1.5.1)", "rouge-score", "s3fs (>=2021.11.1)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "soundfile", "spacy (>=3.0.0)", "sqlalchemy", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "torchaudio (<0.12.0)", "transformers", "typer (<0.5.0)", "zstandard"]
+torch = ["torch"]
+vision = ["Pillow (>=6.2.1)"]
+
+[[package]]
+name = "decorator"
+version = "5.1.1"
+description = "Decorators for Humans"
+category = "main"
+optional = false
+python-versions = ">=3.5"
 
 [[package]]
 name = "deprecation"
@@ -99,9 +211,60 @@ python-versions = "*"
 [package.dependencies]
 packaging = "*"
 
+[[package]]
+name = "dill"
+version = "0.3.6"
+description = "serialize all of python"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+
+[[package]]
+name = "elpis"
+version = "0.1.5"
+description = "A library to perform automatic speech recognition with huggingface transformers."
+category = "main"
+optional = false
+python-versions = ">=3.10,<4.0"
+
+[package.dependencies]
+datasets = ">=2.6.1,<3.0.0"
+librosa = ">=0.9.2,<0.10.0"
+loguru = ">=0.6.0,<0.7.0"
+pedalboard = ">=0.6.2,<0.7.0"
+pympi-ling = ">=1.70.2,<2.0.0"
+torch = ">=1.12.1,<2.0.0"
+transformers = ">=4.23.1,<5.0.0"
+
+[[package]]
+name = "exceptiongroup"
+version = "1.0.4"
+description = "Backport of PEP 654 (exception groups)"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+test = ["pytest (>=6)"]
+
+[[package]]
+name = "filelock"
+version = "3.8.2"
+description = "A platform independent file lock."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo (>=2022.9.29)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.5)"]
+testing = ["covdefaults (>=2.2.2)", "coverage (>=6.5)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-timeout (>=2.1)"]
+
 [[package]]
 name = "firebase-admin"
-version = "5.3.0"
+version = "5.4.0"
 description = "Firebase Admin Python SDK"
 category = "main"
 optional = false
@@ -129,8 +292,51 @@ Jinja2 = ">=3.0"
 Werkzeug = ">=2.0"
 
 [package.extras]
-dotenv = ["python-dotenv"]
 async = ["asgiref (>=3.2)"]
+dotenv = ["python-dotenv"]
+
+[[package]]
+name = "frozenlist"
+version = "1.3.3"
+description = "A list-like structure which implements collections.abc.MutableSequence"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "fsspec"
+version = "2022.11.0"
+description = "File-system specification"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+requests = {version = "*", optional = true, markers = "extra == \"http\""}
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+entrypoints = ["importlib-metadata"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+tqdm = ["tqdm"]
 
 [[package]]
 name = "functions-framework"
@@ -149,28 +355,34 @@ watchdog = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "google-api-core"
-version = "2.10.1"
+version = "2.11.0"
 description = "Google API client core library"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-google-auth = ">=1.25.0,<3.0dev"
+google-auth = ">=2.14.1,<3.0dev"
 googleapis-common-protos = ">=1.56.2,<2.0dev"
-grpcio = {version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""}
-grpcio-status = {version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""}
-protobuf = ">=3.20.1,<5.0.0dev"
+grpcio = [
+    {version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""},
+    {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\""},
+]
+grpcio-status = [
+    {version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""},
+    {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\""},
+]
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 requests = ">=2.18.0,<3.0.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio-status (>=1.33.2,<2.0dev)"]
+grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status (>=1.33.2,<2.0dev)", "grpcio-status (>=1.49.1,<2.0dev)"]
 grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
 grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
 
 [[package]]
 name = "google-api-python-client"
-version = "2.61.0"
+version = "2.68.0"
 description = "Google API Client Library for Python"
 category = "main"
 optional = false
@@ -185,7 +397,7 @@ uritemplate = ">=3.0.1,<5"
 
 [[package]]
 name = "google-auth"
-version = "2.11.0"
+version = "2.15.0"
 description = "Google Authentication Library"
 category = "main"
 optional = false
@@ -198,9 +410,9 @@ rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""}
 six = ">=1.9.0"
 
 [package.extras]
-aiohttp = ["requests (>=2.20.0,<3.0.0dev)", "aiohttp (>=3.6.2,<4.0.0dev)"]
-enterprise_cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
-pyopenssl = ["pyopenssl (>=20.0.0)"]
+aiohttp = ["aiohttp (>=3.6.2,<4.0.0dev)", "requests (>=2.20.0,<3.0.0dev)"]
+enterprise-cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
+pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
 reauth = ["pyu2f (>=0.1.5)"]
 
 [[package]]
@@ -233,7 +445,7 @@ grpc = ["grpcio (>=1.38.0,<2.0dev)"]
 
 [[package]]
 name = "google-cloud-firestore"
-version = "2.6.1"
+version = "2.7.2"
 description = "Google Cloud Firestore API client library"
 category = "main"
 optional = false
@@ -243,11 +455,11 @@ python-versions = ">=3.7"
 google-api-core = {version = ">=1.32.0,<2.0.0 || >=2.8.0,<3.0.0dev", extras = ["grpc"]}
 google-cloud-core = ">=1.4.1,<3.0.0dev"
 proto-plus = ">=1.22.0,<2.0.0dev"
-protobuf = ">=3.19.0,<5.0.0dev"
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-pubsub"
-version = "2.13.6"
+version = "2.13.11"
 description = "Google Cloud Pub/Sub API client library"
 category = "main"
 optional = false
@@ -259,14 +471,14 @@ grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 grpcio = ">=1.38.1,<2.0dev"
 grpcio-status = ">=1.16.0"
 proto-plus = ">=1.22.0,<2.0.0dev"
-protobuf = ">=3.19.0,<5.0.0dev"
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [package.extras]
 libcst = ["libcst (>=0.3.10)"]
 
 [[package]]
 name = "google-cloud-storage"
-version = "2.5.0"
+version = "2.6.0"
 description = "Google Cloud Storage API client library"
 category = "main"
 optional = false
@@ -295,11 +507,11 @@ testing = ["pytest"]
 
 [[package]]
 name = "google-resumable-media"
-version = "2.3.3"
+version = "2.4.0"
 description = "Utilities for Google Media Downloads and Resumable Uploads"
 category = "main"
 optional = false
-python-versions = ">= 3.6"
+python-versions = ">= 3.7"
 
 [package.dependencies]
 google-crc32c = ">=1.0,<2.0dev"
@@ -310,18 +522,18 @@ requests = ["requests (>=2.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.56.4"
+version = "1.57.0"
 description = "Common protobufs used in Google APIs"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-grpcio = {version = ">=1.0.0,<2.0.0dev", optional = true, markers = "extra == \"grpc\""}
-protobuf = ">=3.15.0,<5.0.0dev"
+grpcio = {version = ">=1.44.0,<2.0.0dev", optional = true, markers = "extra == \"grpc\""}
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.0.0,<2.0.0dev)"]
+grpc = ["grpcio (>=1.44.0,<2.0.0dev)"]
 
 [[package]]
 name = "grpc-google-iam-v1"
@@ -337,21 +549,18 @@ grpcio = ">=1.0.0,<2.0.0dev"
 
 [[package]]
 name = "grpcio"
-version = "1.49.0"
+version = "1.51.1"
 description = "HTTP/2-based RPC framework"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
-[package.dependencies]
-six = ">=1.5.2"
-
 [package.extras]
-protobuf = ["grpcio-tools (>=1.49.0)"]
+protobuf = ["grpcio-tools (>=1.51.1)"]
 
 [[package]]
 name = "grpcio-status"
-version = "1.49.0"
+version = "1.51.1"
 description = "Status proto mapping for gRPC"
 category = "main"
 optional = false
@@ -359,8 +568,8 @@ python-versions = ">=3.6"
 
 [package.dependencies]
 googleapis-common-protos = ">=1.5.5"
-grpcio = ">=1.49.0"
-protobuf = ">=4.21.3"
+grpcio = ">=1.51.1"
+protobuf = ">=4.21.6"
 
 [[package]]
 name = "gunicorn"
@@ -370,6 +579,9 @@ category = "main"
 optional = false
 python-versions = ">=3.5"
 
+[package.dependencies]
+setuptools = ">=3.0"
+
 [package.extras]
 eventlet = ["eventlet (>=0.24.1)"]
 gevent = ["gevent (>=1.4.0)"]
@@ -378,7 +590,7 @@ tornado = ["tornado (>=0.2)"]
 
 [[package]]
 name = "httplib2"
-version = "0.20.4"
+version = "0.21.0"
 description = "A comprehensive HTTP client library."
 category = "main"
 optional = false
@@ -387,6 +599,33 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 [package.dependencies]
 pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0.2,<3.0.3 || >3.0.3,<4", markers = "python_version > \"3.0\""}
 
+[[package]]
+name = "huggingface-hub"
+version = "0.11.1"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+filelock = "*"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = "*"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+quality = ["black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "mypy (==0.982)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "isort (>=5.5.4)", "jedi", "pytest", "pytest-cov", "pytest-env", "soundfile"]
+torch = ["torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+
 [[package]]
 name = "idna"
 version = "3.4"
@@ -425,6 +664,48 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "joblib"
+version = "1.2.0"
+description = "Lightweight pipelining with Python functions"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "librosa"
+version = "0.9.2"
+description = "Python module for audio and music processing"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+audioread = ">=2.1.9"
+decorator = ">=4.0.10"
+joblib = ">=0.14"
+numba = ">=0.45.1"
+numpy = ">=1.17.0"
+packaging = ">=20.0"
+pooch = ">=1.0"
+resampy = ">=0.2.2"
+scikit-learn = ">=0.19.1"
+scipy = ">=1.2.0"
+soundfile = ">=0.10.2"
+
+[package.extras]
+display = ["matplotlib (>=3.3.0)"]
+docs = ["ipython (>=7.0)", "matplotlib (>=3.3.0)", "mir-eval (>=0.5)", "numba (<0.50)", "numpydoc", "presets", "sphinx (!=1.3.1)", "sphinx-gallery (>=0.7)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (>=1.0.0,<2.0.0)", "sphinxcontrib-svg2pdfconverter"]
+tests = ["contextlib2", "matplotlib (>=3.3.0)", "pytest", "pytest-cov", "pytest-mpl", "samplerate", "soxr"]
+
+[[package]]
+name = "llvmlite"
+version = "0.39.1"
+description = "lightweight wrapper around basic LLVM functionality"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "loguru"
 version = "0.6.0"
@@ -438,7 +719,7 @@ colorama = {version = ">=0.3.4", markers = "sys_platform == \"win32\""}
 win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""}
 
 [package.extras]
-dev = ["sphinx-rtd-theme (>=0.4.3)", "sphinx-autobuild (>=0.7.1)", "Sphinx (>=4.1.1)", "isort (>=5.1.1)", "black (>=19.10b0)", "pytest-cov (>=2.7.1)", "pytest (>=4.6.2)", "tox (>=3.9.0)", "flake8 (>=3.7.7)", "docutils (==0.16)", "colorama (>=0.3.4)"]
+dev = ["Sphinx (>=4.1.1)", "black (>=19.10b0)", "colorama (>=0.3.4)", "docutils (==0.16)", "flake8 (>=3.7.7)", "isort (>=5.1.1)", "pytest (>=4.6.2)", "pytest-cov (>=2.7.1)", "sphinx-autobuild (>=0.7.1)", "sphinx-rtd-theme (>=0.4.3)", "tox (>=3.9.0)"]
 
 [[package]]
 name = "markupsafe"
@@ -456,6 +737,25 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "multidict"
+version = "6.0.3"
+description = "multidict implementation"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "multiprocess"
+version = "0.70.14"
+description = "better multiprocessing and multithreading in python"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+dill = ">=0.3.6"
+
 [[package]]
 name = "nodeenv"
 version = "1.7.0"
@@ -464,9 +764,25 @@ category = "dev"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
 
+[package.dependencies]
+setuptools = "*"
+
+[[package]]
+name = "numba"
+version = "0.56.4"
+description = "compiling Python code using LLVM"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+llvmlite = ">=0.39.0dev0,<0.40"
+numpy = ">=1.18,<1.24"
+setuptools = "*"
+
 [[package]]
 name = "numpy"
-version = "1.23.3"
+version = "1.23.5"
 description = "NumPy is the fundamental package for array computing with Python."
 category = "main"
 optional = false
@@ -483,9 +799,28 @@ python-versions = ">=3.6"
 [package.dependencies]
 pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
 
+[[package]]
+name = "pandas"
+version = "1.5.2"
+description = "Powerful data structures for data analysis, time series, and statistics"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+numpy = [
+    {version = ">=1.21.0", markers = "python_version >= \"3.10\""},
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+]
+python-dateutil = ">=2.8.1"
+pytz = ">=2020.1"
+
+[package.extras]
+test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
+
 [[package]]
 name = "pedalboard"
-version = "0.6.2"
+version = "0.6.7"
 description = "A Python library for adding effects to audio."
 category = "main"
 optional = false
@@ -496,14 +831,33 @@ numpy = "*"
 
 [[package]]
 name = "pluggy"
-version = "0.13.1"
+version = "1.0.0"
 description = "plugin and hook calling mechanisms for python"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.6"
 
 [package.extras]
 dev = ["pre-commit", "tox"]
+testing = ["pytest", "pytest-benchmark"]
+
+[[package]]
+name = "pooch"
+version = "1.6.0"
+description = "\"Pooch manages your Python library's sample data files: it automatically downloads and stores them in a local directory, with support for versioning and corruption checks.\""
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+appdirs = ">=1.3.0"
+packaging = ">=20.0"
+requests = ">=2.19.0"
+
+[package.extras]
+progress = ["tqdm (>=4.41.0,<5.0.0)"]
+sftp = ["paramiko (>=2.7.0)"]
+xxhash = ["xxhash (>=1.4.3)"]
 
 [[package]]
 name = "proto-plus"
@@ -521,19 +875,22 @@ testing = ["google-api-core[grpc] (>=1.31.5)"]
 
 [[package]]
 name = "protobuf"
-version = "4.21.6"
+version = "4.21.10"
 description = ""
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [[package]]
-name = "py"
-version = "1.11.0"
-description = "library with cross-python path, ini-parsing, io, code, log facilities"
-category = "dev"
+name = "pyarrow"
+version = "10.0.1"
+description = "Python library for Apache Arrow"
+category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.7"
+
+[package.dependencies]
+numpy = ">=1.16.6"
 
 [[package]]
 name = "pyasn1"
@@ -554,9 +911,17 @@ python-versions = "*"
 [package.dependencies]
 pyasn1 = ">=0.4.6,<0.5.0"
 
+[[package]]
+name = "pycparser"
+version = "2.21"
+description = "C parser in Python"
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
 [[package]]
 name = "pyhumps"
-version = "3.7.3"
+version = "3.8.0"
 description = "🐫  Convert strings (and dictionary keys) between snake case, camel case and pascal case in Python. Inspired by Humps for Node"
 category = "main"
 optional = false
@@ -571,7 +936,7 @@ optional = false
 python-versions = "*"
 
 [package.extras]
-test = ["pytest (>=5)", "pytest-mock", "pytest-cov", "coverage (>=4.2)", "lxml"]
+test = ["coverage (>=4.2)", "lxml", "pytest (>=5)", "pytest-cov", "pytest-mock"]
 
 [[package]]
 name = "pyparsing"
@@ -582,11 +947,11 @@ optional = false
 python-versions = ">=3.6.8"
 
 [package.extras]
-diagrams = ["railroad-diagrams", "jinja2"]
+diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pyright"
-version = "1.1.271"
+version = "1.1.282"
 description = "Command line wrapper for pyright"
 category = "dev"
 optional = false
@@ -601,7 +966,7 @@ dev = ["twine (>=3.4.1)"]
 
 [[package]]
 name = "pytest"
-version = "7.1.3"
+version = "7.2.0"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
@@ -610,18 +975,18 @@ python-versions = ">=3.7"
 [package.dependencies]
 attrs = ">=19.2.0"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
+exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
 pluggy = ">=0.12,<2.0"
-py = ">=1.8.2"
-tomli = ">=1.0.0"
+tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
 testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
 
 [[package]]
 name = "pytest-mock"
-version = "3.9.0"
+version = "3.10.0"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 category = "dev"
 optional = false
@@ -631,7 +996,42 @@ python-versions = ">=3.7"
 pytest = ">=5.0"
 
 [package.extras]
-dev = ["pre-commit", "tox", "pytest-asyncio"]
+dev = ["pre-commit", "pytest-asyncio", "tox"]
+
+[[package]]
+name = "python-dateutil"
+version = "2.8.2"
+description = "Extensions to the standard Python datetime module"
+category = "main"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "pytz"
+version = "2022.6"
+description = "World timezone definitions, modern and historical"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "pyyaml"
+version = "6.0"
+description = "YAML parser and emitter for Python"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "regex"
+version = "2022.10.31"
+description = "Alternative regular expression module, to replace re."
+category = "main"
+optional = false
+python-versions = ">=3.6"
 
 [[package]]
 name = "requests"
@@ -649,7 +1049,39 @@ urllib3 = ">=1.21.1,<1.27"
 
 [package.extras]
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
-use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+
+[[package]]
+name = "resampy"
+version = "0.4.2"
+description = "Efficient signal resampling"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+numba = ">=0.53"
+numpy = ">=1.17"
+
+[package.extras]
+design = ["optuna (>=2.10.0)"]
+docs = ["numpydoc", "sphinx (!=1.3.1)"]
+tests = ["pytest (<8)", "pytest-cov", "scipy (>=1.0)"]
+
+[[package]]
+name = "responses"
+version = "0.18.0"
+description = "A utility library for mocking out the `requests` Python library."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+requests = ">=2.0,<3.0"
+urllib3 = ">=1.25.10"
+
+[package.extras]
+tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=4.6)", "pytest-cov", "pytest-localserver", "types-mock", "types-requests"]
 
 [[package]]
 name = "rsa"
@@ -662,6 +1094,54 @@ python-versions = ">=3.6,<4"
 [package.dependencies]
 pyasn1 = ">=0.1.3"
 
+[[package]]
+name = "scikit-learn"
+version = "1.1.3"
+description = "A set of python modules for machine learning and data mining"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+joblib = ">=1.0.0"
+numpy = ">=1.17.3"
+scipy = ">=1.3.2"
+threadpoolctl = ">=2.0.0"
+
+[package.extras]
+benchmark = ["matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
+examples = ["matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"]
+tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.2)", "mypy (>=0.961)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pyamg (>=4.0.0)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "scikit-image (>=0.16.2)"]
+
+[[package]]
+name = "scipy"
+version = "1.9.3"
+description = "Fundamental algorithms for scientific computing in Python"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+numpy = ">=1.18.5,<1.26.0"
+
+[package.extras]
+dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
+doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
+test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "setuptools"
+version = "60.3.1"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=8.2)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx", "sphinx-inline-tabs", "sphinxcontrib-towncrier"]
+testing = ["flake8-2020", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "paver", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.0.1)", "pytest-flake8", "pytest-mypy", "pytest-virtualenv (>=1.2.7)", "pytest-xdist", "sphinx (>=4.3.2)", "virtualenv (>=13.0.0)", "wheel"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -670,6 +1150,41 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 
+[[package]]
+name = "soundfile"
+version = "0.11.0"
+description = "An audio library based on libsndfile, CFFI and NumPy"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+cffi = ">=1.0"
+
+[package.extras]
+numpy = ["numpy"]
+
+[[package]]
+name = "threadpoolctl"
+version = "3.1.0"
+description = "threadpoolctl"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "tokenizers"
+version = "0.13.2"
+description = "Fast and Customizable Tokenizers"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.extras]
+dev = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -678,6 +1193,104 @@ category = "dev"
 optional = false
 python-versions = ">=3.7"
 
+[[package]]
+name = "torch"
+version = "1.12.1"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+typing-extensions = "*"
+
+[[package]]
+name = "tqdm"
+version = "4.64.1"
+description = "Fast, Extensible Progress Meter"
+category = "main"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["py-make (>=0.1.0)", "twine", "wheel"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
+[[package]]
+name = "transformers"
+version = "4.25.1"
+description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.10.0,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+tokenizers = ">=0.11.1,<0.11.3 || >0.11.3,<0.14"
+tqdm = ">=4.27"
+
+[package.extras]
+accelerate = ["accelerate (>=0.10.0)"]
+all = ["Pillow", "accelerate (>=0.10.0)", "codecarbon (==1.2.0)", "flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio"]
+audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+codecarbon = ["codecarbon (==1.2.0)"]
+deepspeed = ["accelerate (>=0.10.0)", "deepspeed (>=0.6.5)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.10.0)", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.6.5)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow", "accelerate (>=0.10.0)", "beautifulsoup4", "black (==22.3)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "flax (>=0.4.1)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pyknp (>=0.6.1)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (==22.3)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pyknp (>=0.6.1)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+docs = ["Pillow", "accelerate (>=0.10.0)", "codecarbon (==1.2.0)", "flax (>=0.4.1)", "hf-doc-builder", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio"]
+docs-specific = ["hf-doc-builder"]
+fairscale = ["fairscale (>0.3)"]
+flax = ["flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "optax (>=0.0.8)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune]", "sigopt"]
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+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.4)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "black (==22.3)", "datasets (!=2.5.0)", "flake8 (>=3.8.3)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)"]
+ray = ["ray[tune]"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf (<=3.20.2)", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.11)", "tensorflow-text", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+torch = ["torch (>=1.7,!=1.12.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torchhub = ["filelock", "huggingface-hub (>=0.10.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.2)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "tqdm (>=4.27)"]
+vision = ["Pillow"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.4.0"
+description = "Backported and Experimental Type Hints for Python 3.7+"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "uritemplate"
 version = "4.1.1"
@@ -688,15 +1301,15 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "urllib3"
-version = "1.26.12"
+version = "1.26.13"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, <4"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 
 [package.extras]
-brotli = ["brotlicffi (>=0.8.0)", "brotli (>=1.0.9)", "brotlipy (>=0.6.0)"]
-secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "urllib3-secure-extra", "ipaddress"]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
+secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
 socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
@@ -708,7 +1321,7 @@ optional = false
 python-versions = ">=3.6"
 
 [package.extras]
-watchmedo = ["argh (>=0.24.1)", "PyYAML (>=3.10)"]
+watchmedo = ["PyYAML (>=3.10)", "argh (>=0.24.1)"]
 
 [[package]]
 name = "werkzeug"
@@ -735,69 +1348,1498 @@ python-versions = ">=3.5"
 [package.extras]
 dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
 
+[[package]]
+name = "xxhash"
+version = "3.1.0"
+description = "Python binding for xxHash"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "yarl"
+version = "1.8.2"
+description = "Yet another URL library"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.10"
-content-hash = "2cdeba3b22b57b492726842a971db65116e86019f09a1b734e85b7a179ef0e4b"
+content-hash = "ac66dbdb2f647da1ebba747268b3c081fc03a9a022fc3bba66026417356bbbad"
 
 [metadata.files]
-attrs = []
-cachecontrol = []
-cachetools = []
-certifi = []
-charset-normalizer = []
-click = []
-cloudevents = []
-colorama = []
-deprecation = []
-firebase-admin = []
-flask = []
-functions-framework = []
-google-api-core = []
-google-api-python-client = []
-google-auth = []
-google-auth-httplib2 = []
-google-cloud-core = []
-google-cloud-firestore = []
-google-cloud-pubsub = []
-google-cloud-storage = []
-google-crc32c = []
-google-resumable-media = []
-googleapis-common-protos = []
-grpc-google-iam-v1 = []
-grpcio = []
-grpcio-status = []
-gunicorn = []
-httplib2 = []
-idna = []
-iniconfig = []
-itsdangerous = []
-jinja2 = []
-loguru = []
-markupsafe = []
-msgpack = []
-nodeenv = []
-numpy = []
-packaging = []
-pedalboard = []
-pluggy = []
-proto-plus = []
-protobuf = []
-py = []
-pyasn1 = []
-pyasn1-modules = []
-pyhumps = []
-pympi-ling = []
-pyparsing = []
-pyright = []
-pytest = []
-pytest-mock = []
-requests = []
-rsa = []
-six = []
-tomli = []
-uritemplate = []
-urllib3 = []
-watchdog = []
-werkzeug = []
-win32-setctime = []
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diff --git a/functions/pyproject.toml b/functions/pyproject.toml
index 15c4d7a..175ed35 100644
--- a/functions/pyproject.toml
+++ b/functions/pyproject.toml
@@ -6,6 +6,7 @@ authors = ["Harry Keightley <harrykeightley@outlook.com>"]
 
 [tool.poetry.dependencies]
 python = "^3.10"
+torch = "~1.12.1"
 google-cloud-storage = "^2.5.0"
 google-cloud-pubsub = "^2.13.6"
 google-auth = "^2.11.0"
@@ -18,6 +19,8 @@ loguru = "^0.6.0"
 cloudevents = "^1.6.1"
 pyhumps = "^3.7.3"
 pedalboard = "^0.6.2"
+elpis = "^0.1.5"
+setuptools = "60.3.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^7.1.3"
diff --git a/functions/tests/__init__.py b/functions/tests/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/functions/tests/functions/test_process_file.py b/functions/tests/functions/test_process_file.py
index 4b5677c..b7b932a 100644
--- a/functions/tests/functions/test_process_file.py
+++ b/functions/tests/functions/test_process_file.py
@@ -1,36 +1,24 @@
-import json
 from pathlib import Path
 from unittest.mock import Mock
 
-from models import Annotation, DatasetOptions, ProcessingJob
+from elpis.datasets import CleaningOptions
+from elpis.models import ElanOptions, ElanTierSelector
+from models import ProcessingJob
 
-from functions.datasets.process_file import (
-    clean_annotation,
-    download_files,
-    generate_training_files,
-    has_finished_processing,
-)
+from functions.datasets.process_file import download_files, has_finished_processing
 
-TEST_ANNOTATION = Annotation(audio_file_name="test.wav", transcript="hi")
-TEST_ANNOTATION_TIMED = Annotation(
-    audio_file_name="test.wav",
-    transcript="hi",
-    start_ms=0,
-    stop_ms=1000,
-)
+ABUI_DATASET_FILES = ["abui_1.eaf", "abui_1.wav", "abui_2.eaf", "abui_2.wav"]
 
 TEST_JOB = ProcessingJob(
     user_id="1",
-    transcription_file_name="test.eaf",
-    audio_file_name="test.wav",
-    dataset_name="dataset",
-    options=DatasetOptions(),
+    dataset_name="Dataset",
+    transcription_file=Path(ABUI_DATASET_FILES[0]),
+    audio_file=Path(ABUI_DATASET_FILES[1]),
+    cleaning_options=CleaningOptions(),
+    elan_options=ElanOptions(ElanTierSelector.ORDER, "1"),
 )
 
 
-ABUI_DATASET_FILES = ["abui_1.eaf", "abui_1.wav", "abui_2.eaf", "abui_2.wav"]
-
-
 def test_download_files(mocker):
     downloader_mock: Mock = mocker.patch(
         "functions.datasets.process_file.download_blob"
@@ -39,48 +27,8 @@ def test_download_files(mocker):
     transcript_file, audio_file = download_files(job=TEST_JOB, dir=dir)
 
     assert downloader_mock.call_count == 2
-    assert transcript_file == dir / TEST_JOB.transcription_file_name
-    assert audio_file == dir / TEST_JOB.audio_file_name
-
-
-def test_clean_annotation(mocker):
-    cleaner_mock: Mock = mocker.patch("functions.datasets.process_file.clean_text")
-    cleaner_mock.return_value = "wow"
-
-    result = clean_annotation(TEST_ANNOTATION, DatasetOptions())
-    cleaner_mock.assert_called_once()
-    assert result.transcript == "wow"
-    assert TEST_ANNOTATION.transcript == "hi"
-
-
-def test_generate_training_files_with_untimed_annotation(tmp_path: Path, mocker):
-    cut_mock: Mock = mocker.patch("functions.datasets.process_file.audio.cut")
-    audio_file = tmp_path / "test.wav"
-
-    transcription, audio = generate_training_files(
-        TEST_ANNOTATION, audio_file, tmp_path
-    )
-    cut_mock.assert_not_called()
-    assert transcription == tmp_path / "test.json"
-    assert audio == audio_file
-
-    with open(transcription) as f:
-        assert Annotation.from_dict(json.load(f)) == TEST_ANNOTATION
-
-
-def test_generate_training_files_with_timed_annotation(tmp_path: Path, mocker):
-    cut_mock: Mock = mocker.patch("functions.datasets.process_file.audio.cut")
-    audio_file = tmp_path / "test.wav"
-
-    transcription, audio = generate_training_files(
-        TEST_ANNOTATION_TIMED, audio_file, tmp_path
-    )
-    cut_mock.assert_called_once()
-    assert transcription == tmp_path / f"test_{TEST_ANNOTATION_TIMED.start_ms}.json"
-    assert audio == tmp_path / f"test_{TEST_ANNOTATION_TIMED.start_ms}.wav"
-
-    with open(transcription) as f:
-        assert Annotation.from_dict(json.load(f)) == TEST_ANNOTATION_TIMED
+    assert transcript_file == dir / TEST_JOB.transcription_file
+    assert audio_file == dir / TEST_JOB.audio_file
 
 
 def test_has_finished_processing():
diff --git a/functions/tests/models/test_annotations.py b/functions/tests/models/test_annotations.py
deleted file mode 100644
index f3aa1e1..0000000
--- a/functions/tests/models/test_annotations.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from models import Annotation
-from pytest import raises
-
-START = 0
-STOP = 1000
-AUDIO_FILE_NAME = "audio"
-TRANSCRIPT = "hi"
-SPEAKER_ID = "goofy"
-
-INVALID_ANNOTATION_DATA = {"audio_file_name": AUDIO_FILE_NAME}
-UNTIMED_ANNOTATION_DATA = {
-    "audio_file_name": AUDIO_FILE_NAME,
-    "transcript": TRANSCRIPT,
-    "speaker_id": SPEAKER_ID,
-}
-TIMING_DATA = {"start_ms": START, "stop_ms": STOP}
-TIMED_ANNOTATION_DATA = UNTIMED_ANNOTATION_DATA | TIMING_DATA
-
-
-def test_annotation_from_valid_dict():
-    annotation = Annotation.from_dict(UNTIMED_ANNOTATION_DATA)
-    assert annotation.audio_file_name == AUDIO_FILE_NAME
-    assert annotation.transcript == TRANSCRIPT
-
-    annotation = Annotation.from_dict(TIMED_ANNOTATION_DATA)
-    assert annotation.start_ms == START
-    assert annotation.stop_ms == STOP
-
-
-def test_annotation_from_invalid_dict():
-    with raises(Exception):
-        Annotation.from_dict(INVALID_ANNOTATION_DATA)
-
-
-def test_to_dict_round_trip():
-    timed_annotation = Annotation.from_dict(TIMED_ANNOTATION_DATA)
-    assert timed_annotation.to_dict() == TIMED_ANNOTATION_DATA
-
-
-def test_is_timed():
-    timed_annotation = Annotation.from_dict(TIMED_ANNOTATION_DATA)
-    assert timed_annotation.is_timed()
-
-    untimed_annotation = Annotation.from_dict(UNTIMED_ANNOTATION_DATA)
-    assert not untimed_annotation.is_timed()
diff --git a/functions/tests/models/test_dataset.py b/functions/tests/models/test_dataset.py
index b2c9662..fab9979 100644
--- a/functions/tests/models/test_dataset.py
+++ b/functions/tests/models/test_dataset.py
@@ -1,179 +1,38 @@
-from models import Dataset, DatasetOptions, ElanOptions, ProcessingJob, TierSelector
-from pytest import raises
-
-# ====== Elan Options ======
-
-VALID_ELAN_OPTIONS_DICT = {
-    "selection_mechanism": "tier_name",
-    "selection_value": "test",
-}
-INVALID_ELAN_OPTIONS_DICT = {
-    "selection_mechanism": "pier_name",
-    "selection_value": "jest",
-}
-
-
-def test_build_elan_options():
-    options = ElanOptions.from_dict(VALID_ELAN_OPTIONS_DICT)
-    assert options.selection_mechanism == TierSelector.NAME
-    assert options.selection_value == "test"
-
-
-def test_build_invalid_elan_options_raises_error():
-    with raises(ValueError):
-        ElanOptions.from_dict(INVALID_ELAN_OPTIONS_DICT)
-
-
-def test_serialize_elan_options():
-    options = ElanOptions(
-        selection_mechanism=TierSelector.NAME, selection_value="hello"
-    )
-    result = options.to_dict()
-    assert result["selection_mechanism"] == "tier_name"
-    assert result["selection_value"] == "hello"
-
-
-# ====== Dataset Options ======
-VALID_DATASET_OPTIONS_WITHOUT_ELAN_DICT = {
-    "punctuation_to_remove": ":",
-    "punctuation_to_explode": ";",
-    "text_to_remove": ["<UNK>"],
-}
-
-VALID_DATASET_OPTIONS_DICT = VALID_DATASET_OPTIONS_WITHOUT_ELAN_DICT | {
-    "elan_options": VALID_ELAN_OPTIONS_DICT
-}
-
-
-def test_default_dataset_options():
-    options = DatasetOptions()
-    assert options.punctuation_to_remove == ""
-    assert options.punctuation_to_explode == ""
-    assert options.text_to_remove == []
-    assert options.elan_options is None
-
-
-def test_build_dataset_options_with_elan():
-    options = DatasetOptions.from_dict(VALID_DATASET_OPTIONS_DICT)
-    assert options.punctuation_to_remove == ":"
-    assert options.punctuation_to_explode == ";"
-    assert options.text_to_remove == ["<UNK>"]
-    assert options.elan_options is not None
-    assert options.elan_options == ElanOptions.from_dict(VALID_ELAN_OPTIONS_DICT)
-
-
-def test_build_dataset_options_without_elan():
-    options = DatasetOptions.from_dict(VALID_DATASET_OPTIONS_WITHOUT_ELAN_DICT)
-    assert options.punctuation_to_remove == ":"
-    assert options.punctuation_to_explode == ";"
-    assert options.text_to_remove == ["<UNK>"]
-    assert options.elan_options is None
-
-
-def test_serialize_dataset_options():
-    options = DatasetOptions(
-        punctuation_to_remove=":",
-        punctuation_to_explode=";",
-        text_to_remove=["<UNK>"],
-        elan_options=ElanOptions.from_dict(VALID_ELAN_OPTIONS_DICT),
-    )
-    assert options.to_dict() == VALID_DATASET_OPTIONS_DICT
+from pathlib import Path
 
+from elpis.datasets import CleaningOptions, Dataset
+from elpis.datasets.dataset import ElanOptions
+from elpis.models import ElanTierSelector
+from models.dataset import CloudDataset, ProcessingJob
 
 # ====== Dataset ======
-FILES_WITH_ELAN = ["1.eaf", "1.wav"]
-FILES_WITHOUT_ELAN = ["1.txt", "1.wav"]
-MISMATCHED_FILES = ["1.eaf", "1.wav", "2.wav", "3.txt"]
-COLLIDING_FILES = ["1.eaf", "1.wav", "1.txt"]
+FILES_WITH_ELAN = [Path(name) for name in ("1.eaf", "1.wav")]
 
+DATASET = Dataset(
+    name="dataset",
+    files=FILES_WITH_ELAN,
+    cleaning_options=CleaningOptions(),
+    elan_options=ElanOptions(ElanTierSelector.NAME, "Phrase"),
+)
 
-VALID_DATASET_DICT = {
-    "name": "dataset",
-    "user_id": "1",
-    "files": FILES_WITH_ELAN,
-    "options": VALID_DATASET_OPTIONS_DICT,
-    "processed": False,
-}
+CLOUD_DATASET_DICT = {"processed": False, "user_id": "1", **DATASET.to_dict()}
 
 
-def test_build_dataset():
-    dataset = Dataset.from_dict(VALID_DATASET_DICT)
-    assert dataset.name == "dataset"
+def test_build_cloud_dataset():
+    dataset = CloudDataset.from_dict(CLOUD_DATASET_DICT)
     assert dataset.user_id == "1"
-    assert dataset.files == FILES_WITH_ELAN
-    assert dataset.options == DatasetOptions.from_dict(VALID_DATASET_OPTIONS_DICT)
     assert dataset.processed == False
 
 
-def test_serialize_dataset():
-    dataset = Dataset.from_dict(VALID_DATASET_DICT)
-    assert dataset.to_dict() == VALID_DATASET_DICT
-
-
-def test_dataset_is_empty():
-    dataset = Dataset.from_dict(VALID_DATASET_DICT)
-    assert not dataset.is_empty()
-
-    dataset.files = []
-    assert dataset.is_empty()
-
-
-def test_dataset_has_elan():
-    dataset = Dataset.from_dict(VALID_DATASET_DICT)
-    assert dataset.has_elan()
-
-    dataset.files = FILES_WITHOUT_ELAN
-    assert not dataset.has_elan()
-
-
-def test_dataset_mismatched_files():
-    dataset = Dataset.from_dict(VALID_DATASET_DICT)
-    assert len(dataset.mismatched_files()) == 0
-
-    dataset.files = MISMATCHED_FILES
-    assert set(dataset.mismatched_files()) == {"2.wav", "3.txt"}
-
-
-def test_duplicate_files():
-    dataset = Dataset.from_dict(VALID_DATASET_DICT)
-    assert len(dataset.colliding_files()) == 0
-
-    dataset.files = COLLIDING_FILES
-    assert set(dataset.colliding_files()) == {"1.eaf", "1.txt"}
-
-
-def test_dataset_batching():
-    dataset = Dataset.from_dict(VALID_DATASET_DICT)
-    batch = dataset.to_batch()
-    assert len(batch) == 1
-    job = batch[0]
-    assert job.user_id == dataset.user_id
-    assert job.dataset_name == dataset.name
-    transcript_file_name, audio_file_name = FILES_WITH_ELAN
-    assert job.transcription_file_name == transcript_file_name
-    assert job.audio_file_name == audio_file_name
-    assert job.options == dataset.options
-
-
-# ====== Processing Job ======
-VALID_JOB_DICT = {
-    "user_id": VALID_DATASET_DICT["user_id"],
-    "dataset_name": VALID_DATASET_DICT["name"],
-    "transcription_file_name": FILES_WITH_ELAN[0],
-    "audio_file_name": FILES_WITH_ELAN[1],
-    "options": VALID_DATASET_OPTIONS_DICT,
-}
+def test_serialize_cloud_dataset():
+    dataset = CloudDataset.from_dict(CLOUD_DATASET_DICT)
+    assert dataset.to_dict() == CLOUD_DATASET_DICT
 
 
-def test_build_processing_job():
-    job = ProcessingJob.from_dict(VALID_JOB_DICT)
-    assert job.user_id == VALID_DATASET_DICT["user_id"]
-    assert job.dataset_name == VALID_DATASET_DICT["name"]
-    assert job.transcription_file_name == FILES_WITH_ELAN[0]
-    assert job.audio_file_name == FILES_WITH_ELAN[1]
-    assert job.options == DatasetOptions.from_dict(VALID_DATASET_OPTIONS_DICT)
+def test_create_processing_jobs():
+    dataset = CloudDataset.from_dict(CLOUD_DATASET_DICT)
 
+    def has_correct_attributes(job: ProcessingJob) -> bool:
+        return job.dataset_name == dataset.name and job.user_id == job.user_id
 
-def test_serialize_processing_job():
-    job = ProcessingJob.from_dict(VALID_JOB_DICT)
-    assert job.to_dict() == VALID_JOB_DICT
+    assert all(map(has_correct_attributes, dataset.to_jobs()))
diff --git a/functions/tests/utils/test_audio.py b/functions/tests/utils/test_audio.py
deleted file mode 100644
index c6260cd..0000000
--- a/functions/tests/utils/test_audio.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import wave
-from pathlib import Path
-
-from loguru import logger
-from utils.audio import cut, get_sample_rate, resample
-
-DATA_DIR = Path(__file__).parent.parent / "data"
-TARGET_SAMPLE_RATE = 23_000
-
-
-def test_cut(tmp_path: Path):
-    logger.info(f"Test cutting dir: {tmp_path}")
-    audio = DATA_DIR / "test.wav"
-    cut_audio = tmp_path / "test.wav"
-    start_ms = 0
-    stop_ms = 1000
-    sample_rate = get_sample_rate(audio)
-    cut(
-        audio_path=audio,
-        destination=cut_audio,
-        start_ms=start_ms,
-        stop_ms=stop_ms,
-    )
-
-    audio_wave = wave.open(str(cut_audio), "rb")
-    frame_count = audio_wave.getnframes()
-    audio_wave.close()
-
-    assert frame_count == (stop_ms - start_ms) * sample_rate / 1000
-    assert sample_rate == get_sample_rate(cut_audio)
-
-
-def test_resample(tmp_path: Path):
-    audio = DATA_DIR / "test.wav"
-    resampled_audio = tmp_path / "test.wav"
-    resample(audio, resampled_audio, TARGET_SAMPLE_RATE)
-    assert resampled_audio.exists()
-
-    resampled_audio_wave = wave.open(str(resampled_audio), "rb")
-    sample_rate = resampled_audio_wave.getframerate()
-    resampled_audio_wave.close()
-
-    assert sample_rate == TARGET_SAMPLE_RATE
-
-
-def test_get_sample_rate():
-    audio = DATA_DIR / "test.wav"
-    assert get_sample_rate(audio) == 16_000
diff --git a/functions/tests/utils/test_clean_text.py b/functions/tests/utils/test_clean_text.py
deleted file mode 100644
index 6f69362..0000000
--- a/functions/tests/utils/test_clean_text.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from models import DatasetOptions
-from utils.clean_text import clean_text, collapse, explode, remove_consecutive_spaces
-
-
-def test_explode():
-    text = "I commit;ted arson"
-    assert explode(text, ";") == "I commit ted arson"
-
-    text = ":::"
-    assert explode(text, ":") == (" " * len(text))
-
-
-def test_collapse():
-    text = "uwu"
-    assert collapse(text, "u") == "w"
-
-    text = ";"
-    assert collapse(text, ";") == ""
-
-
-def test_remove_consecutive_spaces():
-    text = "hey   there "
-    assert remove_consecutive_spaces(text) == "hey there "
-
-
-def test_clean_text_with_defaults_leaves_text_unchanged():
-    text = "unchanged"
-    assert clean_text(text, DatasetOptions()) == text
-
-
-def test_clean_text():
-    text = "This;is   going -to;-;be very inter-esting"
-    cleaning_options = DatasetOptions(
-        text_to_remove=["very"], punctuation_to_explode=";", punctuation_to_remove="-"
-    )
-    expected = "This is going to be interesting".lower()
-    assert clean_text(text, cleaning_options) == expected
diff --git a/functions/tests/utils/test_extract_annotations.py b/functions/tests/utils/test_extract_annotations.py
deleted file mode 100644
index 42f2da6..0000000
--- a/functions/tests/utils/test_extract_annotations.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from pathlib import Path
-from unittest.mock import Mock
-
-from models.dataset import ElanOptions
-from models.elan_tier_selector import TierSelector
-from utils.extract_annotations import (
-    extract_annotations,
-    extract_elan_annotations,
-    extract_text_annotations,
-    get_annotations_by_tier_name,
-    get_annotations_by_tier_order,
-    get_annotations_by_tier_type,
-)
-
-DATA_DIR = Path(__file__).parent.parent / "data"
-ELAN_PATH = DATA_DIR / "test.eaf"
-
-
-def test_extract_annotations_from_text_calls_proper_function(mocker):
-    path = Path("text.txt")
-    extract_text_mock: Mock = mocker.patch(
-        "utils.extract_annotations.extract_text_annotations"
-    )
-    extract_annotations(path)
-    extract_text_mock.assert_called_once_with(path)
-
-
-def test_extract_annotations_from_elan_without_elan_options():
-    path = Path("text.eaf")
-    annotations = extract_annotations(path)
-    assert len(annotations) == 0
-
-
-def test_extract_annotations_from_elan_with_elan_options(mocker):
-    elan_options = ElanOptions(
-        selection_value="Phrase", selection_mechanism=TierSelector.NAME
-    )
-    path = Path("text.eaf")
-    mock: Mock = mocker.patch("utils.extract_annotations.extract_elan_annotations")
-
-    extract_annotations(path, elan_options)
-    mock.assert_called_once_with(
-        path,
-        selection_type=elan_options.selection_mechanism,
-        selection_data=elan_options.selection_value,
-    )
-
-
-def test_extract_annotations_from_unknown_file_format():
-    path = Path("text.ipub")
-    annotations = extract_annotations(path)
-    assert len(annotations) == 0
-
-
-def test_extract_text_annotations(tmp_path: Path):
-    text = "hello"
-    path = tmp_path / "test.txt"
-    with open(path, "w") as f:
-        f.write(text)
-
-    annotations = extract_text_annotations(path)
-    assert len(annotations) == 1
-    annotation = annotations[0]
-    assert annotation.transcript == text
-    assert annotation.audio_file_name == path.stem + ".wav"
-
-
-def test_extract_elan_annotations(mocker):
-    path = Path()
-    selector = TierSelector.NAME
-    value = "Phrase"
-    name_mock: Mock = mocker.patch(
-        "utils.extract_annotations.get_annotations_by_tier_name"
-    )
-    extract_elan_annotations(path, selector, value)
-    name_mock.assert_called_once_with(path, value)
-
-    # Linguistic type
-    selector = TierSelector.TYPE
-    type_mock: Mock = mocker.patch(
-        "utils.extract_annotations.get_annotations_by_tier_type"
-    )
-    extract_elan_annotations(path, selector, value)
-    type_mock.assert_called_once_with(path, value)
-
-    # Tier Order
-    selector = TierSelector.ORDER
-    order_mock: Mock = mocker.patch(
-        "utils.extract_annotations.get_annotations_by_tier_order"
-    )
-    extract_elan_annotations(path, selector, value)
-    # Default if can't convert value to number
-    order_mock.assert_called_with(path, 1)
-
-    value = "2"
-    extract_elan_annotations(path, selector, value)
-    order_mock.assert_called_with(path, 2)
-
-
-def test_generate_utterances_from_tier_id():
-    annotations = get_annotations_by_tier_name(ELAN_PATH, "Phrase")
-    assert len(annotations) == 2
-    assert all(annotation.speaker_id == "SL" for annotation in annotations)
-
-
-def test_missing_linguistic_type():
-    annotations = get_annotations_by_tier_type(ELAN_PATH, "missing")
-    assert len(annotations) == 0
-
-
-def test_generate_utterances_from_linguistic_type():
-    annotations = get_annotations_by_tier_type(ELAN_PATH, "default-lt")
-    assert len(annotations) == 6
-
-
-def test_invalid_tier_order():
-    annotations = get_annotations_by_tier_order(ELAN_PATH, 69)
-    assert len(annotations) == 0
-
-
-def test_generate_utterances_from_tier_order():
-    for tier_order in range(1, 4):
-        annotations = get_annotations_by_tier_order(ELAN_PATH, tier_order)
-        assert len(annotations) == 2
diff --git a/functions/utils/audio.py b/functions/utils/audio.py
deleted file mode 100644
index 286d62d..0000000
--- a/functions/utils/audio.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from pathlib import Path
-
-from pedalboard.io import ReadableAudioFile, WriteableAudioFile
-
-
-def get_sample_rate(audio_path: Path) -> int:
-    """Gets the current sample rate of the given audio file.
-
-    Parameters:
-        audio_path: The path to the audio file.
-
-    Returns:
-        The sample rate of the given file.
-    """
-    with ReadableAudioFile(str(audio_path)) as audio_file:
-        return int(audio_file.samplerate)
-
-
-def resample(audio_path: Path, destination: Path, sample_rate: int) -> None:
-    """Copies a wav file to the destination, with the given
-    sample rate.
-
-    Parameters:
-        audio_path (Path): The path of the file to resample
-        destination (Path): The destination at which to create the resampled file
-        sample_rate (int): The sample rate for the resampled audio.
-    """
-    with ReadableAudioFile(str(audio_path)).resampled_to(sample_rate) as audio_file:
-        data = audio_file.read(audio_file.frames)
-        num_channels = audio_file.num_channels
-
-    with WriteableAudioFile(
-        str(destination), samplerate=sample_rate, num_channels=num_channels
-    ) as destination_file:
-        destination_file.write(data)
-
-
-def cut(audio_path: Path, destination: Path, start_ms: int, stop_ms: int) -> None:
-    """Creates a new wav file at the destination, restricted to the given start
-    and stop times.
-
-    Parameters:
-        audio_path (Path): The path of the file to resample.
-        destination (Path): The destination at which to create the resampled file.
-        start_ms (int): The start time in milliseconds to record from.
-        stop_ms (int):  The stop time in milliseconds to record to.
-    """
-
-    with ReadableAudioFile(str(audio_path)) as audio_file:
-        start = int(start_ms * audio_file.samplerate / 1000)
-        stop = int(stop_ms * audio_file.samplerate / 1000)
-
-        audio_file.seek(start)
-        data = audio_file.read(stop - start)
-        num_channels = audio_file.num_channels
-        sample_rate = audio_file.samplerate
-
-    with WriteableAudioFile(
-        str(destination), samplerate=sample_rate, num_channels=num_channels
-    ) as destination_file:
-        destination_file.write(data)
diff --git a/functions/utils/clean_text.py b/functions/utils/clean_text.py
deleted file mode 100644
index 5e3da7e..0000000
--- a/functions/utils/clean_text.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import re
-
-from models import DatasetOptions
-
-
-def clean_text(text: str, options: DatasetOptions) -> str:
-    """Cleans the text based on the supplied options.
-
-    Parameters:
-        text: The text to clean.
-        options: The cleaning options.
-
-    Returns:
-        The cleaned text
-    """
-    words = text.lower().split()
-    words = filter(lambda word: word not in options.text_to_remove, words)
-
-    if options.punctuation_to_explode != "":
-        words = map(lambda word: explode(word, options.punctuation_to_explode), words)
-
-    if options.punctuation_to_remove != "":
-        words = map(lambda word: collapse(word, options.punctuation_to_remove), words)
-
-    result = " ".join(words).strip()
-    return remove_consecutive_spaces(result)
-
-
-def explode(text: str, pattern: str) -> str:
-    """Replace occurences of the pattern with spaces within the given text.
-
-    Parameters:
-        text: The text to modify.
-        pattern: The pattern of characters to replace with spaces.
-
-    Returns:
-        The text with instances of the pattern exploded.
-    """
-    pattern = re.escape(pattern)
-    return re.sub(rf"[{pattern}]", " ", text)
-
-
-def collapse(text: str, pattern: str) -> str:
-    """Remove occurences of the pattern within the given text.
-
-    Parameters:
-        text: The text to modify.
-        pattern: The pattern of characters to remove.
-
-    Returns:
-        The text with instances of the pattern removed.
-    """
-    pattern = re.escape(pattern)
-    return re.sub(rf"[{pattern}]", "", text)
-
-
-def remove_consecutive_spaces(text: str) -> str:
-    """Replace consecutive spaces with a single one in some given text.
-
-    Parameters:
-        text: The text to modify.
-
-    Returns
-        The supplied text with conseucutive spaces reduced to one.
-    """
-    return re.sub("[ ]+", " ", text)
diff --git a/functions/utils/extract_annotations.py b/functions/utils/extract_annotations.py
deleted file mode 100644
index 99b40b9..0000000
--- a/functions/utils/extract_annotations.py
+++ /dev/null
@@ -1,189 +0,0 @@
-from itertools import chain
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-from loguru import logger
-from models import Annotation, TierSelector
-from models.dataset import ElanOptions
-from pympi.Elan import Eaf
-
-
-def extract_annotations(
-    transcription_file: Path, elan_options: Optional[ElanOptions] = None
-) -> List[Annotation]:
-    """Extracts annotations from the supplied transcription file.
-
-    If the transcription file is an elan file, elan_options is required.
-
-    Parameters:
-        transcription_file: The file from which to extract annotations
-        elan_options: Options to include for determining how to extract annotations
-                from elan data.
-
-    Returns:
-        A list of found annotations.
-        Returns an empty list if there was a problem.
-    """
-    if transcription_file.suffix == ".txt":
-        return extract_text_annotations(transcription_file)
-
-    if transcription_file.suffix != ".eaf":
-        logger.error(f"Unrecognised file format: {transcription_file}")
-        return []
-
-    if elan_options is None:
-        logger.error(f"Missing elan options for extraction job.")
-        return []
-
-    return extract_elan_annotations(
-        transcription_file,
-        selection_type=elan_options.selection_mechanism,
-        selection_data=elan_options.selection_value,
-    )
-
-
-def extract_text_annotations(file: Path) -> List[Annotation]:
-    """Extract transcription information from a text file.
-
-    Parameters:
-        file_name: The name of the downloaded file.
-
-    Returns:
-        A list of utterance information for the given file.
-    """
-    with open(file) as transcription_file:
-        transcription = transcription_file.read()
-
-    return [
-        Annotation(
-            audio_file_name=file.stem + ".wav",
-            transcript=transcription,
-        )
-    ]
-
-
-def extract_elan_annotations(
-    elan_file_path: Path, selection_type: TierSelector, selection_data: str
-) -> List[Annotation]:
-    """Extracts annotations from a particular tier in an eaf file (ELAN
-    Annotation Format).
-
-    Tiers are nodes from the tree structure in the .eaf file.
-    The tier to read from is determined by tier order (eg top tier would be order 1),
-    tier type (eg default-lt) or tier name (eg Phrase).
-
-    Parameters:
-        elan_file_path: The path to the eaf file.
-        selection_type: The method of determining which tier data to extract.
-        selection_data: The data corresponding to the selection_type.
-
-    Returns:
-        A list of the annotations contained for the supplied data. Returns an
-        empty list if the given selection isn't found.
-    """
-    logger.info(
-        f"processing eaf {elan_file_path} using {selection_type}: {selection_data}"
-    )
-
-    match selection_type:
-        case TierSelector.NAME:
-            return get_annotations_by_tier_name(elan_file_path, selection_data)
-        case TierSelector.TYPE:
-            return get_annotations_by_tier_type(elan_file_path, selection_data)
-        case TierSelector.ORDER:
-            try:
-                order = int(selection_data)
-            except:
-                order = 1
-            return get_annotations_by_tier_order(elan_file_path, order)
-
-
-def get_annotations_by_tier_order(
-    elan_file_path: Path, tier_order: int
-) -> List[Annotation]:
-    """Retrieves all annotations for a given tier order within an eaf file.
-
-    Parameters:
-        elan_file_path: The path to the eaf file.
-        tier_order: The tier order to extract from (starts at 1)
-
-    Returns:
-        A list of the annotations contained for the supplied tier order.
-        Returns an empty list if the given tier order exceeds the nesting of
-        the file.
-    """
-    elan = Eaf(elan_file_path)
-
-    tier_names: List[str] = list(elan.get_tier_names())
-    if tier_order > len(tier_names):
-        logger.error(
-            f"tier_order: {tier_order} exceeds tier length for {elan_file_path}"
-        )
-        return []
-
-    tier_name = tier_names[tier_order - 1]
-    return get_annotations_by_tier_name(
-        elan_file_path=elan_file_path, tier_name=tier_name
-    )
-
-
-def get_annotations_by_tier_type(
-    elan_file_path: Path, tier_type: str
-) -> List[Annotation]:
-    """Retrieves all annotations for a given linguistic tier type in an eaf file.
-
-    Parameters:
-        elan_file_path: The path to the eaf file.
-        tier_type: The linguistic type from which to extract Annotation data.
-
-    Returns:
-        A list of the annotations contained for the supplied linguistic type.
-        Returns an empty list if the type is not found.
-    """
-    elan = Eaf(elan_file_path)
-
-    if tier_type not in list(elan.get_linguistic_type_names()):
-        logger.error(f"tier_type: {tier_type} not found in file: {elan_file_path}")
-        return []
-
-    tier_names = elan.get_tier_ids_for_linguistic_type(tier_type)
-    annotations = (
-        get_annotations_by_tier_name(elan_file_path, name) for name in tier_names
-    )
-    # Flatten list of annotations
-    return list(chain(*annotations))
-
-
-def get_annotations_by_tier_name(
-    elan_file_path: Path, tier_name: str
-) -> List[Annotation]:
-    """Retrieves all annotations for a given tier name in an eaf file.
-
-    Parameters:
-        elan_file_path: The path to the eaf file.
-        tier_name: The tier name from which to extract Annotation data.
-
-    Returns:
-        A list of the annotations contained for the supplied tier name.
-        Returns an empty list if the name is not found.
-    """
-    elan = Eaf(elan_file_path)
-
-    if tier_name not in list(elan.get_tier_names()):
-        logger.error(f"tier_name: {tier_name} not found in file {elan_file_path}")
-        return []
-
-    parameters: Dict[str, str] = elan.get_parameters_for_tier(tier_name)
-    speaker_id = parameters.get("PARTICIPANT", None)
-
-    def create_annotation(elan_annotation: Tuple[str, str, str]) -> Annotation:
-        start, end, transcript = elan_annotation
-        return Annotation(
-            audio_file_name=f"{elan_file_path.stem}.wav",
-            transcript=transcript,
-            start_ms=int(start),
-            stop_ms=int(end),
-            speaker_id=speaker_id,
-        )
-
-    return list(map(create_annotation, elan.get_annotation_data_for_tier(tier_name)))
diff --git a/functions/utils/pubsub.py b/functions/utils/pubsub.py
index 24b071b..14c30b4 100644
--- a/functions/utils/pubsub.py
+++ b/functions/utils/pubsub.py
@@ -2,7 +2,7 @@
 from concurrent import futures
 from typing import Any, Callable, Iterable
 
-from google.cloud import pubsub_v1
+from google.cloud.pubsub_v1 import PublisherClient
 from google.cloud.pubsub_v1.futures import Future
 from loguru import logger
 
@@ -16,7 +16,7 @@ def publish_to_topic(topic_name: str, data: Iterable[Any], timeout: int = 60) ->
         data: A list of objects to serialize, encode and publish as separate messages
         timeout: How long the publishing should wait until erroring.
     """
-    publisher = pubsub_v1.PublisherClient()
+    publisher = PublisherClient()
     publish_futures = []
 
     def get_callback(publish_future: Future, data: str) -> Callable[[Future], None]: