From c283653c8645071ab706bd4762e7316c12690802 Mon Sep 17 00:00:00 2001
From: Harry Keightley <harrykeightley@outlook.com>
Date: Mon, 16 Oct 2023 10:16:14 +1000
Subject: [PATCH 1/8] Remove unused imports from example script

---
 example.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/example.py b/example.py
index 458b397..c5fa795 100644
--- a/example.py
+++ b/example.py
@@ -1,11 +1,9 @@
 import shutil
-from itertools import groupby, takewhile
 from pathlib import Path
-from pprint import pprint
 
 from loguru import logger
 from tqdm import tqdm
-from transformers import TrainingArguments, training_args
+from transformers import TrainingArguments
 
 from elpis.datasets import Dataset
 from elpis.datasets.dataset import CleaningOptions

From 97c00462e3e524c1fd5f0b7869b719f84070f416 Mon Sep 17 00:00:00 2001
From: Harry Keightley <harrykeightley@outlook.com>
Date: Mon, 16 Oct 2023 10:16:50 +1000
Subject: [PATCH 2/8] Downgrade torch version to fix missing cuda dependencies

---
 poetry.lock    | 1641 ++++++++++++++++++++++++++++++------------------
 pyproject.toml |    2 +-
 2 files changed, 1018 insertions(+), 625 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index d960003..a8191fb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -25,7 +25,7 @@ testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized",
 
 [[package]]
 name = "aiohttp"
-version = "3.8.5"
+version = "3.8.6"
 description = "Async http client/server framework (asyncio)"
 category = "main"
 optional = false
@@ -79,12 +79,15 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte
 
 [[package]]
 name = "audioread"
-version = "3.0.0"
-description = "multi-library, cross-platform audio decoding"
+version = "3.0.1"
+description = "Multi-library, cross-platform audio decoding."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[package.extras]
+test = ["tox"]
+
 [[package]]
 name = "black"
 version = "22.12.0"
@@ -116,18 +119,18 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "cffi"
-version = "1.15.1"
+version = "1.16.0"
 description = "Foreign Function Interface for Python calling C code."
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 
 [package.dependencies]
 pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.2.0"
+version = "3.3.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 category = "main"
 optional = false
@@ -144,6 +147,17 @@ python-versions = ">=3.7"
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "cmake"
+version = "3.27.7"
+description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.extras]
+test = ["coverage (>=4.2)", "flake8 (>=3.0.4)", "path.py (>=11.5.0)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)", "pytest-runner (>=2.9)", "pytest-virtualenv (>=1.7.0)", "scikit-build (>=0.10.0)", "setuptools (>=28.0.0)", "virtualenv (>=15.0.3)", "wheel"]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -165,7 +179,7 @@ toml = ["tomli"]
 
 [[package]]
 name = "datasets"
-version = "2.14.4"
+version = "2.14.5"
 description = "HuggingFace community-driven open-source library of datasets"
 category = "main"
 optional = false
@@ -174,7 +188,7 @@ python-versions = ">=3.8.0"
 [package.dependencies]
 aiohttp = "*"
 dill = ">=0.3.0,<0.3.8"
-fsspec = {version = ">=2021.11.1", extras = ["http"]}
+fsspec = {version = ">=2023.1.0,<2023.9.0", extras = ["http"]}
 huggingface-hub = ">=0.14.0,<1.0.0"
 multiprocess = "*"
 numpy = ">=1.17"
@@ -223,7 +237,7 @@ graph = ["objgraph (>=1.7.2)"]
 
 [[package]]
 name = "evaluate"
-version = "0.4.0"
+version = "0.4.1"
 description = "HuggingFace community-driven open-source library of evaluation"
 category = "main"
 optional = false
@@ -244,14 +258,14 @@ tqdm = ">=4.62.1"
 xxhash = "*"
 
 [package.extras]
-dev = ["Werkzeug (>=1.0.1)", "absl-py", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
+dev = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
 docs = ["s3fs"]
 evaluator = ["scipy (>=1.7.1)", "transformers"]
 quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"]
 template = ["cookiecutter", "gradio (>=3.0.0)"]
 tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)"]
 tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tests = ["Werkzeug (>=1.0.1)", "absl-py", "bert-score (>=0.3.6)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
+tests = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
 torch = ["torch"]
 
 [[package]]
@@ -267,18 +281,16 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.12.3"
+version = "3.12.4"
 description = "A platform independent file lock."
 category = "main"
 optional = false
 python-versions = ">=3.8"
 
-[package.dependencies]
-typing-extensions = {version = ">=4.7.1", markers = "python_version < \"3.11\""}
-
 [package.extras]
 docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
 testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
+typing = ["typing-extensions (>=4.7.1)"]
 
 [[package]]
 name = "frozenlist"
@@ -290,7 +302,7 @@ python-versions = ">=3.8"
 
 [[package]]
 name = "fsspec"
-version = "2023.9.0"
+version = "2023.6.0"
 description = "File-system specification"
 category = "main"
 optional = false
@@ -326,11 +338,11 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.16.4"
+version = "0.17.3"
 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"
+python-versions = ">=3.8.0"
 
 [package.dependencies]
 filelock = "*"
@@ -342,16 +354,17 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
+docs = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "hf-doc-builder", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)", "watchdog"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-inference = ["aiohttp", "pydantic"]
-quality = ["black (>=23.1,<24.0)", "mypy (==0.982)", "ruff (>=0.0.241)"]
+inference = ["aiohttp", "pydantic (<2.0)"]
+quality = ["black (==23.7)", "mypy (==1.5.1)", "ruff (>=0.0.241)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 torch = ["torch"]
-typing = ["pydantic", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+typing = ["pydantic (<2.0)", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
 
 [[package]]
 name = "idna"
@@ -429,6 +442,14 @@ 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 = "lit"
+version = "17.0.2"
+description = "A Software Testing Tool"
+category = "main"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "llvmlite"
 version = "0.41.0"
@@ -470,14 +491,14 @@ testing = ["pytest"]
 
 [[package]]
 name = "markdown"
-version = "3.4.4"
+version = "3.5"
 description = "Python implementation of John Gruber's Markdown."
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.extras]
-docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.0)", "mkdocs-nature (>=0.4)"]
+docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
 testing = ["coverage", "pyyaml"]
 
 [[package]]
@@ -575,9 +596,133 @@ category = "main"
 optional = false
 python-versions = ">=3.9"
 
+[[package]]
+name = "nvidia-cublas-cu11"
+version = "11.10.3.66"
+description = "CUBLAS native runtime libraries"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
+[[package]]
+name = "nvidia-cuda-cupti-cu11"
+version = "11.7.101"
+description = "CUDA profiling tools runtime libs."
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu11"
+version = "11.7.99"
+description = "NVRTC native runtime libraries"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
+[[package]]
+name = "nvidia-cuda-runtime-cu11"
+version = "11.7.99"
+description = "CUDA Runtime native Libraries"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
+[[package]]
+name = "nvidia-cudnn-cu11"
+version = "8.5.0.96"
+description = "cuDNN runtime libraries"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
+[[package]]
+name = "nvidia-cufft-cu11"
+version = "10.9.0.58"
+description = "CUFFT native runtime libraries"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[[package]]
+name = "nvidia-curand-cu11"
+version = "10.2.10.91"
+description = "CURAND native runtime libraries"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
+[[package]]
+name = "nvidia-cusolver-cu11"
+version = "11.4.0.1"
+description = "CUDA solver native runtime libraries"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu11"
+version = "11.7.4.91"
+description = "CUSPARSE native runtime libraries"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
+[[package]]
+name = "nvidia-nccl-cu11"
+version = "2.14.3"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[[package]]
+name = "nvidia-nvtx-cu11"
+version = "11.7.91"
+description = "NVIDIA Tools Extension"
+category = "main"
+optional = false
+python-versions = ">=3"
+
+[package.dependencies]
+setuptools = "*"
+wheel = "*"
+
 [[package]]
 name = "packaging"
-version = "23.1"
+version = "23.2"
 description = "Core utilities for Python packages"
 category = "main"
 optional = false
@@ -591,10 +736,48 @@ category = "main"
 optional = false
 python-versions = ">=3.9"
 
+[package.dependencies]
+numpy = {version = ">=1.23.2", markers = "python_version >= \"3.11\""}
+python-dateutil = ">=2.8.2"
+pytz = ">=2020.1"
+tzdata = ">=2022.1"
+
+[package.extras]
+all = ["PyQt5 (>=5.15.6)", "SQLAlchemy (>=1.4.36)", "beautifulsoup4 (>=4.11.1)", "bottleneck (>=1.3.4)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=0.8.1)", "fsspec (>=2022.05.0)", "gcsfs (>=2022.05.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.8.0)", "matplotlib (>=3.6.1)", "numba (>=0.55.2)", "numexpr (>=2.8.0)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pandas-gbq (>=0.17.5)", "psycopg2 (>=2.9.3)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.5)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "pyxlsb (>=1.0.9)", "qtpy (>=2.2.0)", "s3fs (>=2022.05.0)", "scipy (>=1.8.1)", "tables (>=3.7.0)", "tabulate (>=0.8.10)", "xarray (>=2022.03.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)", "zstandard (>=0.17.0)"]
+aws = ["s3fs (>=2022.05.0)"]
+clipboard = ["PyQt5 (>=5.15.6)", "qtpy (>=2.2.0)"]
+compression = ["zstandard (>=0.17.0)"]
+computation = ["scipy (>=1.8.1)", "xarray (>=2022.03.0)"]
+consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pyxlsb (>=1.0.9)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)"]
+feather = ["pyarrow (>=7.0.0)"]
+fss = ["fsspec (>=2022.05.0)"]
+gcp = ["gcsfs (>=2022.05.0)", "pandas-gbq (>=0.17.5)"]
+hdf5 = ["tables (>=3.7.0)"]
+html = ["beautifulsoup4 (>=4.11.1)", "html5lib (>=1.1)", "lxml (>=4.8.0)"]
+mysql = ["SQLAlchemy (>=1.4.36)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.8.10)"]
+parquet = ["pyarrow (>=7.0.0)"]
+performance = ["bottleneck (>=1.3.4)", "numba (>=0.55.2)", "numexpr (>=2.8.0)"]
+plot = ["matplotlib (>=3.6.1)"]
+postgresql = ["SQLAlchemy (>=1.4.36)", "psycopg2 (>=2.9.3)"]
+spss = ["pyreadstat (>=1.1.5)"]
+sql-other = ["SQLAlchemy (>=1.4.36)"]
+test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.8.0)"]
+
+[[package]]
+name = "pandas"
+version = "2.1.1"
+description = "Powerful data structures for data analysis, time series, and statistics"
+category = "main"
+optional = false
+python-versions = ">=3.9"
+
 [package.dependencies]
 numpy = [
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -657,7 +840,7 @@ numpy = "*"
 
 [[package]]
 name = "platformdirs"
-version = "3.10.0"
+version = "3.11.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 category = "main"
 optional = false
@@ -699,11 +882,11 @@ xxhash = ["xxhash (>=1.4.3)"]
 
 [[package]]
 name = "psutil"
-version = "5.9.5"
+version = "5.9.6"
 description = "Cross-platform lib for process and system monitoring in Python."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 
 [package.extras]
 test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
@@ -740,7 +923,7 @@ test = ["coverage (>=4.2)", "lxml", "pytest (>=5)", "pytest-cov", "pytest-mock"]
 
 [[package]]
 name = "pyright"
-version = "1.1.325"
+version = "1.1.331"
 description = "Command line wrapper for pyright"
 category = "dev"
 optional = false
@@ -755,7 +938,7 @@ dev = ["twine (>=3.4.1)"]
 
 [[package]]
 name = "pytest"
-version = "7.4.1"
+version = "7.4.2"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
@@ -815,7 +998,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "rapidfuzz"
-version = "3.2.0"
+version = "3.4.0"
 description = "rapid fuzzy string matching"
 category = "main"
 optional = false
@@ -826,11 +1009,11 @@ full = ["numpy"]
 
 [[package]]
 name = "regex"
-version = "2023.8.8"
+version = "2023.10.3"
 description = "Alternative regular expression module, to replace re."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [[package]]
 name = "requests"
@@ -904,7 +1087,7 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
 [[package]]
 name = "scikit-learn"
-version = "1.3.0"
+version = "1.3.1"
 description = "A set of python modules for machine learning and data mining"
 category = "main"
 optional = false
@@ -912,7 +1095,7 @@ python-versions = ">=3.8"
 
 [package.dependencies]
 joblib = ">=1.1.1"
-numpy = ">=1.17.3"
+numpy = ">=1.17.3,<2.0"
 scipy = ">=1.5.0"
 threadpoolctl = ">=2.0.0"
 
@@ -940,16 +1123,16 @@ test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "sciki
 
 [[package]]
 name = "setuptools"
-version = "68.1.2"
+version = "68.2.2"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.8"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5,<=7.1.2)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "six"
@@ -1032,7 +1215,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "torch"
-version = "2.1.0"
+version = "2.0.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 category = "main"
 optional = false
@@ -1040,10 +1223,21 @@ python-versions = ">=3.8.0"
 
 [package.dependencies]
 filelock = "*"
-fsspec = "*"
 jinja2 = "*"
 networkx = "*"
+nvidia-cublas-cu11 = {version = "11.10.3.66", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu11 = {version = "11.7.101", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu11 = {version = "8.5.0.96", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu11 = {version = "10.9.0.58", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu11 = {version = "10.2.10.91", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu11 = {version = "11.4.0.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu11 = {version = "11.7.4.91", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu11 = {version = "2.14.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu11 = {version = "11.7.91", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
+triton = {version = "2.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 typing-extensions = "*"
 
 [package.extras]
@@ -1132,13 +1326,31 @@ torchhub = ["filelock", "huggingface-hub (>=0.16.4,<1.0)", "importlib-metadata",
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (<10.0.0)"]
 
+[[package]]
+name = "triton"
+version = "2.0.0"
+description = "A language and compiler for custom Deep Learning operations"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+cmake = "*"
+filelock = "*"
+lit = "*"
+torch = "*"
+
+[package.extras]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
 [[package]]
 name = "typing-extensions"
-version = "4.7.1"
-description = "Backported and Experimental Type Hints for Python 3.7+"
+version = "4.8.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [[package]]
 name = "tzdata"
@@ -1150,7 +1362,7 @@ python-versions = ">=2"
 
 [[package]]
 name = "urllib3"
-version = "2.0.4"
+version = "2.0.6"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "main"
 optional = false
@@ -1162,6 +1374,17 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "wheel"
+version = "0.41.2"
+description = "A built-package format for Python"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
+
 [[package]]
 name = "win32-setctime"
 version = "1.1.0"
@@ -1175,7 +1398,7 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
 
 [[package]]
 name = "xxhash"
-version = "3.3.0"
+version = "3.4.1"
 description = "Python binding for xxHash"
 category = "main"
 optional = false
@@ -1196,7 +1419,7 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.10"
-content-hash = "3184bb6d2c863964bbdafedcab7616714537ee961561fb0f39bd405a1e350bc4"
+content-hash = "500d7658aacde1b1b381fbdd228be52618d89051c8cec2d580a07c4c19d501d6"
 
 [metadata.files]
 accelerate = [
@@ -1204,93 +1427,93 @@ accelerate = [
     {file = "accelerate-0.22.0.tar.gz", hash = "sha256:2b0a83e3cd07c89448c5d5a94f72bc1db98d5e0c498ca17984871f01dbf83247"},
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+    {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9ecb6c987b62437c2f99c01e97caf8d25660bf541fe79a481d05732e5236719c"},
+    {file = "xxhash-3.4.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:696b4e18b7023527d5c50ed0626ac0520edac45a50ec7cf3fc265cd08b1f4c03"},
+    {file = "xxhash-3.4.1.tar.gz", hash = "sha256:0379d6cf1ff987cd421609a264ce025e74f346e3e145dd106c0cc2e3ec3f99a9"},
 ]
 yarl = [
     {file = "yarl-1.9.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8c2ad583743d16ddbdf6bb14b5cd76bf43b0d0006e918809d5d4ddf7bde8dd82"},
diff --git a/pyproject.toml b/pyproject.toml
index 14cd95a..e649396 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,7 @@ evaluate = "^0.4.0"
 accelerate = "^0.22.0"
 jiwer = "^3.0.3"
 sounddevice = "^0.4.6"
-torch = "^2.1.0"
+torch = ">=2.0.0, !=2.0.1, !=2.1.0"
 pedalboard = "^0.8.3"
 
 [tool.poetry.dev-dependencies]

From 4c21ff87a176b0b6deaae816cf7922e2fd0c1e09 Mon Sep 17 00:00:00 2001
From: Harry Keightley <harrykeightley@outlook.com>
Date: Mon, 16 Oct 2023 10:17:11 +1000
Subject: [PATCH 3/8] Allow `Job`s to be constructed from dictionaries

---
 elpis/models/job.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/elpis/models/job.py b/elpis/models/job.py
index fa146a8..a421d16 100644
--- a/elpis/models/job.py
+++ b/elpis/models/job.py
@@ -2,7 +2,7 @@
 
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import List, Optional
+from typing import Any, Dict, List, Optional
 
 from transformers import HfArgumentParser, TrainingArguments
 
@@ -289,12 +289,12 @@ def parser():
         return HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))  # type: ignore
 
     @classmethod
-    def from_args(cls) -> Job:
+    def from_args(cls, args=None) -> Job:
         (
             model_args,
             data_args,
             training_args,
-        ) = Job.parser().parse_args_into_dataclasses()
+        ) = Job.parser().parse_args_into_dataclasses(args)
         return cls(
             model_args=model_args, data_args=data_args, training_args=training_args
         )
@@ -309,3 +309,14 @@ def from_json(cls, file: Path) -> Job:
         return cls(
             model_args=model_args, data_args=data_args, training_args=training_args
         )
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> Job:
+        (
+            model_args,
+            data_args,
+            training_args,
+        ) = Job.parser().parse_dict(data)
+        return cls(
+            model_args=model_args, data_args=data_args, training_args=training_args
+        )

From e05981cb981615e793f584bcbe6405505f211401 Mon Sep 17 00:00:00 2001
From: Harry Keightley <harrykeightley@outlook.com>
Date: Mon, 16 Oct 2023 11:40:53 +1000
Subject: [PATCH 4/8] Set seed before training and log when train/eval are
 skipped.

---
 elpis/trainer/trainer.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/elpis/trainer/trainer.py b/elpis/trainer/trainer.py
index eb1e2dd..a485500 100644
--- a/elpis/trainer/trainer.py
+++ b/elpis/trainer/trainer.py
@@ -13,6 +13,7 @@
     AutoTokenizer,
     Trainer,
     Wav2Vec2Processor,
+    set_seed,
 )
 from transformers.trainer_utils import get_last_checkpoint, is_main_process
 
@@ -46,6 +47,8 @@ def run_job(
         cache_dir = job.model_args.cache_dir
         Path(output_dir).mkdir(exist_ok=True, parents=True)
 
+        set_seed(job.training_args.seed)
+
         logger.info("Preparing Datasets...")
         config = create_config(job)
         dataset = create_dataset(job)
@@ -256,6 +259,7 @@ def last_checkpoint(job: Job) -> Optional[str]:
 
 def train(job: Job, trainer: Trainer, dataset: DatasetDict):
     if not job.training_args.do_train:
+        logger.info("Skipping training: `job.training_args.do_train` is false.")
         return
 
     checkpoint = last_checkpoint(job)
@@ -283,6 +287,7 @@ def train(job: Job, trainer: Trainer, dataset: DatasetDict):
 
 def evaluate(job: Job, trainer: Trainer, dataset: DatasetDict):
     if not job.training_args.do_eval:
+        logger.info("Skipping eval: `job.training_args.do_eval` is false.")
         return
 
     logger.info("*** Evaluate ***")

From a09769224ea836c750bc1fc3952b4ada18e1c3b7 Mon Sep 17 00:00:00 2001
From: Harry Keightley <harrykeightley@outlook.com>
Date: Mon, 16 Oct 2023 11:41:31 +1000
Subject: [PATCH 5/8] Update `clean_text` parameters to be more general (punc
 -> char)

---
 elpis/datasets/__init__.py      |  2 +-
 elpis/datasets/clean_text.py    | 24 +++++++++++++++---------
 elpis/datasets/preprocessing.py |  4 ++--
 3 files changed, 18 insertions(+), 12 deletions(-)

diff --git a/elpis/datasets/__init__.py b/elpis/datasets/__init__.py
index 37e9e2d..14561d2 100644
--- a/elpis/datasets/__init__.py
+++ b/elpis/datasets/__init__.py
@@ -1,6 +1,6 @@
 from elpis.datasets.dataset import CleaningOptions, Dataset, ProcessingBatch
 from elpis.datasets.preprocessing import process_batch
-from elpis.datasets.processing import create_dataset, prepare_dataset
+from elpis.datasets.processing import prepare_dataset, create_dataset
 
 __all__ = [
     "CleaningOptions",
diff --git a/elpis/datasets/clean_text.py b/elpis/datasets/clean_text.py
index 76b445d..ee0754c 100644
--- a/elpis/datasets/clean_text.py
+++ b/elpis/datasets/clean_text.py
@@ -5,31 +5,37 @@
 def clean_text(
     text: str,
     words_to_remove: Optional[List[str]] = None,
-    punctuation_to_explode: str = "",
-    punctuation_to_remove: str = "",
+    characters_to_explode: str = "",
+    characters_to_remove: str = "",
+    to_lower=True,
 ) -> str:
     """Cleans the text based on the supplied options.
 
     Parameters:
         text: The text to clean.
-        options: The cleaning options.
+        words_to_remove: Words to remove from the text.
+        characters_to_remove: A string of chars to remove from the text.
+        characters_to_explode: A string of chars to replace with spaces in the text.
+        to_lower: True iff the resulting text should be converted to lower case.
+            Converts to uppercase if False.
 
     Returns:
         The cleaned text
     """
-    words = text.upper().split()
+    words = text.split()
 
     if words_to_remove is not None:
         words = filter(lambda word: word not in words_to_remove, words)
 
-    if punctuation_to_explode != "":
-        words = map(lambda word: explode(word, punctuation_to_explode), words)
+    if characters_to_explode != "":
+        words = map(lambda word: explode(word, characters_to_explode), words)
 
-    if punctuation_to_remove != "":
-        words = map(lambda word: collapse(word, punctuation_to_remove), words)
+    if characters_to_remove != "":
+        words = map(lambda word: collapse(word, characters_to_remove), words)
 
     result = " ".join(words).strip()
-    return remove_consecutive_spaces(result)
+    result = remove_consecutive_spaces(result)
+    return result.lower() if to_lower else result.upper()
 
 
 def explode(text: str, pattern: str) -> str:
diff --git a/elpis/datasets/preprocessing.py b/elpis/datasets/preprocessing.py
index 29307d1..d2cdfe0 100644
--- a/elpis/datasets/preprocessing.py
+++ b/elpis/datasets/preprocessing.py
@@ -63,8 +63,8 @@ def clean_annotation(
     transcript = clean_text(
         text=annotation.transcript,
         words_to_remove=cleaning_options.words_to_remove,
-        punctuation_to_explode=cleaning_options.punctuation_to_explode,
-        punctuation_to_remove=cleaning_options.punctuation_to_remove,
+        characters_to_explode=cleaning_options.punctuation_to_explode,
+        characters_to_remove=cleaning_options.punctuation_to_remove,
     )
     result = copy(annotation)
     result.transcript = transcript

From 12e2b41c3f9801f66342bb5491b4cba7af773e4e Mon Sep 17 00:00:00 2001
From: Harry Keightley <harrykeightley@outlook.com>
Date: Mon, 16 Oct 2023 11:42:59 +1000
Subject: [PATCH 6/8] Allow creation of datasets from huggingface. Run cleaning
 and sample constraints on constructed datasets.

---
 elpis/datasets/processing.py      | 96 ++++++++++++++++++++++++++++++-
 elpis/models/__init__.py          | 12 +++-
 elpis/models/job.py               | 32 ++++++++---
 tests/datasets/test_processing.py |  6 +-
 4 files changed, 134 insertions(+), 12 deletions(-)

diff --git a/elpis/datasets/processing.py b/elpis/datasets/processing.py
index 085821a..5eec97f 100644
--- a/elpis/datasets/processing.py
+++ b/elpis/datasets/processing.py
@@ -6,12 +6,20 @@
 from loguru import logger
 from transformers import AutoFeatureExtractor, AutoTokenizer
 
+from elpis.datasets.clean_text import clean_text
 from elpis.models.job import Job
 
 LOGGING_TRANSCRIPT_SAMPLE = 2
 
 
-def create_dataset(
+def create_dataset(job: Job) -> DatasetDict:
+    if Path(job.data_args.dataset_name_or_path).is_dir():
+        return create_local_dataset(job)
+
+    return create_hf_dataset(job)
+
+
+def create_local_dataset(
     job: Job,
     test_size: float = 0.2,
 ) -> DatasetDict:
@@ -65,6 +73,45 @@ def resolve_audio_path(row: Dict[str, Any]) -> Dict[str, Any]:
     return dataset
 
 
+def create_hf_dataset(job: Job) -> DatasetDict:
+    dataset = DatasetDict()
+    data_args = job.data_args
+
+    if job.training_args.do_train:
+        dataset["train"] = load_dataset(
+            data_args.dataset_name_or_path,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            token=data_args.token,
+        )
+
+        if data_args.audio_column_name not in dataset["train"].column_names:
+            raise ValueError(
+                f"audio_column_name '{data_args.audio_column_name}' not found"
+                f" in dataset '{data_args.dataset_name_or_path}'."
+                " Make sure to set `audio_column_name` to the correct audio column - one of"
+                f" {', '.join(dataset['train'].column_names)}."
+            )
+
+        if data_args.text_column_name not in dataset["train"].column_names:
+            raise ValueError(
+                f"text_column_name {data_args.text_column_name} not found"
+                f" in dataset '{data_args.dataset_name_or_path}'. "
+                "Make sure to set `text_column_name` to the correct text column - one of "
+                f"{', '.join(dataset['train'].column_names)}."
+            )
+
+    if job.training_args.do_eval:
+        dataset["eval"] = load_dataset(
+            data_args.dataset_name_or_path,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            token=data_args.token,
+        )
+
+    return dataset
+
+
 def prepare_dataset(
     job: Job,
     tokenizer: AutoTokenizer,
@@ -77,6 +124,8 @@ def prepare_dataset(
         dataset: The dataset on which to apply the preprocessing
         processor: The processor to apply over the dataset
     """
+    dataset = clean_dataset(job, dataset)
+    dataset = constrain_to_max_samples(job, dataset)
 
     # Load the audio data and resample if necessary.
     dataset = dataset.cast_column(
@@ -131,3 +180,48 @@ def is_audio_in_length_range(length: int):
     logger.info(f"Test encoding labels: {dataset['train'][0]['labels']}")
 
     return dataset
+
+
+def constrain_to_max_samples(job: Job, dataset: DatasetDict) -> DatasetDict:
+    max_train_samples = job.data_args.max_train_samples
+    max_eval_samples = job.data_args.max_eval_samples
+
+    if job.training_args.do_train and max_train_samples is not None:
+        dataset["train"] = dataset["train"].select(range(max_train_samples))
+
+    if job.training_args.do_eval and max_eval_samples is not None:
+        dataset["eval"] = dataset["eval"].select(range(max_eval_samples))
+
+    return dataset
+
+
+def clean_dataset(job: Job, dataset: DatasetDict) -> DatasetDict:
+    if not job.data_args.do_clean:
+        return dataset
+
+    text_column = job.data_args.text_column_name
+
+    def clean(batch: Dict[str, Any]):
+        characters_to_remove = "".join(job.data_args.chars_to_remove or [])
+        characters_to_explode = "".join(job.data_args.chars_to_explode or [])
+
+        batch[text_column] = (
+            clean_text(
+                batch[text_column],
+                words_to_remove=job.data_args.words_to_remove,
+                characters_to_remove=characters_to_remove,
+                characters_to_explode=characters_to_explode,
+                to_lower=job.data_args.do_lower_case or True,
+            )
+            + " "  # Note: not sure why this is necessary, but saw in hf docs.
+        )
+
+        return batch
+
+    with job.training_args.main_process_first(desc="Dataset cleaning."):
+        dataset = dataset.map(
+            clean,
+            desc="Cleaning the dataset and standardizing case.",
+        )
+
+    return dataset
diff --git a/elpis/models/__init__.py b/elpis/models/__init__.py
index 33ec9f4..1de4029 100644
--- a/elpis/models/__init__.py
+++ b/elpis/models/__init__.py
@@ -1,5 +1,15 @@
 from elpis.models.annotation import Annotation
 from elpis.models.elan_options import ElanOptions, ElanTierSelector
+from elpis.models.job import DataArguments, Job, ModelArguments
 from elpis.models.vocab import VOCAB_FILE, Vocab
 
-__all__ = ["Annotation", "ElanOptions", "ElanTierSelector", "Vocab", "VOCAB_FILE"]
+__all__ = [
+    "Annotation",
+    "ElanOptions",
+    "ElanTierSelector",
+    "Job",
+    "Vocab",
+    "VOCAB_FILE",
+    "DataArguments",
+    "ModelArguments",
+]
diff --git a/elpis/models/job.py b/elpis/models/job.py
index a421d16..9b99bb3 100644
--- a/elpis/models/job.py
+++ b/elpis/models/job.py
@@ -186,11 +186,33 @@ class DataArguments:
             )
         },
     )
-    chars_to_ignore: Optional[List[str]] = list_field(
+    do_clean: bool = field(
+        default=True,
+        metadata={"help": "True if the dataset should be cleaned before use."},
+    )
+    words_to_remove: Optional[List[str]] = list_field(
+        default=[],
+        metadata={
+            "help": "A list of words to remove from the transcripts during dataset cleaning."
+        },
+    )
+    chars_to_remove: Optional[List[str]] = list_field(
+        default=[],
+        metadata={
+            "help": "A list of characters to remove from the transcripts during dataset cleaning."
+        },
+    )
+    chars_to_explode: Optional[List[str]] = list_field(
+        default=[],
+        metadata={
+            "help": "A list of characters to replace with spaces in the transcripts during dataset cleaning."
+        },
+    )
+    do_lower_case: Optional[bool] = field(
         default=None,
-        metadata={"help": "A list of characters to remove from the transcripts."},
+        metadata={"help": "Whether the target text should be lower cased."},
     )
-    eval_metrics: List[str] = list_field(
+    eval_metrics: List[str] = list_field(  # type: ignore
         default=DEFAULT_METRICS,
         metadata={
             "help": "A list of metrics the model should be evaluated on. E.g. `('wer', 'cer')`"
@@ -270,10 +292,6 @@ class DataArguments:
             )
         },
     )
-    do_lower_case: Optional[bool] = field(
-        default=None,
-        metadata={"help": "Whether the target text should be lower cased."},
-    )
 
 
 @dataclass
diff --git a/tests/datasets/test_processing.py b/tests/datasets/test_processing.py
index f671e60..fa28f8e 100644
--- a/tests/datasets/test_processing.py
+++ b/tests/datasets/test_processing.py
@@ -5,13 +5,13 @@
 from loguru import logger
 from transformers import TrainingArguments
 
-from elpis.datasets.processing import create_dataset
+from elpis.datasets.processing import create_local_dataset
 from elpis.models.job import DataArguments, Job, ModelArguments
 
 DATA_PATH = Path(__file__).parent.parent / "data" / "processing"
 
 
-def test_create_dataset(tmp_path: Path):
+def test_create_local_dataset(tmp_path: Path):
     cache_dir = tmp_path / "cache"
     dataset_dir = tmp_path / "dataset"
     model_dir = tmp_path / "model"
@@ -39,6 +39,6 @@ def test_create_dataset(tmp_path: Path):
         ),
     )
 
-    dataset = create_dataset(job)
+    dataset = create_local_dataset(job)
     assert "train" in dataset
     assert "eval" in dataset

From 2f0df2ff8977cbec56911a5994b46654a83f2430 Mon Sep 17 00:00:00 2001
From: Harry Keightley <harrykeightley@outlook.com>
Date: Tue, 17 Oct 2023 17:22:48 +1000
Subject: [PATCH 7/8] Allow training on hf datasets

---
 elpis/datasets/processing.py |  61 ++++++++++++------
 elpis/models/job.py          |   6 ++
 elpis/models/vocab.py        |   2 +
 elpis/trainer/trainer.py     | 116 ++++++++++++++++++++++++-----------
 hf_example.py                |  86 ++++++++++++++++++++++++++
 5 files changed, 215 insertions(+), 56 deletions(-)
 create mode 100644 hf_example.py

diff --git a/elpis/datasets/processing.py b/elpis/datasets/processing.py
index 5eec97f..29c09de 100644
--- a/elpis/datasets/processing.py
+++ b/elpis/datasets/processing.py
@@ -2,7 +2,14 @@
 from pathlib import Path
 from typing import Any, Dict, List
 
-from datasets import Audio, DatasetDict, load_dataset
+from datasets import (
+    Audio,
+    Dataset,
+    DatasetDict,
+    IterableDataset,
+    IterableDatasetDict,
+    load_dataset,
+)
 from loguru import logger
 from transformers import AutoFeatureExtractor, AutoTokenizer
 
@@ -12,7 +19,7 @@
 LOGGING_TRANSCRIPT_SAMPLE = 2
 
 
-def create_dataset(job: Job) -> DatasetDict:
+def create_dataset(job: Job) -> DatasetDict | IterableDatasetDict:
     if Path(job.data_args.dataset_name_or_path).is_dir():
         return create_local_dataset(job)
 
@@ -73,14 +80,18 @@ def resolve_audio_path(row: Dict[str, Any]) -> Dict[str, Any]:
     return dataset
 
 
-def create_hf_dataset(job: Job) -> DatasetDict:
-    dataset = DatasetDict()
+def create_hf_dataset(job: Job) -> DatasetDict | IterableDatasetDict:
     data_args = job.data_args
 
+    dataset = DatasetDict()
+    if data_args.stream_dataset:
+        dataset = IterableDatasetDict()
+
     if job.training_args.do_train:
         dataset["train"] = load_dataset(
             data_args.dataset_name_or_path,
             data_args.dataset_config_name,
+            streaming=data_args.stream_dataset,
             split=data_args.train_split_name,
             token=data_args.token,
         )
@@ -107,6 +118,7 @@ def create_hf_dataset(job: Job) -> DatasetDict:
             data_args.dataset_config_name,
             split=data_args.eval_split_name,
             token=data_args.token,
+            streaming=data_args.stream_dataset,
         )
 
     return dataset
@@ -116,8 +128,8 @@ def prepare_dataset(
     job: Job,
     tokenizer: AutoTokenizer,
     feature_extractor: AutoFeatureExtractor,
-    dataset: DatasetDict,
-) -> DatasetDict:
+    dataset: DatasetDict | IterableDatasetDict,
+) -> DatasetDict | IterableDatasetDict:
     """Runs some preprocessing over the given dataset.
 
     Parameters:
@@ -163,18 +175,23 @@ def is_audio_in_length_range(length: int):
 
     with job.training_args.main_process_first(desc="dataset map preprocessing"):
         worker_count = job.data_args.preprocessing_num_workers
+
+        kwargs = {}
+        if not job.data_args.stream_dataset:
+            kwargs = {
+                "num_proc": worker_count,
+                "desc": "Dataset Preprocessing",
+            }
+
         dataset = dataset.map(
             _prepare_dataset,
             remove_columns=next(iter(dataset.values())).column_names,
-            num_proc=worker_count,
-            desc="preprocess datasets",
+            **kwargs,
         )
 
         # filter data that is shorter than min_input_length
         dataset = dataset.filter(
-            is_audio_in_length_range,
-            num_proc=worker_count,
-            input_columns=["input_length"],
+            is_audio_in_length_range, input_columns=["input_length"], **kwargs
         )
 
     logger.info(f"Test encoding labels: {dataset['train'][0]['labels']}")
@@ -182,20 +199,29 @@ def is_audio_in_length_range(length: int):
     return dataset
 
 
-def constrain_to_max_samples(job: Job, dataset: DatasetDict) -> DatasetDict:
+def constrain_to_max_samples(
+    job: Job, dataset: DatasetDict | IterableDatasetDict
+) -> DatasetDict | IterableDatasetDict:
     max_train_samples = job.data_args.max_train_samples
     max_eval_samples = job.data_args.max_eval_samples
 
+    def take(n: int, dataset: Dataset | IterableDataset) -> Dataset | IterableDataset:
+        if job.data_args.stream_dataset:
+            return dataset.take(n)  # type: ignore
+        return dataset.select(range(n))  # type: ignore
+
     if job.training_args.do_train and max_train_samples is not None:
-        dataset["train"] = dataset["train"].select(range(max_train_samples))
+        dataset["train"] = take(max_train_samples, dataset["train"])  # type: ignore
 
     if job.training_args.do_eval and max_eval_samples is not None:
-        dataset["eval"] = dataset["eval"].select(range(max_eval_samples))
+        dataset["eval"] = take(max_eval_samples, dataset["eval"])  # type: ignore
 
     return dataset
 
 
-def clean_dataset(job: Job, dataset: DatasetDict) -> DatasetDict:
+def clean_dataset(
+    job: Job, dataset: DatasetDict | IterableDatasetDict
+) -> DatasetDict | IterableDatasetDict:
     if not job.data_args.do_clean:
         return dataset
 
@@ -219,9 +245,6 @@ def clean(batch: Dict[str, Any]):
         return batch
 
     with job.training_args.main_process_first(desc="Dataset cleaning."):
-        dataset = dataset.map(
-            clean,
-            desc="Cleaning the dataset and standardizing case.",
-        )
+        dataset = dataset.map(clean)
 
     return dataset
diff --git a/elpis/models/job.py b/elpis/models/job.py
index 9b99bb3..e8d7883 100644
--- a/elpis/models/job.py
+++ b/elpis/models/job.py
@@ -133,6 +133,12 @@ class DataArguments:
             "help": "The configuration name of the dataset to use (via the datasets library)."
         },
     )
+    stream_dataset: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to stream the dataset as opposed to downloading it all at once."
+        },
+    )
     train_split_name: str = field(
         default="train+validation",
         metadata={
diff --git a/elpis/models/vocab.py b/elpis/models/vocab.py
index 581bb09..61090bc 100644
--- a/elpis/models/vocab.py
+++ b/elpis/models/vocab.py
@@ -4,6 +4,8 @@
 from pathlib import Path
 from typing import Any, Dict, Iterable, Optional, Set
 
+from datasets import DatasetDict
+
 VOCAB_FILE = "vocab.json"
 
 
diff --git a/elpis/trainer/trainer.py b/elpis/trainer/trainer.py
index a485500..c5f2690 100644
--- a/elpis/trainer/trainer.py
+++ b/elpis/trainer/trainer.py
@@ -1,9 +1,10 @@
 import warnings
 from contextlib import nullcontext
+from functools import partial, reduce
 from pathlib import Path
-from typing import Optional
+from typing import Any, Iterable, Optional
 
-from datasets import DatasetDict
+from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
 from loguru import logger
 from transformers import (
     AutoConfig,
@@ -125,7 +126,7 @@ def create_config(job: Job) -> AutoConfig:
 
 
 def create_tokenizer(
-    job: Job, config: AutoConfig, dataset: DatasetDict
+    job: Job, config: AutoConfig, dataset: DatasetDict | IterableDatasetDict
 ) -> AutoTokenizer:
     tokenizer_name_or_path = job.model_args.tokenizer_name_or_path
     if tokenizer_name_or_path is not None:
@@ -135,39 +136,6 @@ def create_tokenizer(
             trust_remote_code=job.data_args.trust_remote_code,
         )
 
-    training_args = job.training_args
-
-    # save vocab in training output dir
-    tokenizer_name_or_path = job.training_args.output_dir
-    vocab_file = Path(tokenizer_name_or_path) / VOCAB_FILE
-
-    # Delete existing vocab file if overwriting
-    with training_args.main_process_first():
-        if training_args.overwrite_output_dir and vocab_file.is_file():
-            try:
-                vocab_file.unlink()
-            except OSError:
-                # in shared file-systems it might be the case that
-                # two processes try to delete the vocab file at the some time
-                pass
-
-    # Build up a vocab from the dataset.
-    with training_args.main_process_first(desc="dataset map vocabulary creation"):
-        if not vocab_file.is_file():
-            Path(tokenizer_name_or_path).mkdir(exist_ok=True, parents=True)
-            text_column = job.data_args.text_column_name
-
-            vocab = Vocab.from_strings(dataset["train"][text_column])
-            if "test" in dataset:
-                test_vocab = Vocab.from_strings(dataset["test"][text_column])
-                vocab = vocab.merge(test_vocab)
-
-            vocab.add(job.data_args.unk_token)
-            vocab.add(job.data_args.pad_token)
-            vocab.replace(" ", job.data_args.word_delimiter_token)
-            logger.info(f"Vocab: {vocab.vocab}")
-            vocab.save(vocab_file)
-
     # If the tokenizer has just been created,
     # it is defined by `tokenizer_class` if present in config else by `model_type`
     tokenizer_kwargs = {
@@ -179,14 +147,88 @@ def create_tokenizer(
         "do_lower_case": job.data_args.do_lower_case,
     }
 
+    tokenizer_folder = Path(job.training_args.output_dir)
+    create_vocab(job, dataset)
+
     return AutoTokenizer.from_pretrained(
-        tokenizer_name_or_path,
+        str(tokenizer_folder),
         token=job.data_args.token,
         trust_remote_code=job.data_args.trust_remote_code,
         **tokenizer_kwargs,
     )
 
 
+def create_vocab(job: Job, dataset: DatasetDict | IterableDatasetDict) -> None:
+    """Effectful- creates a vocabulary.json file in the model output dir, for use in tokenization.
+
+    Assumes that we are training our own tokenizer from scratch rather than
+    using a pretrained one.
+
+    Parameters:
+        job: The job containing info about the required training tasks.
+        dataset: The dataset dictionary to create the vocabulary from.
+    """
+    training_args = job.training_args
+
+    # save vocab in training output dir
+    tokenizer_folder = Path(job.training_args.output_dir)
+    tokenizer_folder.mkdir(exist_ok=True, parents=True)
+
+    vocab_file = tokenizer_folder / VOCAB_FILE
+
+    # Delete existing vocab file if we are overwriting
+    with training_args.main_process_first():
+        if training_args.overwrite_output_dir and vocab_file.is_file():
+            try:
+                vocab_file.unlink()
+            except OSError:
+                # in shared file-systems it might be the case that
+                # two processes try to delete the vocab file at the some time
+                pass
+
+    if vocab_file.is_file():
+        return
+
+    # Build up a vocab from the dataset.
+    with training_args.main_process_first(desc="Dataset Vocabulary Creation"):
+
+        def extract_all_chars(text: list[str]):
+            all_text = " ".join(text)
+            vocab = list(set(all_text))
+            return {"vocab": [vocab]}
+
+        vocab_datasets = dataset.map(
+            extract_all_chars,
+            input_columns=job.data_args.text_column_name,
+            batched=True,
+            batch_size=-1,
+            # keep_in_memory=True,
+            # num_proc=job.data_args.preprocessing_num_workers,
+            remove_columns=dataset["train"].column_names,
+        )
+        logger.info(dataset)
+        logger.info(f"Values: {vocab_datasets.values()}")
+
+        def create_vocab_from_batches(batches: Iterable[dict[str, Any]]) -> Vocab:
+            vocabs = map(lambda batch: "".join(batch["vocab"][0]), batches)
+            return Vocab.from_strings(vocabs)
+
+        if job.data_args.stream_dataset:
+            splits: Iterable[IterableDataset] = vocab_datasets.values()
+            vocabs = map(create_vocab_from_batches, splits)
+            vocab = reduce(Vocab.merge, vocabs, Vocab({}))
+        else:
+            # Note: in this case the values() are datasets, which are treated 
+            # as if they were batches. 
+            vocab = create_vocab_from_batches(vocab_datasets.values())
+
+        vocab.add(job.data_args.unk_token)
+        vocab.add(job.data_args.pad_token)
+        vocab.replace(" ", job.data_args.word_delimiter_token)
+        logger.info(f"Created Vocab: {vocab}")
+        vocab.save(tokenizer_folder)
+
+
 def update_config(job: Job, config: AutoConfig, tokenizer: AutoTokenizer) -> None:
     config.update(  # type: ignore
         {
diff --git a/hf_example.py b/hf_example.py
new file mode 100644
index 0000000..0e7356a
--- /dev/null
+++ b/hf_example.py
@@ -0,0 +1,86 @@
+from pathlib import Path
+
+from transformers import TrainingArguments
+
+from elpis.models.job import DataArguments, Job, ModelArguments
+from elpis.trainer.trainer import run_job
+from elpis.transcriber.results import build_elan, build_text
+from elpis.transcriber.transcribe import build_pipeline, transcribe
+
+DATASETS_PATH = Path(__file__).parent.parent.parent / "datasets"
+DIGITS_PATH = DATASETS_PATH / "digits-preview"
+TRANSCRIBE_AUDIO = DIGITS_PATH / "test/audio2.wav"
+
+print("------ Training files ------")
+# Setup
+tmp_path = Path("testdir")
+# tmp_path = Path("/tmp") / "testscript"
+model_dir = tmp_path / "model"
+output_dir = tmp_path / "output"
+
+# Make all directories
+for directory in model_dir, output_dir:
+    directory.mkdir(exist_ok=True, parents=True)
+
+# Train the model
+job = Job(
+    model_args=ModelArguments(
+        "facebook/wav2vec2-base",
+        attention_dropout=0.1,
+        layerdrop=0.0,
+        freeze_feature_encoder=True,
+    ),
+    data_args=DataArguments(
+        dataset_name_or_path="mozilla-foundation/common_voice_11_0",
+        dataset_config_name="gn",
+        train_split_name="train",
+        text_column_name="sentence",
+        audio_column_name="audio",
+        min_duration_in_seconds=1,
+        max_duration_in_seconds=20,
+        max_train_samples=100,
+        do_clean=True,
+        # stream_dataset=True,
+    ),
+    training_args=TrainingArguments(
+        output_dir=str(model_dir),
+        overwrite_output_dir=True,
+        do_train=True,
+        do_eval=False,
+        per_device_train_batch_size=16,
+        per_device_eval_batch_size=1,
+        num_train_epochs=20,
+        learning_rate=1e-4,
+        group_by_length=True,
+        logging_steps=10,
+        save_total_limit=2,
+    ),
+)
+
+
+print("------ JOB ------")
+print(job)
+run_job(job)
+print("------ TRAINED ------")
+
+# Perform inference with pipeline
+print("------ INFER ------")
+asr = build_pipeline(
+    pretrained_location=str(model_dir.absolute()),
+)
+
+annotations = transcribe(TRANSCRIBE_AUDIO, asr)
+print(build_text(annotations))
+
+# Build output files
+print("------ OUTPUT ------")
+text_file = output_dir / "test.txt"
+
+with open(text_file, "w") as output_file:
+    output_file.write(build_text(annotations))
+
+elan_file = output_dir / "test.eaf"
+eaf = build_elan(annotations)
+eaf.to_file(str(elan_file))
+
+print("voila ;)")

From b8d49bb2f7f9b9461bc90abba152a713edb5a675 Mon Sep 17 00:00:00 2001
From: Harry Keightley <harrykeightley@outlook.com>
Date: Tue, 17 Oct 2023 17:31:56 +1000
Subject: [PATCH 8/8] Black formatting

---
 elpis/trainer/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/elpis/trainer/trainer.py b/elpis/trainer/trainer.py
index c5f2690..aafd8e1 100644
--- a/elpis/trainer/trainer.py
+++ b/elpis/trainer/trainer.py
@@ -218,8 +218,8 @@ def create_vocab_from_batches(batches: Iterable[dict[str, Any]]) -> Vocab:
             vocabs = map(create_vocab_from_batches, splits)
             vocab = reduce(Vocab.merge, vocabs, Vocab({}))
         else:
-            # Note: in this case the values() are datasets, which are treated 
-            # as if they were batches. 
+            # Note: in this case the values() are datasets, which are treated
+            # as if they were batches.
             vocab = create_vocab_from_batches(vocab_datasets.values())
 
         vocab.add(job.data_args.unk_token)