diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 0000000..5afb6fa
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,64 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
+// https://github.com/microsoft/vscode-dev-containers/tree/v0.245.0/containers/docker-existing-dockerfile
+{
+	"name": "Existing Dockerfile",
+
+	"build": {
+		"dockerfile": "../Dockerfile",
+		// Update 'VARIANT' to pick a Node version: 16, 14, 12.
+		// Append -bullseye or -buster to pin to an OS version.
+		// Use -bullseye variants on local arm64/Apple Silicon.
+		"args": { "VERSION": "00001122334455_0123abc" }
+	},
+
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+
+	// Uncomment the next line to run commands after the container is created - for example installing curl.
+	// "postCreateCommand": "apt-get update && apt-get install -y curl",
+
+	// Uncomment when using a ptrace-based debugger like C++, Go, and Rust
+	// "runArgs": [ "-e BASE_PATH=/"],
+
+	"containerEnv": {},
+
+	// "workspaceMount": "source=${localWorkspaceFolder},target=/app,type=bind",
+    // "workspaceFolder": "/app",
+
+	// Uncomment to use the Docker CLI from inside the container. See https://aka.ms/vscode-remote/samples/docker-from-docker.
+	"mounts": [
+        //"source=${localWorkspaceFolder}/src,target=/app/src,type=bind",
+		    // "source=${localWorkspaceFolder}/tests,target=/app/tests,type=bind",
+        "source=/mnt/availae_results,target=/mnt/availae_results,type=bind",
+		    "source=/mnt/c/Users/Administrator/Documents/phil,target=/phil,type=bind",
+        "source=/mnt/c/Users/Administrator/Documents/phil,target=/phil,type=bind",
+        "source=/mnt/c/Users/Administrator/Documents/phil/output,target=/output,type=bind"
+	],
+
+	//"workspaceMount": "source=vscode-extensions,target=/vscode/extensions,type=volume",
+
+	// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
+	"remoteUser": "appuser",
+
+	// Configure tool-specific properties.
+	"customizations": {
+		// Configure properties specific to VS Code.
+		"vscode": {
+			"settings": {
+				"python.testing.pytestArgs": [
+					"tests/app_tests"
+				]
+			},
+			"extensions": [
+				"ms-python.python",
+				"ms-python.pylint"
+			]
+		}
+	},
+	"settings": {
+		"python.testing.pytestArgs": [
+			"tests/app_tests"
+		]
+	}
+
+}
\ No newline at end of file
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
new file mode 100644
index 0000000..ef05008
--- /dev/null
+++ b/.github/workflows/build.yml
@@ -0,0 +1,66 @@
+# $schema: https://json.schemastore.org/github-action.json
+name: build
+
+on:
+  push:
+    branches:
+      - "*"
+    paths-ignore:
+      - "**/*.md"
+  pull_request:
+    paths-ignore:
+      - "**/*.md"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: nschloe/action-cached-lfs-checkout@v1
+
+      - uses: docker/setup-buildx-action@v2
+        id: buildx
+        with:
+          install: true
+
+
+      - name: Build image
+        # run build script, get the version from the last line of output, then save it to the env var for use in subsequent steps
+        # echo "CRANE_VERSION=$(sh ./build.sh | tail -n1)" >> $GITHUB_ENV
+        run: |
+          source ./build.sh
+          echo "PR_VERSION=$PR_VERSION" >> $GITHUB_ENV
+
+      # reading and writing to mounted directories doesn't work unless permissions are given to 'other'
+      - name: permissions for running tests with mounts
+        run: |
+          find ./tests -type d -exec chmod 777 {} +
+          find ./tests -type f -exec chmod 644 {} +
+
+      - name: Test from container
+        run: ./scripts/run_tests.sh
+
+      # set up python so we can run pytest to test invoking the container from the host
+      # - name: Set up Python 3.9
+      #   uses: actions/setup-python@v4
+      #   with:
+      #     python-version: 3.9
+
+      # - name: Test from Host
+      #   run: |
+      #     python -m pip install --upgrade pip
+      #     pip install pytest 
+      #     pytest ./src/tests/end_to_end
+        
+      - name: Login to Docker Hub
+        if: (github.ref == 'refs/heads/main' || contains(github.ref, 'refs/tag'))
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKER_USER }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: push to docker
+        if: (github.ref == 'refs/heads/main' || contains(github.ref, 'refs/tag'))
+        run: |
+          docker push qutecoacoustics/perchrunner:${{ env.PR_VERSION }}
+          docker push qutecoacoustics/perchrunner:latest
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..afba73e
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,12 @@
+__pycache__
+
+tests/output/*
+
+!test/output/.gitkeep
+
+local_scripts/*
+
+*.ipynb_checkpoints/
+*.ipynb_checkpoints/*
+
+.vscode/launch.local.json
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..082c09f
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "tests/app_tests"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..1db1201
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,60 @@
+FROM --platform=linux/amd64 python:3.10-bookworm as perch_runner_dev
+
+RUN apt update 
+RUN apt install -y libsndfile1 ffmpeg
+
+# download and install poetry
+# consider changing to pip install 'poetry==$POETRY_VERSION'
+# see https://stackoverflow.com/questions/53835198/integrating-python-poetry-with-docker
+# RUN curl -sSL https://install.python-poetry.org | python3 -
+
+RUN mkdir /app && mkdir /app/src
+WORKDIR /app
+
+
+# we use our own pyproject file modified from the perch on
+# because we have extra dev dependencies, and also we remove some unecessary
+# deps from the basic perch
+COPY ./pyproject.toml /app
+
+# install perch dependencies (not in venv since we are using docker)
+# ENV PATH="/root/.local/bin:$PATH"
+# RUN poetry config virtualenvs.create false --local
+# RUN /root/.local/bin/poetry install
+# # this is due to "connection pool is full" error
+# # https://stackoverflow.com/questions/74385209/poetry-install-throws-connection-pool-is-full-discarding-connection-pypi-org
+# RUN poetry config installer.max-workers 10
+# RUN poetry install --no-interaction --no-ansi -vvv
+
+# install perch_runner dependencies
+
+# RUN pip install git+https://github.com/google-research/perch.git@8cc4468afaac730e77d84ac447f0874f09d10a25
+RUN pip install git+https://github.com/google-research/perch.git@3746672d406c6cfe48acb0e725248cea05f57445
+
+# there seems to be a problem with the way flax interacts with jaxlib 
+# this is a temporary fix to see if it helps without introducing other problems
+# RUN pip install --upgrade flax jax jaxlib
+
+#COPY --from=perch_runner_build /app/requirements.txt /app
+
+# RUN pip install .
+# RUN pip install .[dev]
+
+COPY ./src /app/src
+
+# this is the trained linear models
+COPY ./models /models
+
+# this is the embedding model
+RUN python /app/src/download_model.py --version 4 --destination /models
+
+# ENV PYTHONPATH "${PYTHONPATH}:/app/perch-main"
+
+#COPY ./tests /app/tests
+
+RUN pip install librosa
+RUN pip install numpy 
+RUN pip install pytest pytest-mock
+
+RUN useradd -u 1000 -ms /bin/bash appuser
+
diff --git a/Dockerfile copy b/Dockerfile copy
new file mode 100644
index 0000000..e56dc54
--- /dev/null
+++ b/Dockerfile copy	
@@ -0,0 +1,41 @@
+FROM --platform=linux/amd64 python:3.10-bookworm as perch_runner
+
+RUN apt update 
+RUN apt install -y libsndfile1 ffmpeg
+
+# download and install poetry
+# consider changing to pip install 'poetry==$POETRY_VERSION'
+# see https://stackoverflow.com/questions/53835198/integrating-python-poetry-with-docker
+RUN curl -sSL https://install.python-poetry.org | python3 -
+
+RUN mkdir /app && mkdir /app/src
+WORKDIR /app
+
+# download and unzip Perch
+ARG perch_repo=https://github.com/google-research/chirp/archive/refs/heads/main.zip
+RUN wget $perch_repo && unzip main.zip && rm main.zip
+# WORKDIR /app/perch-main
+WORKDIR /app
+
+# we use our own pyproject file modified from the perch on
+# because we have extra dev dependencies, and also we remove some unecessary
+# deps from the basic perch
+COPY ./pyproject.toml /app
+
+# install perch dependencies (not in venv since we are using docker)
+ENV PATH="/root/.local/bin:$PATH"
+RUN poetry config virtualenvs.create false --local
+# RUN /root/.local/bin/poetry install
+# this is due to "connection pool is full" error
+# https://stackoverflow.com/questions/74385209/poetry-install-throws-connection-pool-is-full-discarding-connection-pypi-org
+RUN poetry config installer.max-workers 10
+RUN poetry install --no-interaction --no-ansi -vvv
+
+# install perch_runner dependencies
+
+COPY ./src /app/src
+
+RUN python /app/src/download_model.py --version 4 --destination /models
+
+ENV PYTHONPATH "${PYTHONPATH}:/app/perch-main"
+
diff --git a/Dockerfile_alpine b/Dockerfile_alpine
new file mode 100644
index 0000000..2359bc0
--- /dev/null
+++ b/Dockerfile_alpine
@@ -0,0 +1,60 @@
+FROM --platform=linux/amd64 python:3.10-alpine as perch_runner_dev
+
+# Install system dependencies
+RUN apk update && apk add --no-cache libsndfile ffmpeg git
+
+# download and install poetry
+# consider changing to pip install 'poetry==$POETRY_VERSION'
+# see https://stackoverflow.com/questions/53835198/integrating-python-poetry-with-docker
+# RUN curl -sSL https://install.python-poetry.org | python3 -
+
+RUN mkdir /app && mkdir /app/src
+WORKDIR /app
+
+
+# we use our own pyproject file modified from the perch on
+# because we have extra dev dependencies, and also we remove some unecessary
+# deps from the basic perch
+COPY ./pyproject.toml /app
+
+# install perch dependencies (not in venv since we are using docker)
+# ENV PATH="/root/.local/bin:$PATH"
+# RUN poetry config virtualenvs.create false --local
+# RUN /root/.local/bin/poetry install
+# # this is due to "connection pool is full" error
+# # https://stackoverflow.com/questions/74385209/poetry-install-throws-connection-pool-is-full-discarding-connection-pypi-org
+# RUN poetry config installer.max-workers 10
+# RUN poetry install --no-interaction --no-ansi -vvv
+
+# install perch_runner dependencies
+
+# RUN pip install git+https://github.com/google-research/perch.git@8cc4468afaac730e77d84ac447f0874f09d10a25
+RUN pip install git+https://github.com/google-research/perch.git@3746672d406c6cfe48acb0e725248cea05f57445
+
+# there seems to be a problem with the way flax interacts with jaxlib 
+# this is a temporary fix to see if it helps without introducing other problems
+# RUN pip install --upgrade flax jax jaxlib
+
+#COPY --from=perch_runner_build /app/requirements.txt /app
+
+# RUN pip install .
+# RUN pip install .[dev]
+
+COPY ./src /app/src
+
+# this is the trained linear models
+COPY ./models /models
+
+# this is the embedding model
+RUN python /app/src/download_model.py --version 4 --destination /models
+
+# ENV PYTHONPATH "${PYTHONPATH}:/app/perch-main"
+
+#COPY ./tests /app/tests
+
+RUN pip install librosa
+RUN pip install numpy 
+RUN pip install pytest pytest-mock
+
+RUN useradd -u 1000 -ms /bin/bash appuser
+
diff --git a/Docs.md b/Docs.md
new file mode 100644
index 0000000..a2231a8
--- /dev/null
+++ b/Docs.md
@@ -0,0 +1,25 @@
+This document contains instructions to run one of the following recognizers over unlabelled audio
+- pw (Plains Wanderer)
+- cgw (Carpentarian Grass Wren)
+
+# Setup
+
+1. Prepare the paths to the relevant folders 
+
+  You will need the following paths on your computer
+  1. A folder with audio recordings to analyse (input files)
+  2. A writable folder where we can store temporary files
+  3. A writeable folder where we can save the results for each input file
+
+2. Install docker if you don't have it.
+
+You will need **Docker** installed on your computer
+
+To install docker, please see `https://docs.docker.com/engine/install/`. Docker is software that can run a docker *container*. The recognizer (i.e. the trained model and the scripts needed to run it) and its dependencies are all bundled into this container. 
+
+
+# Running the recognizer
+
+Then, open a terminal shell and enter the following command
+
+./Scripts/run_inference.sh 
diff --git a/README.md b/README.md
index 71a173e..8a03252 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,146 @@
-# A docker container to train linear models from chirp embeddings and to run inference using this model on embeddings of long files
+# perch-runner
+
+A docker container that runs Perch.
+
+## Commands
+
+This container has three basic functions:
+
+1. Generate Perch embeddings for a given audio file
+2. To classify the embeddings given an audio file and one or more additional linear classifier
+3. To measure distance between a given sample and a given audio file (Not implemented Yet)
+
+Each of these output a result per 5 second chunks
+
+### Generate embeddings
+
+The `generate` command accepts one input file or one input folder and one output directory.
+
+- One file will be produced containing the embeddings for the input file.
+- The file MUST be in a self-describing format. 
+- Each result in the file must have at least
+  - a `start` value representing the start of the record in seconds
+  - an `embedding` value representing the embedding for the record
+    - For CSV this should be a Base64 encoded string of the embedding vector
+  - a `source_separation_channel` representing the virtual channel the record came from
+  - and other columns are allowed as needed
+- Files should include provenance metadata in their headers
+- Note: audio recordings IDs (or other source identifying information) are
+  not needed. The assumption is that this container is run by an orchestrator
+  that knows what audio file is being processed.
+
+#### Usage
+
+```
+generate [-F|--format] [-c|--config <config-file>] <input-file> <output-directory>
+    <input-file>                The file to generate embeddings for
+    <output-directory>          The directory to write the embeddings to
+    -F|--format [csv|parquet]   The format of the embeddings file: `CSV`` or `Parquet``
+    -c|--config <config-file>   An optional configuration file to use
+```
+
+#### Configuration file
+
+```yaml
+hop_length_seconds: 5.0
+```
+
+#### Examples
+
+```bash
+cd /data
+curl -o audio.wav https://api.ecosounds.org/audio_recordings/123.wav
+docker run -v /data:/data perch-runner generate /data/audio.wav /data/output
+```
+
+### Classify
+
+The `classify` command accepts one input file, one output directory, and one or more classifiers.
+The classifiers are provided in a config file.
+
+- One file will be produced containing the classifications for the input file.
+- The file MUST be in a self-describing format.
+- The file should return results in \<insert link to our event common format\>
+- Files should include provenance metadata in their headers
+
+#### Usage
+
+```
+classify [-F|--format] [-c|--config <config-file> ] <input-file> <output-directory>
+    <input-file>                The file to generate embeddings for
+    <output-directory>          The directory to write the embeddings to
+    -c|--config <config-file>   An optional configuration file to use
+```
+
+#### Configuration file
+
+```yaml
+hop_length_seconds: 5.0
+classifiers:
+  - name: "classifier1"
+    tags: ["tag1", "tag2"]
+    # base64 encoded model
+    model: "asd321jhfsdhk438asdmlfas89i3mnlksf..."
+  - name: "classifier2"
+    tags: ["tag3", "tag4"]
+     # base64 encoded model
+    model: "..."
+
+```
+
+#### Example
+
+```bash 
+cd /data
+curl -o audio.wav https://api.ecosounds.org/audio_recordings/123.wav
+docker run -v /data:/data perch-runner classify -c /data/config.yml /data/audio.wav /data/output
+```
+
+### Distance
+
+The `distance` command accepts one input file, one output directory, and a query file.
+
+- The query file must be an audio file that is 5 seconds long.
+- The output of this command is the same as the output of the `generate` command,
+    but with an additional column `distance` that represents the distance between the
+    query and the sample.
+
+#### Usage
+
+```
+distance [-F|--format] [-c|--config <config-file> ] <input-file> <query-file> <output-directory> 
+    <input-file>                The file to generate embeddings for
+    <output-directory>          The directory to write the embeddings to
+    <query-file>                The file to use as the query
+    -c|--config <config-file>   An optional configuration file to use
+```
+
+#### Configuration file
+
+```yaml
+hop_length_seconds: 5.0
+```
+
+#### Example
+
+```bash
+cd /data
+curl -o audio.wav https://api.ecosounds.org/audio_recordings/123.wav
+curl -o query.wav https://api.ecosounds.org/audio_recordings/456/media.wav?start=10&end=15
+docker run -v /data:/data perch-runner distance -c /data/config.yml /data/audio.wav /data/query.wav /data/output
+```
+
+
+## Batch Mode
+
+The "generate" and "inference" commands can be also be used in batch mode by altering the entrypoint to `/app/src/batch.py`
+
+
+# Tests
+
+## Host tests
+
+To test the docker container works as expected from the host:
+1. Into a virtual environment install the testing dependencies with `pip install -r requirements-host.txt`
+2. Run `pytest 
 
diff --git a/README_original.md b/README_original.md
new file mode 100644
index 0000000..94df706
--- /dev/null
+++ b/README_original.md
@@ -0,0 +1,147 @@
+# perch-runner
+
+A docker container that runs Perch.
+
+## Commands
+
+This container has three basic functions:
+
+1. Generate Perch embeddings for a given audio file
+2. To classify the embeddings given an audio file and one or more additional linear classifier
+3. To measure distance between a given sample and a given audio file (Not implemented Yet)
+
+
+All three commands output results in 5 second chunks.
+
+### Generate embeddings
+
+The `generate` command accepts one input file and one output directory.
+
+- One file will be produced containing the embeddings for the input file.
+- The file MUST be in a self-describing format.
+- Each result in the file must have at least
+  - a `start` value representing the start of the record in seconds
+  - an `embedding` value representing the embedding for the record
+    - For CSV this should be a Base64 encoded string of the embedding vector
+  - a `source_separation_channel` representing the virtual channel the record came from
+  - and other columns are allowed as needed
+- Files should include provenance metadata in their headers
+- Note: audio recordings IDs (or other source identifying information) are
+  not needed. The assumption is that this container is run by an orchestrator
+  that knows what audio file is being processed.
+
+#### Usage
+
+```
+generate [-F|--format] [-c|--config <config-file>] <input-file> <output-directory>
+    <input-file>                The file to generate embeddings for
+    <output-directory>          The directory to write the embeddings to
+    -F|--format [csv|parquet]   The format of the embeddings file: `CSV`` or `Parquet``
+    -c|--config <config-file>   An optional configuration file to use
+```
+
+#### Configuration file
+
+```yaml
+hop_length_seconds: 5.0
+```
+
+#### Examples
+
+```bash
+cd /data
+curl -o audio.wav https://api.ecosounds.org/audio_recordings/123.wav
+docker run -v /data:/data perch-runner generate /data/audio.wav /data/output
+```
+
+### Classify
+
+The `classify` command accepts one input file, one output directory, and one or more classifiers.
+The classifiers are provided in a config file.
+
+- One file will be produced containing the classifications for the input file.
+- The file MUST be in a self-describing format.
+- The file should return results in \<insert link to our event common format\>
+- Files should include provenance metadata in their headers
+
+#### Usage
+
+```
+classify [-F|--format] [-c|--config <config-file> ] <input-file> <output-directory>
+    <input-file>                The file to generate embeddings for
+    <output-directory>          The directory to write the embeddings to
+    -c|--config <config-file>   An optional configuration file to use
+```
+
+#### Configuration file
+
+```yaml
+hop_length_seconds: 5.0
+classifiers:
+  - name: "classifier1"
+    tags: ["tag1", "tag2"]
+    # base64 encoded model
+    model: "asd321jhfsdhk438asdmlfas89i3mnlksf..."
+  - name: "classifier2"
+    tags: ["tag3", "tag4"]
+     # base64 encoded model
+    model: "..."
+
+```
+
+#### Example
+
+```bash 
+cd /data
+curl -o audio.wav https://api.ecosounds.org/audio_recordings/123.wav
+docker run -v /data:/data perch-runner classify -c /data/config.yml /data/audio.wav /data/output
+```
+
+### Distance
+
+The `distance` command accepts one input file, one output directory, and a query file.
+
+- The query file must be an audio file that is 5 seconds long.
+- The output of this command is the same as the output of the `generate` command,
+    but with an additional column `distance` that represents the distance between the
+    query and the sample.
+
+#### Usage
+
+```
+distance [-F|--format] [-c|--config <config-file> ] <input-file> <query-file> <output-directory> 
+    <input-file>                The file to generate embeddings for
+    <output-directory>          The directory to write the embeddings to
+    <query-file>                The file to use as the query
+    -c|--config <config-file>   An optional configuration file to use
+```
+
+#### Configuration file
+
+```yaml
+hop_length_seconds: 5.0
+```
+
+#### Example
+
+```bash
+cd /data
+curl -o audio.wav https://api.ecosounds.org/audio_recordings/123.wav
+curl -o query.wav https://api.ecosounds.org/audio_recordings/456/media.wav?start=10&end=15
+docker run -v /data:/data perch-runner distance -c /data/config.yml /data/audio.wav /data/query.wav /data/output
+```
+
+
+## Batch Mode
+
+The "generate" and "inference" commands can be also be used in batch mode by altering the entrypoint to `/app/src/batch.py`
+
+
+# Tests
+
+## Host tests
+
+To test the docker container works as expected from the host:
+1. Into a virtual environment install the testing dependencies with `pip install -r requirements-host.txt`
+2. Run `pytest 
+
diff --git a/build.sh b/build.sh
new file mode 100755
index 0000000..155b770
--- /dev/null
+++ b/build.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+#set -x
+
+# crane version is the datetime and git hash
+# this variable name is required by the github CI
+PR_VERSION=$(date '+%Y%m%d%H%M%S')_$(git rev-parse --short HEAD)
+
+echo "building container with version $PR_VERSION"
+
+tag=qutecoacoustics/perchrunner
+
+# use buildx build rather than just build, so that the --load is an option
+# --load is required so that the image is saved to the local docker images when run on github CI
+
+docker buildx build \
+-t $tag:$PR_VERSION \
+-t $tag:latest \
+--load \
+--build-arg VERSION=$PR_VERSION \
+--progress=plain \
+.
\ No newline at end of file
diff --git a/models/pw/trained_model_pw_02.keras b/models/pw/trained_model_pw_02.keras
new file mode 100755
index 0000000..3cb4ada
Binary files /dev/null and b/models/pw/trained_model_pw_02.keras differ
diff --git a/models/pw/trained_model_pw_02.keras.labels.json b/models/pw/trained_model_pw_02.keras.labels.json
new file mode 100755
index 0000000..178d04f
--- /dev/null
+++ b/models/pw/trained_model_pw_02.keras.labels.json
@@ -0,0 +1 @@
+["neg", "pos"]
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..3826b9f
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,6794 @@
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+
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+
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..7890efc
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,21 @@
+[tool.poetry]
+name = "perch_runner"
+version = "0.1.0"
+description = "Scripts to use perch basic functionality"
+authors = ["Phil Eichinski <philip.eichinski@qut.edu.au>"]
+license = "Apache 2.0"
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+chirp = {git = "https://github.com/google-research/perch.git", rev = "3746672d406c6cfe48acb0e725248cea05f57445"}
+librosa = "^0.10.1"
+numpy = "^1.24.0"
+
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.0"
+pytest-mock = "^3.12.0"
+
+[build-system]
+requires = ["poetry-core>=1.5.0"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/pyproject_bak.toml b/pyproject_bak.toml
new file mode 100644
index 0000000..b446a5d
--- /dev/null
+++ b/pyproject_bak.toml
@@ -0,0 +1,41 @@
+[tool.poetry]
+name = "perch_runner"
+version = "0.1.0"
+description = "Scripts to use perch basic functionality"
+authors = ["Phil Eichinski <philip.eichinski@qut.edu.au>"]
+license = "Apache 2.0"
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+absl-py = "^1.4.0"
+apache-beam = {version = "^2.50.0", extras = ["gcp"]}
+clu = "^0.0.9"
+flax = "^0.7.4"
+jax = "^0.4.16"
+ml-collections = "^0.1.1"
+optax = "^0.1.7"
+# Note that tensorflow and tensorflow-io versions must be synchronized.
+# See: https://github.com/tensorflow/io#tensorflow-version-compatibility
+tensorflow = "=2.13.0"
+tensorflow-io = "=0.33"
+tensorflow-datasets = {version = "^4.9.3", extras = ["dev"]}
+pandas = {version = "^2.1.1", extras=["gcp"]}
+etils = {version = "^1.5.0", extras = ["epath"]}
+ratelimiter = "^1.2.0.post0"
+SPARQLWrapper = "^2.0.0"
+scenic = {git = "https://github.com/google-research/scenic.git"}
+tensorflow-hub = "^0.14.0"
+imageio = "^2.5.0"
+matplotlib = "^3.6.1"
+notebook = "^7.0.4"
+chex = "^0.1.7"
+numba = "^0.57"
+
+[tool.poetry.group.dev.dependencies]
+librosa = "^0.10.1"
+numpy = "^1.24.0"
+pytest = "^7.4.0"
+
+[build-system]
+requires = ["poetry-core>=1.5.0"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/requirements-host.txt b/requirements-host.txt
new file mode 100644
index 0000000..256bbc6
--- /dev/null
+++ b/requirements-host.txt
@@ -0,0 +1,4 @@
+# When running this app in the docker container, we can test it from the host by running the scripts that launch the 
+# container. This file contains the requirments to run the tests from the host that do this. 
+
+pytest
\ No newline at end of file
diff --git a/scripts/Readme.md b/scripts/Readme.md
new file mode 100644
index 0000000..c6165ca
--- /dev/null
+++ b/scripts/Readme.md
@@ -0,0 +1 @@
+This scripts can be run to launch the docker contain and run the app from the host in docker
\ No newline at end of file
diff --git a/scripts/classify.sh b/scripts/classify.sh
new file mode 100755
index 0000000..be004a3
--- /dev/null
+++ b/scripts/classify.sh
@@ -0,0 +1,67 @@
+#! /bin/bash
+
+# launches a docker container interactive with the necessary mounts for running inference on a folder of embeddings
+
+# Argument Parsing
+source="$1"
+output="$2"
+recognizer="$3"
+image="${4:-qutecoacoustics/perchrunner:latest}"
+
+# Required Parameter Validation
+if [[ -z "$source" || -z "$output" || -z "$recognizer" ]]; then
+    echo "Error: Missing required parameters (--source, --output, --recognizer)"
+    exit 1 
+fi
+
+# Required Parameter Validation
+if [[ -z "$source" || -z "$output" || -z "$recognizer" ]]; then
+    echo "Error: Missing required parameters (source, output, recognizer)"
+    exit 1 
+fi
+
+if [[ ! -e "$source" ]]; then
+    echo "Error: Source path does not exist: $source"
+    exit 1
+elif [[ -d "$source" && -z "$(ls -A "$source")" ]]; then
+    echo "Error: Source directory is empty: $source"
+    exit 1
+fi
+
+if [[ ! -d "$output" ]]; then
+    echo "Error: Output folder does not exist: $output"
+    exit 1
+fi
+
+# based on a recognizer name, get the name of the default config file for that recognizer
+declare -A recognizer_configs
+recognizer_configs["pw"]="pw.classify.yml"
+recognizer_configs["cgw"]="cgw.classify.yml"
+
+if [[ -n ${recognizer_configs[$recognizer]} ]]; then
+    echo "Using config file: ${recognizer_configs[$recognizer]}"
+else
+    echo "Recognizer $key not supported"
+    exit 1
+fi
+
+# paths to things inside the container, to be mounted
+embeddings_container="/mnt/embeddings"
+output_container="/mnt/output"
+output_dir=$output_container/search_results
+
+command="python /app/src/app.py classify --source_folder $embeddings_container --output_folder $output_container  --config_file ${recognizer_configs[$recognizer]}"
+
+#command="python /app/src/app.py --embeddings_dir $embeddings_container --model_path $model_path --output_dir $output_dir --skip_if_file_exists"
+
+
+
+ 
+echo "launching container with command: $command"
+
+set -x
+docker run --user appuser:appuser --rm \
+-v "$(pwd)/src":/app/src \
+-v "$source":$embeddings_container \
+-v "$output":$output_container $image $command
+set +x
diff --git a/scripts/embed.sh b/scripts/embed.sh
new file mode 100755
index 0000000..bd05ab1
--- /dev/null
+++ b/scripts/embed.sh
@@ -0,0 +1,49 @@
+#! /bin/bash
+
+# launches a docker container interactive with the necessary mounts for running inference on a folder of embeddings
+
+# Argument Parsing
+source="$1"
+output="$2"
+image="${3:-qutecoacoustics/perchrunner:latest}"
+
+# Required Parameter Validation
+if [[ -z "$source" || -z "$output" ]]; then
+    echo "Error: Missing required parameters (source, output)"
+    exit 1 
+fi
+
+echo $(pwd)
+echo $source
+
+# Source Path Checks
+if [[ ! -s "$source" ]]; then
+    echo "Error: Source audio folder does not exist: $source"
+    exit 1
+fi
+
+if [[ ! -s "$source" ]]; then
+    echo "Error: Source is empty: $source"
+    exit 1
+fi
+
+# paths to things inside the container, to be mounted
+source_container="/mnt/input"
+output_container="/mnt/output"
+
+source_folder_host=$(dirname "$source")
+source_basename=$(basename "$source")
+
+command="python /app/src/app.py generate --source_file $source_container/$source_basename --output_folder $output_container"
+ 
+echo "launching container with command: $command"
+
+set -x
+docker run --user appuser:appuser --rm \
+-v "$(pwd)/src":/app/src \
+-v "$source_folder_host":$source_container \
+-v "$output":$output_container $image $command
+set +x
+
+# add this in to mount the source directory to run changes without rebuilding
+# -v "$(pwd)/src":/app/src \
\ No newline at end of file
diff --git a/scripts/run_tests.sh b/scripts/run_tests.sh
new file mode 100755
index 0000000..034d3ae
--- /dev/null
+++ b/scripts/run_tests.sh
@@ -0,0 +1,13 @@
+#docker image list
+
+# tests are not included in image, but pytest is installed. 
+# To run tests in the container, we mount the test directory
+
+# run tests from within the container
+set -x
+docker run \
+-v $(pwd)/tests/:/app/tests \
+--entrypoint python \
+qutecoacoustics/perchrunner:latest -m pytest /app/tests/app_tests
+set +x
+
diff --git a/src/active_learning_from_parquet.ipynb b/src/active_learning_from_parquet.ipynb
new file mode 100644
index 0000000..b12a9ae
--- /dev/null
+++ b/src/active_learning_from_parquet.ipynb
@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-16 01:10:53.900207: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+      "2024-01-16 01:10:53.902301: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2024-01-16 01:10:53.944374: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2024-01-16 01:10:53.946322: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2024-01-16 01:10:54.943547: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import json\n",
+    "\n",
+    "from inference_parquet import process_embeddings\n",
+    "from train_linear_model_slim import train_and_save\n",
+    "from active_learning_from_parquet import plot_logits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "unlabelled_source = \"/mnt/input\"\n",
+    "labelled_source = \"/mnt/labelled\"\n",
+    "model_output_file = \"/mnt/output/pw_model.keras\"\n",
+    "embeddings_dir = \"/mnt/embeddings/\"\n",
+    "search_results_dir = \"/mnt/output/search_results\"\n",
+    "\n",
+    "skip_if_file_exists = True\n",
+    "embedding_model_version = 4\n",
+    "\n",
+    "print(Path(unlabelled_source).exists())\n",
+    "print(Path(labelled_source).exists())\n",
+    "print(Path(model_output_file).parent.exists())\n",
+    "print(Path(embeddings_dir).exists())\n",
+    "print(Path(search_results_dir).parent.exists())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "if skip_if_file_exists and Path(model_output_file).exists() and Path(model_output_file + '.labels.json').exists():\n",
+    "    classifier_model = model_output_file\n",
+    "    with open(model_output_file + '.labels.json', 'r') as file:\n",
+    "        labels = json.load(file)\n",
+    "else:\n",
+    "    classifier_model, labels = train_and_save(labelled_source, model_output_file, embedding_model_version)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true,
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "processing 17923 embeddings files\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing: 100%|██████████| 17923/17923 [1:55:14<00:00,  2.59it/s]  "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finished processing 17923 embeddings files\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "process_embeddings(embeddings_dir, (classifier_model, labels), search_results_dir, skip_if_file_exists)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plot_logits(inference_results, target_class = \"pos\", target_logit = None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "display_search_results(inference_results, labels, target_label='pos')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/active_learning_from_parquet.py b/src/active_learning_from_parquet.py
new file mode 100644
index 0000000..139883d
--- /dev/null
+++ b/src/active_learning_from_parquet.py
@@ -0,0 +1,115 @@
+import argparse
+import matplotlib.pyplot as plt
+import numpy as np
+from pathlib import Path
+import json
+import pandas as pd
+from dataclasses import dataclass
+
+from chirp.projects.bootstrap import display
+from chirp.projects.bootstrap.search import TopKSearchResults, SearchResult
+
+
+from inference_parquet import process_embeddings
+from train_linear_model_slim import train_and_save
+
+
+
+def active_learning(labelled_source, model_output_file, embedding_model_version, embeddings_dir, search_results_dir, skip_if_file_exists = False):
+
+    if skip_if_file_exists and Path(model_output_file).exists() and Path(model_output_file + '.labels.json').exists():
+        classifier_model = model_output_file
+        with open(model_output_file + '.labels.json', 'r') as file:
+            labels = json.load(file)
+    else:
+        classifier_model, labels = train_and_save(labelled_source, model_output_file, embedding_model_version)
+
+    process_embeddings(embeddings_dir, classifier_model, search_results_dir, labels, skip_if_file_exists)
+
+    display_search_results_from_folder(search_results_dir, tuple(labels) + ('unknown',))
+
+
+
+
+def plot_logits(inference_results, target_class, target_logit = None):
+
+    all_logits = inference_results[target_class].values
+    
+
+    # Plot the histogram of logits.
+    _, ys, _ = plt.hist(all_logits, bins=128, density=True)
+    plt.xlabel(f'{target_class} logit')
+    plt.ylabel('density')
+    # plt.yscale('log')
+    plt.plot([target_logit, target_logit], [0.0, np.max(ys)], 'r:')
+    plt.show()
+
+#@title Display results for the target label. { vertical-output: true }
+
+def display_search_results_from_folder(inference_dir, labels):
+
+    # reads all csvs in the given folder and concatenates them into a single dataframe
+    # then calls display_search_results with this dataframe
+    inference_results_df = pd.concat([pd.read_csv(file) for file in Path(inference_dir).rglob('*.csv')])
+    display_search_results(inference_results_df, tuple(labels) + ('unknown',))
+
+
+def display_search_results(results_df, labels, target_label='pos', sample_rate = 32000):
+    
+    
+    # class Result():
+    #     def __init__(self, **kwargs):
+    #         self.__dict__.update(kwargs)
+
+    # results = [Result(**row) for row in results_df.to_dict('records')]
+
+
+    results = TopKSearchResults([], top_k=20)
+
+    for row in results_df.to_dict('records'):
+        result = SearchResult(
+            filename=row['filename'],
+            timestamp_offset=row['offset_seconds'],
+            embedding=None,
+            score=row[target_label],
+            sort_score=row[target_label]
+        )
+        results.update(result)
+        
+        
+    display_labels = labels
+
+    source_map = {source: source for source in results_df['filename'].unique()}
+
+
+    extra_labels = []  #@param
+    for label in extra_labels:
+        if label not in labels:
+            display_labels += (label,)
+    if 'unknown' not in labels:
+        display_labels += ('unknown',)
+
+    display.display_search_results(
+        results, sample_rate,
+        source_map,
+        checkbox_labels=display_labels,
+        max_workers=5)
+    
+    print("done")
+
+
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--labelled_source", help="path to folder of folders")
+    parser.add_argument("--model_output_file", default='/output/trained_model.keras', help="where to save the model")
+    parser.add_argument("--embedding_model_version", default=4, help="path to embedding model")
+    parser.add_argument("--embeddings_dir", help="path to directory of embeddings files")
+    parser.add_argument("--search_results_dir", help="save the inference results here")
+    parser.add_argument("--skip_if_file_exists", default=0, type=int, help="if true, will not perform any step where the output file already exists")
+    args = parser.parse_args()
+    #config = config_dict.create(**vars(args))
+
+    active_learning(args.labelled_source, args.model_output_file, int(args.embedding_model_version), args.embeddings_dir, args.search_results_dir, bool(int(args.skip_if_file_exists)))
\ No newline at end of file
diff --git a/src/app.py b/src/app.py
new file mode 100644
index 0000000..3ee715d
--- /dev/null
+++ b/src/app.py
@@ -0,0 +1,61 @@
+#!/usr/local/bin/python
+
+"""
+Entrypoint for processing a single file
+"""
+
+import argparse
+from src.config import load_config
+from src.embed_audio_slim import embed_file_and_save, embed_folder
+from src.inference_parquet import classify_file_and_save, process_folder
+#import train_linear_model
+#import inference_slim
+
+def main():
+
+    print("start")
+
+    valid_commands = ('generate', 'train', 'classify')
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("command", choices=list(valid_commands), help=" | ".join(valid_commands))
+    parser.add_argument("--source_file", help="path to the file to analyze")
+    parser.add_argument("--source_folder", help="path to the a folder of files to analyze")
+    parser.add_argument("--config_file", default=None, help="path to the config file")
+    parser.add_argument("--output_folder", help="where to save the result file")
+    args = parser.parse_args()
+
+    if bool(args.source_file) == bool(args.source_folder):
+        parser.error('You must specify exactly one of --source_file or --source_folder, not both.')
+
+
+    config = load_config(args.config_file)
+
+    if args.source_file:
+
+        if args.command == "generate":
+            embed_file_and_save(args.source_file, args.output_folder, config)
+        elif args.command == "train":
+            parser.error('Incompatible args. Please specify --source_folder, not --source_file.')
+        elif args.command == "classify":
+            print(f"classify file {args.source_file} to {args.output_folder}")
+            classify_file_and_save(args.source_file, args.output_folder, config)
+        else:
+            print("invalid command")
+
+    else:
+
+        if args.command == "generate":
+            embed_folder(args.source_folder, args.output_folder, config)
+        elif args.command == "train":
+            # train_linear_model.train(args.source_file, config, args.output_folder)
+            print("train: not implemented yet")
+        elif args.command == "classify":
+            print(f"classify folder {args.source_folder} to {args.output_folder}")
+            process_folder(args.source_folder, args.output_folder, config)
+        else:
+            print("invalid command")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/batch.py b/src/batch.py
new file mode 100644
index 0000000..ef4fb4a
--- /dev/null
+++ b/src/batch.py
@@ -0,0 +1,88 @@
+#!/usr/local/bin/python
+
+"""
+Entrypoint for processing a csv of input and output files
+"""
+
+import argparse
+import yaml
+import csv
+from pathlib import Path
+
+from src.embed_audio_slim import embed_file_and_save
+from src.config import load_config
+#import train_linear_model
+#import inference_slim
+
+def read_items(source_csv, start_row, end_row):
+    """
+    given a path to a csv of items to analyze, and the rows to include, 
+    returns a list of dicts that has the source and output paths
+    """
+     
+    # Initialize an empty list to store the dictionaries
+    items = []
+
+    # Open the CSV file
+    with open(source_csv, mode='r', encoding='utf-8') as file:
+        # Create a csv.DictReader object
+        reader = csv.DictReader(file)
+
+        # Iterate over the CSV rows
+        for row in reader:
+            # Each row is a dictionary
+            items.append(row)
+
+    if start_row is None:
+        start_row = 0
+
+    if end_row is None:
+        end_row = len(items) - 1
+
+    # the +1 is because end_row is inclusive
+    items = items[start_row:(end_row + 1)]
+
+    return items
+
+
+def batch(command, source_csv, start_row, end_row, config_file, overwrite_existing=False):
+
+    items = read_items(source_csv, start_row, end_row)
+    config = load_config(config_file)
+
+    for item in items:
+
+        if Path(item['output']).exists() or overwrite_existing:
+            print(f"skipping {item['source']} because output already exists")
+        else:
+            print(f"processing {item['source']}")
+
+            match command:
+                case "generate":
+                    embed_file_and_save(item['source'], item['output'], config)
+                case "inference":
+                    # inference_slim.analze
+                    print("inference")
+                case _:
+                    print("invalid command")
+
+
+def main ():
+    """Just the arg parsing from command line"""
+    valid_commands = ('generate', 'inference')
+    parser = argparse.ArgumentParser()
+    parser.add_argument("command", choices=list(valid_commands), help=" | ".join(valid_commands))
+    parser.add_argument("--source_csv", help="path to a csv that has the columns 'source' and 'output'")
+    parser.add_argument("--start_row", default=None, help="which row on the csv to start from (zero index)")
+    parser.add_argument("--end_row", help="last row in the csv to process (zero index)")
+    parser.add_argument("--config_file", default=None, help="path to the config file")
+    parser.add_argument("--overwrite_existing", default=False, help="if true, will overwrite existing files, else will skip if exists")
+    args = parser.parse_args()
+    batch(args.command, args.source_csv, int(args.start_row), int(args.end_row), args.config_file, args.overwrite_existing)
+
+
+if __name__ == "__main__":
+    main()
+
+
+
diff --git a/src/config.py b/src/config.py
new file mode 100644
index 0000000..8407758
--- /dev/null
+++ b/src/config.py
@@ -0,0 +1,66 @@
+"""
+Parsing config files. 
+def load_config(yml_source):
+Only one config file can be specified to the public function load_config.
+This config file can inherit from other config files, if they have the key 'inherit' with the value of the path to that parent config
+"""
+
+import yaml
+from pathlib import Path
+from ml_collections import config_dict
+
+config_locations = ['', './src/default_configs', Path.cwd()]
+
+def resolve_source(source):
+
+    for loc in config_locations:
+        # print the contents of the directory loc
+        # print(f'checking {loc}:  {", ".join([str(entry.name) for entry in Path(loc).iterdir()])}')
+        joined = Path(loc) / Path(source)
+        if joined.exists():
+            return joined
+
+    raise FileNotFoundError(f'could not find {source} in {config_locations}. cwd is {Path.cwd()}')
+
+
+def parse_and_merge(yml_source, stack=None):
+    """
+    load a yml file, check if it is supposed to inherit from anything, and if so load and merge the parent
+    this is done recursively. stack will keep track of everything loaded to ensure there is no circular reference
+    """
+
+    yml_source = resolve_source(yml_source)
+
+    if stack is None:
+        stack = []
+    elif yml_source in stack:
+        raise ValueError(f'circular reference detected: {yml_source} in {stack}')
+    
+    with open(yml_source, 'r') as f:
+        config = yaml.safe_load(f)
+
+    # if inherit is inside the config, load an merge the parent
+    if 'inherit' in config:
+        parent_path = config.pop('inherit')
+        parent_config = parse_and_merge(parent_path, stack + [yml_source])
+        config = {**parent_config, **config}
+
+    return config
+
+
+def load_config(yml_source):
+
+    if yml_source is None:
+        config = {}
+    else:
+        config = parse_and_merge(yml_source)
+
+    print("loaded config:")
+    print(config)
+
+    return config_dict.create(**config)
+
+
+
+        
+
diff --git a/src/data_frames.py b/src/data_frames.py
new file mode 100644
index 0000000..5dd1949
--- /dev/null
+++ b/src/data_frames.py
@@ -0,0 +1,183 @@
+"""
+utilities for formatting embeddings into data frames, including multi-channel embeddings
+"""
+
+import pandas as pd
+import numpy as np
+import base64
+import struct
+
+def embeddings_to_df(embeddings) -> pd.DataFrame:
+    """
+    @param embeddings np.array; array of shape (n_segment, n_channels, n_features + 1), where dim 3 is the embeddings for a frame
+    plus the frame offset
+    @returns pd.DataFrame (n_segments * n_channels) rows and n_features + 2 columns, one col for each feature plus one for offset and one for channel 
+    """
+
+    df = pd.DataFrame()
+
+    for channel in range(embeddings.shape[1]):
+        array_2d = embeddings[:, channel, :]
+        channel_df = pd.DataFrame(array_2d)
+        channel_df.insert(0, 'channel', channel)
+        # concatenate channel_df to df
+        df = pd.concat([df, channel_df], ignore_index=True)
+
+    #rename the columns
+
+    new_columns = ['channel', 'offset'] + embedding_col_names(embeddings.shape[2] - 1)
+    df.columns = new_columns
+
+    return df
+
+
+def df_to_embeddings(df: pd.DataFrame) -> np.array:
+
+    """
+    @param df pd.DataFrame; DataFrame with (n_segments * n_channels) rows and n_features + 2 columns (including offset and channel)
+    @returns np.array; array of shape (n_segment, n_channels, n_features + 1) 
+    """
+
+    # Determine the number of channels and features
+    n_channels = df['channel'].nunique()
+    n_features = len(df.columns) - 2  # Subtracting the offset and channel columns
+
+    # Sort the DataFrame based on 'offset' and 'channel' to ensure correct ordering
+    df_sorted = df.sort_values(by=['offset', 'channel'])
+
+    # Drop the 'channel' column and pivot the DataFrame to get the correct shape
+    df_pivot = df_sorted.drop('channel', axis=1)
+    reshaped_array = df_pivot.values.reshape(-1, n_channels, n_features + 1)
+
+    return reshaped_array
+
+        
+
+def serialize_embeddings_df(df, metadata_columns = ('channel', 'offset')) -> pd.DataFrame:
+    """
+    Converts a dataframe of embeddings with 1 embedding feature per column
+    to a dataframe that has 1 column for all embeddings as a base64 encoded string
+    """
+    
+    if not all(col in df.columns for col in metadata_columns):
+        raise ValueError("supplied metadata columns are not in the given dataframe")
+
+    new_columns = list(metadata_columns) + ["embeddings"]
+    new_df = pd.DataFrame(columns=new_columns)
+
+    feature_columns = [col for col in df.columns if col not in metadata_columns]
+
+
+    for row in df.itertuples(index=False):
+
+        features = row[len(metadata_columns):]
+        encoded_features = serialize_array(np.array(features, dtype=np.float32))
+        new_row = row[:len(metadata_columns)] + (encoded_features,)
+
+        new_df.loc[len(new_df)] = new_row
+        #new_df = new_df.append(pd.Series(new_row, index=new_columns), ignore_index=True)
+
+    return new_df
+
+
+def deserialize_embeddings_df(df, embedding_col = 'embeddings') -> pd.DataFrame:
+    """
+    Converts a dataframe of embeddings with 1 columns for all embedding features as base64 encoded
+    array of floats to 1 column per embedding feature as float
+    """
+    
+    if not embedding_col in df.columns:
+        raise ValueError("supplied embeddings column is not in the given dataframe")
+    
+    # deserialize the 1st row to get the number of feature columns
+    embeddings_0 = deserialize_array(df["embeddings"][0])
+
+    metadata_columns = list(df.columns)
+    metadata_columns.remove(embedding_col)
+
+    new_columns = metadata_columns + embedding_col_names(len(embeddings_0))
+    new_df = pd.DataFrame(columns=new_columns)
+
+
+    for row in df.itertuples(index=False):
+
+        metadata = [getattr(row, key) for key in metadata_columns]
+        serialized_embeddings = getattr(row, embedding_col)
+        raw_embeddings = list(deserialize_array(serialized_embeddings))
+        new_df.loc[len(new_df)] = metadata + raw_embeddings
+
+    return new_df
+
+
+
+def serialize_array_old(array: np.ndarray[np.float32]) -> str:
+    """
+    serializes a single clip's embeddings from a 1280 array or list to a string
+    using base64 encoding
+    """
+
+    if not isinstance(array, np.ndarray) or not array.dtype == np.float32:
+        # embeddings should be float32, so we don't support anything else. 
+        # so that we can decode on the otherside and be sure it matches
+        supplied_type = f'{type(array)} {array.dtype}' if isinstance(array, np.ndarray) else type(array)
+        raise TypeError(f"Value must be a float32 array, but {supplied_type} was given")
+
+    #byte_data = b''.join(struct.pack('f', f) for f in array)
+    # byte_data = np.array(array, dtype=np.float32).tobytes()
+    #base64_encoded = base64.b64encode(array.tobytes(order='little')).decode('ascii')
+    bytes = array.tobytes()
+    base64_encoded = base64.b64encode(bytes).decode('ascii')
+    return base64_encoded
+
+def serialize_array(array: np.ndarray, dtype=np.float32) -> str:
+    """
+    serializes a single clip's embeddings from a 1280 array or list to a string
+    using base64 encoding
+    """
+
+    if not isinstance(array, np.ndarray) or not array.dtype == dtype:
+        # embeddings should be float32, however another type can be specified
+        # We don't actually need to know the dtype to encode, but we need to know it to decode
+        # therefore we require it to be explicitly specified or that the array matches the default
+        # to prevent mistakes
+        supplied_type = f'{type(array)} {array.dtype}' if isinstance(array, np.ndarray) else type(array)
+        raise TypeError(f"Value must be a {dtype} array, but {supplied_type} was given")
+
+    #byte_data = b''.join(struct.pack('f', f) for f in array)
+    # byte_data = np.array(array, dtype=np.float32).tobytes()
+    #base64_encoded = base64.b64encode(array.tobytes(order='little')).decode('ascii')
+    bytes = array.tobytes()
+    base64_encoded = base64.b64encode(bytes).decode('ascii')
+    return base64_encoded
+
+def deserialize_array(base64_encoded, dtype=np.float32) -> str:
+    """
+    serializes a single clip's embeddings from a 1280 array or list to a string
+    using base64 encoding
+    """
+
+    byte_data = base64.b64decode(base64_encoded)
+    #float_data = struct.unpack('f'*int(len(byte_data)/bytes_per_element), byte_data)
+    float_data = np.frombuffer(byte_data, dtype=dtype, count=-1, offset=0)
+    return float_data
+
+
+
+def deserialize_array_old(base64_encoded) -> np.array:
+    """
+    deserializes a string and produces an array of shape (1280,)
+
+    """
+
+    byte_data = base64.b64decode(base64_encoded)
+    #float_data = struct.unpack('f'*int(len(byte_data)/bytes_per_element), byte_data)
+    float_data = np.frombuffer(byte_data, dtype=np.float32, count=-1, offset=0)
+    return float_data
+    #return np.array(float_data)
+
+    
+def embedding_col_names(num_features: int) -> list:
+    """
+    generates column names for the embedding features columns
+    """
+    return [f'f{i:04d}' for i in range(num_features)]
\ No newline at end of file
diff --git a/src/default_configs/classify.yml b/src/default_configs/classify.yml
new file mode 100644
index 0000000..def5ce0
--- /dev/null
+++ b/src/default_configs/classify.yml
@@ -0,0 +1 @@
+skip_if_file_exists: True
\ No newline at end of file
diff --git a/src/default_configs/pw.classify.yml b/src/default_configs/pw.classify.yml
new file mode 100644
index 0000000..536f6dd
--- /dev/null
+++ b/src/default_configs/pw.classify.yml
@@ -0,0 +1,2 @@
+classifier: pw
+inherit: classify.yml
diff --git a/src/download_model.py b/src/download_model.py
new file mode 100644
index 0000000..19c1161
--- /dev/null
+++ b/src/download_model.py
@@ -0,0 +1,34 @@
+"""
+Uses the tensorflow_hub pacakge to download and save the model during build
+This is probably more reliable than wget or similar, since it seems to deal with the redirect to kaggle etc
+"""
+
+import argparse
+import shutil
+from pathlib import Path
+import os
+import tensorflow_hub as hub
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--version", help="version of the model to download")
+parser.add_argument("--destination", default="4", help="where to download to")
+args = parser.parse_args()
+
+PERCH_TF_HUB_URL = 'https://tfhub.dev/google/bird-vocalization-classifier'
+
+model_url = f'{PERCH_TF_HUB_URL}/{args.version}'
+# This model behaves exactly like the usual saved_model.
+# model = hub.load(model_url)
+
+model_path = hub.resolve(model_url)
+
+destination = Path(args.destination) / Path(args.version)
+
+shutil.move(model_path, destination)
+
+if os.path.exists(destination / Path("saved_model.pb")):
+  print(f"Model saved to {destination}")
+else:
+  raise Exception("Failed to save model")
+
+
diff --git a/src/embed_audio_slim.py b/src/embed_audio_slim.py
new file mode 100644
index 0000000..973ad33
--- /dev/null
+++ b/src/embed_audio_slim.py
@@ -0,0 +1,191 @@
+# embed a single audio file and save to a self-describing format
+# we don't need to store information about the file that the example comes from
+
+# Global imports
+from pathlib import Path
+import numpy as np
+# import tqdm
+import argparse
+import pandas as pd
+import soundfile
+from math import ceil
+
+
+from chirp import audio_utils
+
+# we need to house config in this object because
+# it's what TaxonomyModelTF expects. Specifically it needs an object that has dot 
+# access to values as well as dict-like access for using the spread operator (**config) 
+from ml_collections import config_dict
+
+from chirp.inference.models import TaxonomyModelTF
+
+from src import data_frames 
+
+def merge_defaults(config: config_dict):
+  """
+  gets the config based on user-supplied config and if they are missing then uses defaults
+  """
+
+  merged_config = config_dict.create(
+    hop_size = 5,
+    segment_length = 60,
+    max_segments = -1
+  )
+
+  if config is None:
+    config = config_dict.create()
+
+  for key in config:
+    merged_config[key] = config[key]
+
+  return merged_config
+
+
+def embed_folder(source_folder, output_folder, config: config_dict = None) -> None:
+   """
+   for each file in source_folder, embeds and then saves to output_folder with a name that matches the original 
+   """
+
+   source_folder = Path(source_folder)
+   source_files = [file.relative_to(source_folder) for file in source_folder.rglob('*wav') if file.is_file()]
+  
+   for source_file in source_files:
+     print(f'analysing {source_file}')
+     embeddings = embed_one_file(Path(source_folder / source_file), config)
+     dest = Path(output_folder / source_file).with_suffix('.parquet')
+     save_embeddings(embeddings, dest, source_file)
+
+
+
+def embed_file_and_save(source: str, destination: str, config: config_dict = None) -> None:
+    """
+    embeds a single file and saves to destination
+    source can be either a filename or a folder. If it's a folder that exists, the original basename is used with parquet extension
+    """
+
+    source = Path(source)
+    destination = Path(destination)
+
+    # check if destination is a directory which exists. If so, generate the filename to save as based on the source file basename
+    if destination.is_dir():
+       destination = destination / Path(source.name).with_suffix('.parquet')
+    elif destination.suffix not in ('.parquet', '.csv'):
+       raise ValueError(f"Invalid destination: {destination}. Must be a file with a valid extension or an existing directory")
+
+
+    embeddings = embed_one_file(source, config)
+    save_embeddings(embeddings, destination, source)
+
+
+def embed_one_file(source: str, config: config_dict = None) -> np.array:
+
+    config = merge_defaults(config)
+
+    # check audio exists and get the duration
+    audio_file = soundfile.SoundFile(source)
+    audio_duration = audio_file.frames / audio_file.samplerate
+    print(f'analysing {source} samples: {audio_file.frames}, sr: {audio_file.samplerate}, duration {audio_duration} sec')
+
+    model_path = "/models/4"
+
+    # model config contains some values from this function's config plus
+    # some values we have fixed.
+    model_config = config_dict.create(
+        hop_size_s = config.hop_size,
+        model_path = model_path,
+        sample_rate = 32000,
+        window_size_s = 5.0
+    )
+
+    print('\n\nLoading model(s)...')
+    #embedding_model = TaxonomyModelTF.from_config(config.embed_fn_config["model_config"])
+    embedding_model = TaxonomyModelTF.from_config(model_config)
+
+    # an empty array of the with zero rows to concatenate to
+    # 1280 embeddings plus one column for the offset_seconds
+    # TODO: I think the shape will be different when we have audio separation channels
+    file_embeddings = np.empty((0, 1, 1281), dtype=np.float32)
+
+    total_segments = ceil(audio_duration / config.segment_length)
+    num_segments = min(config.max_segments, total_segments) if config.max_segments >= 1 else total_segments
+
+    for segment_num in range(num_segments):
+      offset_s = config.segment_length * segment_num
+      print(f'getting embeddings for offsets {offset_s} to {offset_s + config.segment_length}')
+
+      audio = audio_utils.load_audio_window(
+          source, offset_s, 
+          model_config.sample_rate, config.segment_length
+      )
+
+      if len(audio) > 1:
+        embeddings = embedding_model.embed(audio).embeddings
+
+        # the last segment of the file might be smaller than the rest, so we always use its length
+        # dtype should bee float32 to match the embeddings
+        offsets = np.arange(0,embeddings.shape[0]*config.hop_size,config.hop_size, dtype=np.float32).reshape(embeddings.shape[0],1,1) + offset_s
+
+        # if source separation was used, embeddings will have an extra dimention for channel
+        # for consistency we will add the extra dimention even if there is only 1 channel
+
+        shape = embeddings.shape
+        if len(shape) == 2:
+           embeddings = embeddings.reshape(shape[0], 1, shape[1])
+        
+        segment_embeddings = np.concatenate((offsets, embeddings), axis=2)
+        file_embeddings = np.concatenate((file_embeddings, segment_embeddings), axis=0)
+      else:
+        print('no audio found')
+
+    return file_embeddings
+
+
+def save_embeddings(embeddings: np.array, destination: str, source: str=None, file_type=None):
+    """
+    saves the embeddings to the destination in a format
+    @param embeddings: a numpy array of embeddings, of shape (num_segments, num_channels, 1281). The first column is the offset in seconds
+    @param destination: the path to save the embeddings file to
+    @param source: what to add in the source column. This is probably a workbench recording url or a path to the original file
+    @param file_type: the type of file to save to. If None, will be determined from the extension of the destination
+    """
+    
+    destination = Path(destination)
+    print(f'creating output folder: {destination.parent}')
+    destination.parent.mkdir(exist_ok=True, parents=True)
+    embeddings_df = data_frames.embeddings_to_df(embeddings)
+
+    if file_type is None:
+       # determine from extension
+       file_type = destination.suffix[1:]
+
+    
+    embeddings_df.insert(0, 'source', str(source))
+
+    match file_type:
+        case "parquet":
+            print(f'saving as parquet to {destination}')
+            embeddings_df.to_parquet(destination, index=False)
+        case "csv":
+            embeddings_df = data_frames.serialize_embeddings_df(embeddings_df)
+            embeddings_df.to_csv(destination, index=False)
+        case _:
+            raise ValueError(f'Invalid file type for saving embeddings data frame: {file_type}')
+
+
+# def main ():
+#     parser = argparse.ArgumentParser()
+#     parser.add_argument("--source_file", help="path to the file to analyze")
+#     parser.add_argument("--output_file", help="file to save embeddings to")
+#     parser.add_argument("--max_segments", default=-1, type=int, help="only analyse this many segments of the file. Useful for debugging quickly. If ommitted will analyse all")
+#     parser.add_argument("--segment_length", default=60, type=int,  help="the file is split into segments of this duration in sections to save loading entire files into ram")
+#     parser.add_argument("--hop_size", default=5, type=float,  help="create an 5 second embedding every this many seconds. Leave as default 5s for no overlap and no gaps.")
+#     args = parser.parse_args()
+#     config = config_dict.create(**vars(args))
+
+#     embeddings = embed_one_file(args.source_file, config)
+#     save_embeddings(embeddings, args.output_file, args.source_file)
+      
+
+# if __name__ == "__main__":
+#     main()
diff --git a/src/inference_parquet.py b/src/inference_parquet.py
new file mode 100644
index 0000000..ed912f7
--- /dev/null
+++ b/src/inference_parquet.py
@@ -0,0 +1,382 @@
+# run inference file by file on parquet embeddings files
+# each input parquet file will result in a csv file with the same name
+
+import argparse
+import json
+from pathlib import Path
+from functools import partial
+import random
+from dataclasses import dataclass
+
+
+import keras
+from ml_collections import ConfigDict
+import pandas as pd
+import tensorflow as tf
+# progress bar
+import tqdm
+
+# utilities for mapping tf_examples. 
+# this is probably needed because it is used during the embedding stage
+# and for things like referring to keys in the embedding metadata we 
+# use constants defined here
+from chirp.inference import tf_examples
+from src.data_frames import df_to_embeddings
+
+@dataclass
+class Classifier:
+    model: keras.Model
+    labels: list
+
+
+
+def find_model(model_path):
+    """
+    looks for a model with absolute path, or relative to the container's models directory or relative to the current path
+    returns the path to the .keras file and the .labels.json file
+    if a keras file or labels file is missing, it will return None for that file
+    the path can be either to a directory or to a keras file. If it's a directory, it will look for a .keras file in the directory
+    if it's a file, it will look for a .labels.json file in the same directory
+    """
+
+    # for model in all locations relative to here, in order of preference
+    locations = ['', '/models', Path.cwd()]
+
+    for location in locations:
+
+        cur_model_path = Path(location) / model_path
+
+        if Path(cur_model_path).is_dir():
+            keras_file = next((file for file in cur_model_path.glob('*.keras') if file.is_file()), None)
+            labels_file = next((file for file in cur_model_path.glob('*.labels.json') if file.is_file()), None)
+            return keras_file, labels_file
+
+        elif Path(cur_model_path).is_file():
+            keras_file = cur_model_path
+            labels_file = keras_file.with_suffix(f"{keras_file.suffix}.labels.json")
+            return keras_file, labels_file
+        
+    return None, None
+
+
+def load_classifier(classifier) -> Classifier:
+    """
+    @param model: either:
+                 - a classifier dataclass instance (i.e. model + labels)
+                 - a tuple of (a keras model, list of labels) or, where to load these from, i.e:
+                 - path to either a .keras file that has a corresponding sibling .labels.json file
+                 - the path to a folder containing one .keras file and a corresponding labels file
+    """
+
+    if isinstance(classifier, Classifier):
+        # already a classifier instance, just return it
+        return classifier
+    elif isinstance(classifier, tuple):
+        # already have the model and labels, just construct the classifier dataclass instance
+        model, labels = classifier
+        return Classifier(model=model, labels=labels)
+    else:
+        # assume it's a path to a saved model. We now need to load it
+        model_path = Path(classifier)
+
+    keras_file, labels_file = find_model(model_path) 
+
+    if keras_file is None:
+        raise ValueError(f'no keras file found at {model_path}')
+
+    if not Path(labels_file).is_file():
+        raise ValueError(f'no .labels.json file found at {model_path}')
+    
+    model = keras.models.load_model(keras_file)
+    
+    with open(labels_file) as f:
+        labels = tuple(json.load(f))
+
+    return Classifier(model=model, labels=labels)
+
+
+
+def process_folder(input_path, output_path, config):
+    """
+    processes all the embeddings files in a folder, including nested folders, and saves the results to the output path with the same structure
+    @param embeddings_path: path to the directory of embeddings files
+    @param classifier_model: either a tuple of (model, labels) or a path to a saved model 
+                             (where saved model labels path is model_path + '.labels.json' by convention). 
+    """
+
+    # slightly more efficient to load the classifier once rather than for each file
+    classifier = load_classifier(config.classifier)
+
+
+    # list of paths to the embeddings files relative to the embeddings_path
+    embeddings_files_relative = [path.relative_to(input_path) for path in Path(input_path).rglob('*.parquet')]
+
+    # dodgy parallel: shuffle and start script in a different process with skip_if_file_exists=True
+    random.shuffle(embeddings_files_relative)
+
+    print(f'processing {len(embeddings_files_relative)} embeddings files')
+
+    for index, embedding_file in enumerate(tqdm.tqdm(embeddings_files_relative, desc="Processing")):
+
+        file_output_path = output_path / embedding_file.with_suffix('.csv')
+        if config.skip_if_file_exists and file_output_path.exists():
+            #print(f'skipping {embedding_file} as {file_output_path} already exists')
+            continue
+        #print(f'processing {index} of {len(embeddings_files_relative)}: {embedding_file}')
+        results = classify_embeddings_file(input_path / embedding_file, classifier)
+        save_classification_results(results, file_output_path)
+
+
+    print(f'finished processing {len(embeddings_files_relative)} embeddings files')
+
+
+
+def classify_file_and_save(embeddings_file: Path | str, file_output_path: Path | str, config: ConfigDict):
+    """
+    Given a path to an embeddings file, run the classifier defined in the supplied config
+    and save the results
+    @param embeddings_file: path to the embeddings file
+    @param file_output_path: path to save the results to. If a folder, will give the filename to match the input file name
+    """
+    embeddings_file = Path(embeddings_file)
+    file_output_path = Path(file_output_path)
+    
+    if file_output_path.is_dir():
+        file_output_path = file_output_path / embeddings_file.with_suffix('.csv').name
+
+    results = classify_embeddings_file(embeddings_file, config.classifier)
+    save_classification_results(results, file_output_path)
+    
+
+
+def classify_embeddings_file(embedding_file: Path | str, classifier: Classifier | str):
+    """
+    Given a path to an embeddings file, run the classifier
+    and save the results to a csv file and return the results as a dataframe
+    """
+    classifier = load_classifier(classifier)
+    df = pd.read_parquet(embedding_file)
+    return classify_df(df, classifier)
+
+
+def save_classification_results(results_df, file_output_path):
+    """
+    Save the given dataframe to the given path, creating folders if necessary
+    """
+    Path(file_output_path).parent.mkdir(parents=True, exist_ok=True)
+    results_df.to_csv(file_output_path, index=False)
+
+
+def classify_df(embeddings_df: pd.DataFrame, classifier: Classifier):
+    """
+    Given a dataframe of embeddings, run the given classifier
+    """
+    embeddings_ds = create_tf_dataset(embeddings_df)
+    #debug_classifier(embeddings_ds, embeddings_classifier)
+    # lazy map doesn't get executed until we iterate over the dataset
+    classify_function = partial(classify_batch, embeddings_classifier=classifier.model)
+    embeddings_ds = embeddings_ds.map(classify_function)
+    # these must match the order of the classes in the classifier model
+    # TODO: save these with and retrieve from the model
+
+    results = classify_items(embeddings_ds, classifier.labels)
+    # just get the scores, fname and offset. no distances (not sure what this even is) or embeddings
+    results_df = pd.DataFrame(results, columns=['filename', 'offset_seconds'] + list(classifier.labels))
+    return results_df
+
+
+
+def debug_classifier(ds, model):
+    """
+    just a little function to test the classifier on a single batch
+    explicitly instead of by adding a map function to the dataset, so it's easier to 
+    debug 
+    """
+
+    model_2cl = keras.models.load_model('/phil/output/trained_model.keras')
+    model_3cl = keras.models.load_model('/phil/output/trained_model_3cl.keras')
+
+    exes = list(ds.take(2))
+
+    emb_single = exes[0][tf_examples.EMBEDDING]
+    emb_double = tf.concat([emb_single, exes[1][tf_examples.EMBEDDING]], axis=1)
+
+    ex_single = {tf_examples.EMBEDDING: emb_single}
+    emb_double = {tf_examples.EMBEDDING: emb_double}
+
+    # classify a single example both 1 and 2 channels with both 2 and 3 class models
+    classify_batch(exes[0], model, 1)
+    classify_batch(ex_single, model_2cl, 1)
+    classify_batch(ex_single, model_3cl, 1)
+    classify_batch(emb_double, model_2cl, 1)
+    classify_batch(emb_double, model_3cl, 1)
+
+    # emb2_shape = [12, 2, 1280]
+    # emb2 = tf.reshape(tf.range(emb2_shape[0] * emb2_shape[1] * emb2_shape[2]), emb2_shape)
+    # flat_emb2 = tf.reshape(emb2, [-1, emb2_shape[-1]]) 
+
+
+   
+
+def read_parquet_files(folder_path):
+    """
+    ** deprecated. we process file by file and save each file to its own results file now **
+    Returns a dataframe of all the parquet files in the folder, concatenated
+    """
+    # Create a Path object for the folder
+    folder = Path(folder_path)
+    
+    # List to hold dataframes
+    dfs = []
+    
+    # Loop through all parquet files in the folder
+    for file_path in folder.rglob('*.parquet'):
+        df = pd.read_parquet(file_path)
+        dfs.append(df)
+
+    # Concatenate all dataframes
+    combined_df = pd.concat(dfs, ignore_index=True)
+    return combined_df
+
+   
+def create_tf_dataset(dataframe, rows_per_item=12):
+    """
+    Create a tf.data.Dataset from a dataframe of embeddings
+    Assumes that for a given source, time offsets are sorted and and for a given source, time_offset, channels are sorted
+    @param rows_per_item. The number of rows in the dataframe that correspond to a single dataset item
+                          The dataset is created like this to match closer to the orignal active learning notebook. It does
+                          seem to complicate things somewhat though. 
+    """
+
+    dataset_elements = []
+    
+    # convert to a dict of dataframes, one for each source (and remove the source column from the dataframe)
+    source_files = {name: group.drop('source', axis=1) for name, group in dataframe.groupby('source')}
+
+
+    for source, df in source_files.items():
+       
+        embeddings = df_to_embeddings(df)
+
+        # for a given channel, offsets will be the same, so we can just take the first channel
+        offsets = embeddings[:, 0, 0]
+        # all rows (dim1) all channels (dim2), all features (dim3 without the offset)
+        features = embeddings[:, :, 1:]
+
+        for start_row in range(0, len(features), rows_per_item):
+            # account for the fact that the last item may not have the full number of rows
+            end_row = min(start_row + rows_per_item, features.shape[0])
+            item_embedding = features[start_row:end_row, :, :]
+            item = {
+                'filename': source,
+                'timestamp_s': offsets[start_row:end_row],
+                'embedding': features[start_row:end_row, :, :],
+                'embedding_shape': tf.constant(item_embedding.shape, dtype=tf.int64),
+                'raw_audio': tf.constant([], dtype=tf.float32),
+                'raw_audio_shape': tf.constant([], dtype=tf.int64),
+                'separated_audio': tf.constant([], dtype=tf.float32),
+                'separated_audio_shape': tf.constant([], dtype=tf.int64)
+            }
+            dataset_elements.append(item)
+
+    dataset = tf.data.Dataset.from_generator(
+      lambda: iter(dataset_elements),
+      output_types={'filename': tf.string, 'timestamp_s': tf.float32, 'embedding': tf.float32, 
+                    'embedding_shape': tf.int64, 'raw_audio': tf.float32, 'raw_audio_shape': tf.int64, 
+                    'separated_audio': tf.float32, 'separated_audio_shape': tf.int64},
+                    # not sure about timestamp_s type. it's a scalar according to the element_spec of the original dataset from tf_records
+                    # (and was always 0.0) but I feel like it needs to be an array of dim 1 and length
+      # not sure if this is necessary. I did it to match to the original dataset from tf_records. 
+      # Those cases where it is None I think is due to the item having unknown dimension due to the final item not having the full number of rows
+      output_shapes={'filename': (), 'timestamp_s': None, 'embedding': None, 
+                          'embedding_shape': (None,), 'raw_audio': None,  'raw_audio_shape': (None,), 
+                          'separated_audio': None, 'separated_audio_shape': (None,)}
+    )
+
+    return dataset
+
+
+def classify_batch(batch, embeddings_classifier):
+    """
+    based on the original classify_batch function in perch search
+    except we don't have a target_index, we just return scores for all classes
+    The original was optimised to make searching for a target logit of a target class amongst all embeddings very vast
+    """
+    emb = batch[tf_examples.EMBEDDING]
+    emb_shape = tf.shape(emb)
+    # flat_emb: 2d array of shape (num_segments * num_channels, num_features)
+    # i.e. we stack the channels one under the other
+    flat_emb = tf.reshape(emb, [-1, emb_shape[-1]]) 
+    # this is where we actually run the forward pass
+    logits = embeddings_classifier(flat_emb) # 2d array of shape [num_segmends * num_channels, num_classes]
+    # this reshapes back into a 3d array of shape [num_segments, num_channels, num_classes]
+    logits = tf.reshape(
+        logits, [emb_shape[0], emb_shape[1], tf.shape(logits)[-1]]
+    ) 
+    # Restrict to target class by selecting only the target class index from the last dimension.
+    # logits = logits[..., target_index]
+    # Take the maximum logit over channels, which is dimension 1
+    logits = tf.reduce_max(logits, axis=1)
+    batch['scores'] = logits
+    return batch
+
+ 
+def classify_items(embeddings_ds, label_names, use_progress_bar = False):
+
+    #all_distances = []
+    all_results = []
+
+    try:
+
+        # we are already using a progress bar for the loop over files, so probably don't use one here, 
+        # but it's an option if we want it
+        wrapped_iterable = tqdm.tqdm(embeddings_ds.as_numpy_iterator()) if use_progress_bar else embeddings_ds.as_numpy_iterator()
+
+        # iterate over the examples, which are the embedding files
+        for ex in wrapped_iterable:
+            #distances = ex['scores'].reshape([-1])
+            #all_distances.extend(distances)
+
+            # iterate over the segments within the embedding file
+            # for t in range(ex[tf_examples.EMBEDDING].shape[0]):
+            #     offset_s = t * embeddings_config["embed_fn_config"]["model_config"]["hop_size_s"] + ex[tf_examples.TIMESTAMP_S]
+            for t in range(len(ex["timestamp_s"])):
+                offset_s = ex["timestamp_s"][t]
+                result = {
+                    "embedding": ex[tf_examples.EMBEDDING][t, :, :],
+                    "filename": ex['filename'].decode(),
+                    "offset_seconds": offset_s
+                }
+
+                for i, label in enumerate(label_names):
+                    result[label] = ex['scores'][t, i]
+                  
+                #print(f'{result["filename"]} {result["offset_seconds"]}')
+   
+                all_results.append(result)
+            
+            
+
+    
+    except KeyboardInterrupt:
+        pass
+
+    return all_results
+
+
+# def main ():
+#     """
+#     entrypoint to this script from the command line allows us to process a directory of embeddings
+#     files and save the results to a directory of csv files
+#     """
+#     parser = argparse.ArgumentParser()
+#     parser.add_argument("--embeddings_dir", help="path to directory of embeddings files")
+#     parser.add_argument("--model_path", help="path to the saved classifier model")
+#     parser.add_argument("--output_dir", help="save the results here")
+#     parser.add_argument("--skip_if_file_exists", action='store_true', help="skip processing if the output file already exists")
+#     args = parser.parse_args()
+#     process_embeddings(args.embeddings_dir, args.model_path, args.output_dir, args.skip_if_file_exists)
+
+
+# if __name__ == "__main__":
+#     main()
diff --git a/src/launch_notebook.sh b/src/launch_notebook.sh
new file mode 100755
index 0000000..10c56e5
--- /dev/null
+++ b/src/launch_notebook.sh
@@ -0,0 +1,3 @@
+#! /bin/bash
+
+poetry run python -m notebook --ip 0.0.0.0  --allow-root
\ No newline at end of file
diff --git a/src/retired/active_learning_from_parquet_old.py b/src/retired/active_learning_from_parquet_old.py
new file mode 100644
index 0000000..d58d5b7
--- /dev/null
+++ b/src/retired/active_learning_from_parquet_old.py
@@ -0,0 +1,238 @@
+#@title Imports. { vertical-output: true }
+import collections
+import json
+from ml_collections import config_dict
+import numpy as np
+from etils import epath
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import tqdm
+
+from pathlib import Path
+import pandas as pd
+
+#
+from chirp.inference import colab_utils
+colab_utils.initialize(use_tf_gpu=True, disable_warnings=True)
+#
+from chirp.inference import tf_examples
+from chirp.models import metrics
+from chirp.projects.bootstrap import bootstrap
+from chirp.projects.bootstrap import search
+from chirp.projects.bootstrap import display
+from chirp.projects.multicluster import classify
+from chirp.projects.multicluster import data_lib
+
+
+
+def read_parquet_files(folder_path):
+    # Create a Path object for the folder
+    folder = Path(folder_path)
+    
+    # List to hold dataframes
+    dfs = []
+    
+    # Loop through all parquet files in the folder
+    for file_path in folder.rglob('*.parquet'):
+        df = pd.read_parquet(file_path)
+        dfs.append(df)
+
+    # Concatenate all dataframes
+    combined_df = pd.concat(dfs, ignore_index=True)
+    return combined_df
+
+def create_tf_dataset(dataframe):
+    
+    feature_columns = [f'f{i:04d}' for i in range(1280)]
+    metadata_columns = ['source', 'offset', 'channel']
+    features_df = dataframe[feature_columns].values
+    metadata = dataframe[metadata_columns]
+
+    features_tensor = tf.convert_to_tensor(features_df, dtype=tf.float32)
+    dataset = tf.data.Dataset.from_tensor_slices(({"features": features_tensor, 
+                                                   "metadata": metadata.to_dict('list')}))
+    return dataset
+
+
+#@title Configure data and model locations. { vertical-output: true }
+
+
+embeddings_path = '/phil/output/pw_embeddings_all/'  #@param
+
+# path to folder of parquet files
+embeddings_path_2 = '/phil/output/cgw/file_embeddings/cgw_embeddings/20230526/'
+
+combined_embeddings_df = read_parquet_files(embeddings_path_2)
+embeddings_dataset = create_tf_dataset(combined_embeddings_df)
+
+
+# Path to the labeled wav data.
+# Should be in 'folder-of-folders' format - a folder with sub-folders for
+# each class of interest.
+# Audio in sub-folders should be wav files.
+# Audio should ideally be 5s audio clips, but the system is quite forgiving.
+labeled_data_path = '/phil/cgw_labelled/all_merged/'  #@param
+labeled_data_path = '/phil/cgw_labelled/tiny_subset/'  #@param
+
+#@title Load the model. { vertical-output: true }
+
+# Get relevant info from the embedding configuration.
+embeddings_path = epath.Path(embeddings_path)
+with (embeddings_path / 'config.json').open() as f:
+  embedding_config = config_dict.ConfigDict(json.loads(f.read()))
+embeddings_glob = embeddings_path / 'embeddings-*'
+# this is just a dataclass which is basically the embedding config but reshaped a bit, and the annotated path added
+config = bootstrap.BootstrapConfig.load_from_embedding_config(
+    embeddings_path=embeddings_path,
+    annotated_path=labeled_data_path)
+embedding_hop_size_s = config.embedding_hop_size_s
+
+
+# this is a dataclass with
+# - the config
+# - the model
+# - the embeddings dataset of unlabelled audio. 
+# - the 'source infos' which has an entry per 'shard' i.e. per 60s of audio. 
+
+project_state = bootstrap.BootstrapState(config)
+embedding_model = project_state.embedding_model
+
+
+dataset = project_state.embeddings_dataset
+# the dataset items have the structure as follows (we might need to emulate this)
+# 'embedding', numpy.ndarray of shape (12,1,1280) (note: not a tensor)
+# 'embedding_shape', array with values [12,1,1280]
+# 'filename', byte string e.g. b'032/20220917_AAO_-23.54115+140.19473/20220917T183300+1000_SunsetToSunrise_-23.54115+140.19473.wav'
+# 'raw_audio', numpy array float32. in this case it's empty.
+# 'raw_audio_shape', numpy array float32. in this case it's empty.
+# 'separated_audio', numpy array float32. in this case it's empty. 
+# 'separated_audio_shape', tensor shape (0,) (when I try to inspec this the debug server disconnects)
+# 'timestamp_s' tensor of shape (). All seems to have value 0.0, but this might be cause of a mistake I made during embedding to only do the 1st min?
+# items = list(dataset.take(1).as_numpy_iterator())
+
+# @title Load+Embed the Labeled Dataset. { vertical-output: true }
+
+# Time-pooling strategy for examples longer than the model's window size.
+time_pooling = 'mean'  # @param
+
+# this is the LABELLED dataset
+merged = data_lib.MergedDataset.from_folder_of_folders(
+    base_dir=labeled_data_path,
+    embedding_model=embedding_model,
+    time_pooling=time_pooling,
+    load_audio=False,
+    target_sample_rate=-2,
+    audio_file_pattern='*'
+)
+
+# Label distribution
+lbl_counts = np.sum(merged.data['label_hot'], axis=0)
+print('num classes :', (lbl_counts > 0).sum())
+print('mean ex / class :', lbl_counts.sum() / (lbl_counts > 0).sum())
+print('min ex / class :', (lbl_counts + (lbl_counts == 0) * 1e6).min())
+
+
+#@title Train linear model over embeddings. { vertical-output: true }
+
+# Number of random training examples to choose form each class.
+# Set exactly one of train_ratio and train_examples_per_class
+train_ratio = 0.95  #@param
+train_examples_per_class = None  #@param
+
+# Number of random re-trainings. Allows judging model stability.
+num_seeds = 1  #@param
+
+# Classifier training hyperparams.
+# These should be good defaults.
+batch_size = 32
+num_epochs = 128
+num_hiddens = -1
+learning_rate = 1e-3
+
+metrics = collections.defaultdict(list)
+for seed in tqdm.tqdm(range(num_seeds)):
+  if num_hiddens > 0:
+    model = classify.get_two_layer_model(
+        num_hiddens, merged.embedding_dim, merged.num_classes)
+  else:
+    model = classify.get_linear_model(
+        merged.embedding_dim, merged.num_classes)
+  run_metrics = classify.train_embedding_model(
+      model, merged, train_ratio, train_examples_per_class,
+      num_epochs, seed, batch_size, learning_rate)
+  metrics['acc'].append(run_metrics.top1_accuracy)
+  metrics['auc_roc'].append(run_metrics.auc_roc)
+  metrics['cmap'].append(run_metrics.cmap_value)
+  metrics['maps'].append(run_metrics.class_maps)
+  metrics['test_logits'].append(run_metrics.test_logits)
+
+mean_acc = np.mean(metrics['acc'])
+mean_auc = np.mean(metrics['auc_roc'])
+mean_cmap = np.mean(metrics['cmap'])
+# Merge the test_logits into a single array.
+test_logits = {
+    k: np.concatenate([logits[k] for logits in metrics['test_logits']])
+    for k in metrics['test_logits'][0].keys()
+}
+
+print(f'acc:{mean_acc:5.2f}, auc_roc:{mean_auc:5.2f}, cmap:{mean_cmap:5.2f}')
+for lbl, auc in zip(merged.labels, run_metrics.class_maps):
+  if np.isnan(auc):
+    continue
+  print(f'\n{lbl:8s}, auc_roc:{auc:5.2f}')
+  colab_utils.prstats(f'test_logits({lbl})',
+                      test_logits[merged.labels.index(lbl)])
+  
+
+  #@title Run model on target unlabeled data. { vertical-output: true }
+
+# Choose the target class to work with.
+target_class = 'pos'  #@param
+# Choose a target logit; will display results close to the target.
+# Set to None to get the highest-logit examples.
+target_logit = None  #@param
+# Number of results to display.
+num_results = 25  #@param
+
+# Create the embeddings dataset.
+embeddings_ds = tf_examples.create_embeddings_dataset(
+    embeddings_path, file_glob='embeddings-*')
+target_class_idx = merged.labels.index(target_class)
+results, all_logits = search.classifer_search_embeddings_parallel(
+    embeddings_classifier=model,
+    target_index=target_class_idx,
+    embeddings_dataset=embeddings_ds,
+    hop_size_s=embedding_hop_size_s,
+    target_score=target_logit,
+    top_k=num_results
+)
+
+# Plot the histogram of logits.
+_, ys, _ = plt.hist(all_logits, bins=128, density=True)
+plt.xlabel(f'{target_class} logit')
+plt.ylabel('density')
+# plt.yscale('log')
+plt.plot([target_logit, target_logit], [0.0, np.max(ys)], 'r:')
+plt.show()
+
+#@title Display results for the target label. { vertical-output: true }
+
+display_labels = merged.labels
+
+extra_labels = []  #@param
+for label in extra_labels:
+  if label not in merged.labels:
+    display_labels += (label,)
+if 'unknown' not in merged.labels:
+  display_labels += ('unknown',)
+
+display.display_search_results(
+    results, embedding_model.sample_rate,
+    project_state.source_map,
+    checkbox_labels=display_labels,
+    max_workers=5)
+
+#@title Add selected results to the labeled data. { vertical-output: true }
+
+# results.write_labeled_data(
+#     config.annotated_path, embedding_model.sample_rate)
\ No newline at end of file
diff --git a/src/retired/active_learning_original.py b/src/retired/active_learning_original.py
new file mode 100644
index 0000000..d6acf38
--- /dev/null
+++ b/src/retired/active_learning_original.py
@@ -0,0 +1,176 @@
+#@title Imports. { vertical-output: true }
+import collections
+import json
+from ml_collections import config_dict
+import numpy as np
+from etils import epath
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import tqdm
+#
+from chirp.inference import colab_utils
+colab_utils.initialize(use_tf_gpu=True, disable_warnings=True)
+#
+from chirp.inference import tf_examples
+from chirp.models import metrics
+from chirp.projects.bootstrap import bootstrap
+from chirp.projects.bootstrap import search
+from chirp.projects.bootstrap import display
+from chirp.projects.multicluster import classify
+from chirp.projects.multicluster import data_lib
+
+#@title Configure data and model locations. { vertical-output: true }
+
+# Path containing TFRecords of unlabeled embeddings.
+# We will load the model which was used to compute the embeddings automatically.
+embeddings_path = '/phil/output/pw_embeddings_all/'  #@param
+
+# Path to the labeled wav data.
+# Should be in 'folder-of-folders' format - a folder with sub-folders for
+# each class of interest.
+# Audio in sub-folders should be wav files.
+# Audio should ideally be 5s audio clips, but the system is quite forgiving.
+labeled_data_path = '/phil/cgw_labelled/all_merged/'  #@param
+labeled_data_path = '/phil/cgw_labelled/tiny_subset/'
+
+#@title Load the model. { vertical-output: true }
+
+# Get relevant info from the embedding configuration.
+embeddings_path = epath.Path(embeddings_path)
+with (embeddings_path / 'config.json').open() as f:
+  embedding_config = config_dict.ConfigDict(json.loads(f.read()))
+embeddings_glob = embeddings_path / 'embeddings-*'
+
+config = bootstrap.BootstrapConfig.load_from_embedding_config(
+    embeddings_path=embeddings_path,
+    annotated_path=labeled_data_path)
+embedding_hop_size_s = config.embedding_hop_size_s
+project_state = bootstrap.BootstrapState(config)
+embedding_model = project_state.embedding_model
+
+
+# @title Load+Embed the Labeled Dataset. { vertical-output: true }
+
+# Time-pooling strategy for examples longer than the model's window size.
+time_pooling = 'mean'  # @param
+
+merged = data_lib.MergedDataset.from_folder_of_folders(
+    base_dir=labeled_data_path,
+    embedding_model=embedding_model,
+    time_pooling=time_pooling,
+    load_audio=False,
+    target_sample_rate=-2,
+    audio_file_pattern='*'
+)
+
+# Label distribution
+lbl_counts = np.sum(merged.data['label_hot'], axis=0)
+print('num classes :', (lbl_counts > 0).sum())
+print('mean ex / class :', lbl_counts.sum() / (lbl_counts > 0).sum())
+print('min ex / class :', (lbl_counts + (lbl_counts == 0) * 1e6).min())
+
+
+#@title Train linear model over embeddings. { vertical-output: true }
+
+# Number of random training examples to choose form each class.
+# Set exactly one of train_ratio and train_examples_per_class
+train_ratio = 0.95  #@param
+train_examples_per_class = None  #@param
+
+# Number of random re-trainings. Allows judging model stability.
+num_seeds = 1  #@param
+
+# Classifier training hyperparams.
+# These should be good defaults.
+batch_size = 32
+num_epochs = 128
+num_hiddens = -1
+learning_rate = 1e-3
+
+metrics = collections.defaultdict(list)
+for seed in tqdm.tqdm(range(num_seeds)):
+  if num_hiddens > 0:
+    model = classify.get_two_layer_model(
+        num_hiddens, merged.embedding_dim, merged.num_classes)
+  else:
+    model = classify.get_linear_model(
+        merged.embedding_dim, merged.num_classes)
+  run_metrics = classify.train_embedding_model(
+      model, merged, train_ratio, train_examples_per_class,
+      num_epochs, seed, batch_size, learning_rate)
+  metrics['acc'].append(run_metrics.top1_accuracy)
+  metrics['auc_roc'].append(run_metrics.auc_roc)
+  metrics['cmap'].append(run_metrics.cmap_value)
+  metrics['maps'].append(run_metrics.class_maps)
+  metrics['test_logits'].append(run_metrics.test_logits)
+
+mean_acc = np.mean(metrics['acc'])
+mean_auc = np.mean(metrics['auc_roc'])
+mean_cmap = np.mean(metrics['cmap'])
+# Merge the test_logits into a single array.
+test_logits = {
+    k: np.concatenate([logits[k] for logits in metrics['test_logits']])
+    for k in metrics['test_logits'][0].keys()
+}
+
+print(f'acc:{mean_acc:5.2f}, auc_roc:{mean_auc:5.2f}, cmap:{mean_cmap:5.2f}')
+for lbl, auc in zip(merged.labels, run_metrics.class_maps):
+  if np.isnan(auc):
+    continue
+  print(f'\n{lbl:8s}, auc_roc:{auc:5.2f}')
+  colab_utils.prstats(f'test_logits({lbl})',
+                      test_logits[merged.labels.index(lbl)])
+  
+
+  #@title Run model on target unlabeled data. { vertical-output: true }
+
+# Choose the target class to work with.
+target_class = 'pos'  #@param
+# Choose a target logit; will display results close to the target.
+# Set to None to get the highest-logit examples.
+target_logit = None  #@param
+# Number of results to display.
+num_results = 25  #@param
+
+# Create the embeddings dataset.
+embeddings_ds = tf_examples.create_embeddings_dataset(
+    embeddings_path, file_glob='embeddings-*')
+target_class_idx = merged.labels.index(target_class)
+results, all_logits = search.classifer_search_embeddings_parallel(
+    embeddings_classifier=model,
+    target_index=target_class_idx,
+    embeddings_dataset=embeddings_ds,
+    hop_size_s=embedding_hop_size_s,
+    target_score=target_logit,
+    top_k=num_results
+)
+
+# Plot the histogram of logits.
+_, ys, _ = plt.hist(all_logits, bins=128, density=True)
+plt.xlabel(f'{target_class} logit')
+plt.ylabel('density')
+# plt.yscale('log')
+plt.plot([target_logit, target_logit], [0.0, np.max(ys)], 'r:')
+plt.show()
+
+#@title Display results for the target label. { vertical-output: true }
+
+display_labels = merged.labels
+
+extra_labels = []  #@param
+for label in extra_labels:
+  if label not in merged.labels:
+    display_labels += (label,)
+if 'unknown' not in merged.labels:
+  display_labels += ('unknown',)
+
+display.display_search_results(
+    results, embedding_model.sample_rate,
+    project_state.source_map,
+    checkbox_labels=display_labels,
+    max_workers=5)
+
+#@title Add selected results to the labeled data. { vertical-output: true }
+
+# results.write_labeled_data(
+#     config.annotated_path, embedding_model.sample_rate)
\ No newline at end of file
diff --git a/src/retired/embed_audio.py b/src/retired/embed_audio.py
new file mode 100644
index 0000000..ad5e97b
--- /dev/null
+++ b/src/retired/embed_audio.py
@@ -0,0 +1,166 @@
+## duplication of the embed_audio.ipynb notebook, so it's easier to run with breakpoints in vscode
+
+#@title Imports. { vertical-output: true }
+
+# Global imports
+from etils import epath
+from ml_collections import config_dict
+import numpy as np
+import tensorflow as tf
+import tqdm
+from chirp.inference import colab_utils
+colab_utils.initialize(use_tf_gpu=True, disable_warnings=True)
+import argparse
+
+from chirp import audio_utils
+from chirp.inference import embed_lib
+from chirp.inference import tf_examples
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input")
+parser.add_argument("--output")
+
+args = parser.parse_args()
+
+
+print("--run embed--")
+print(f"input: {args.input}")
+print(f"output: {args.output}")
+
+# source_files = epath.Path('').glob(args.input)
+# print(f"source_files: {list(source_files)}")
+
+
+# Define the model
+model_choice = 'perch'  #@param
+
+config = config_dict.ConfigDict()
+config.embed_fn_config = config_dict.ConfigDict()
+config.embed_fn_config.model_config = config_dict.ConfigDict()
+
+
+
+
+
+# Pick the input and output targets.
+config.source_file_patterns = [args.input]  #@param
+config.output_dir = args.output  #@param
+
+# For Perch, the directory containing the model.
+# Alternatively, set the perch_tfhub_model_version, and the model will load
+# directly from TFHub.
+# Note that only one of perch_model_path and perch_tfhub_version should be set.
+perch_model_path = '/models/4'  #@param
+perch_tfhub_version = None  #@param
+
+# For BirdNET, point to the specific tflite file.
+birdnet_model_path = ''  #@param
+if model_choice == 'perch':
+  config.embed_fn_config.model_key = 'taxonomy_model_tf'
+  config.embed_fn_config.model_config.window_size_s = 5.0
+  config.embed_fn_config.model_config.hop_size_s = 5.0
+  config.embed_fn_config.model_config.sample_rate = 32000
+  config.embed_fn_config.model_config.tfhub_version = perch_tfhub_version
+  config.embed_fn_config.model_config.model_path = perch_model_path
+elif model_choice == 'birdnet':
+  config.embed_fn_config.model_key = 'birdnet'
+  config.embed_fn_config.model_config.window_size_s = 3.0
+  config.embed_fn_config.model_config.hop_size_s = 3.0
+  config.embed_fn_config.model_config.sample_rate = 48000
+  config.embed_fn_config.model_config.model_path = birdnet_model_path
+  # Note: This class list is appropriate for Birdnet 2.1, 2.2, and 2.3
+  config.embed_fn_config.model_config.class_list_name = 'birdnet_v2_1'
+  config.embed_fn_config.model_config.num_tflite_threads = 4
+
+# Only write embeddings to reduce size.
+config.embed_fn_config.write_embeddings = True
+config.embed_fn_config.write_logits = False
+config.embed_fn_config.write_separated_audio = False
+config.embed_fn_config.write_raw_audio = False
+
+
+# Embedding windows are broken up into groups, typically one minute in length.
+# This lets us limit input size to the model, track progres and
+# recover from failures more easily.
+config.shard_len_s = 60  #@param
+config.num_shards_per_file = 2  #@param
+
+# Number of parent directories to include in the filename.
+config.embed_fn_config.file_id_depth = 2
+
+# Number of TF Record files to create.
+config.tf_record_shards = 10  #@param
+
+#@title Set up. { vertical-output: true }
+
+# Set up the embedding function, including loading models.
+embed_fn = embed_lib.EmbedFn(**config.embed_fn_config)
+print('\n\nLoading model(s)...')
+embed_fn.setup()
+
+# Create output directory and write the configuration.
+output_dir = epath.Path(config.output_dir)
+output_dir.mkdir(exist_ok=True, parents=True)
+embed_lib.maybe_write_config(config, output_dir)
+
+# Create SourceInfos.
+source_infos = embed_lib.create_source_infos(
+    config.source_file_patterns,
+    config.num_shards_per_file,
+    config.shard_len_s)
+print(f'Found {len(source_infos)} source infos.')
+
+print('\n\nTest-run of model...')
+window_size_s = config.embed_fn_config.model_config.window_size_s
+sr = config.embed_fn_config.model_config.sample_rate
+z = np.zeros([int(sr * window_size_s)])
+embed_fn.embedding_model.embed(z)
+print('Setup complete!')
+
+# @title Run embedding. (fast) { vertical-output: true }
+
+# Uses multiple threads to load audio before embedding.
+# This tends to be faster, but can fail if any audio files are corrupt.
+
+embed_fn.min_audio_s = 1.0
+record_file = (output_dir / 'embeddings.tfrecord').as_posix()
+succ, fail = 0, 0
+
+existing_embedding_ids = embed_lib.get_existing_source_ids(
+    output_dir, 'embeddings-*')
+
+source_infos = embed_lib.get_new_source_infos(
+    source_infos, existing_embedding_ids, config.embed_fn_config.file_id_depth)
+
+print(f'Processing {len(source_infos)} new source infos.')
+
+audio_iterator = audio_utils.multi_load_audio_window(
+    filepaths=[s.filepath for s in source_infos],
+    offsets=[s.shard_num * s.shard_len_s for s in source_infos],
+    sample_rate=config.embed_fn_config.model_config.sample_rate,
+    window_size_s=config.shard_len_s,
+)
+with tf_examples.EmbeddingsTFRecordMultiWriter(
+    output_dir=output_dir, num_files=config.tf_record_shards) as file_writer:
+  for source_info, audio in tqdm.tqdm(
+      zip(source_infos, audio_iterator), total=len(source_infos)):
+    file_id = source_info.file_id(config.embed_fn_config.file_id_depth)
+    offset_s = source_info.shard_num * source_info.shard_len_s
+    example = embed_fn.audio_to_example(file_id, offset_s, audio)
+    if example is None:
+      fail += 1
+      continue
+    file_writer.write(example.SerializeToString())
+    succ += 1
+  file_writer.flush()
+print(f'\n\nSuccessfully processed {succ} source_infos, failed {fail} times.')
+
+fns = [fn for fn in output_dir.glob('embeddings-*')]
+ds = tf.data.TFRecordDataset(fns)
+parser = tf_examples.get_example_parser()
+ds = ds.map(parser)
+for ex in ds.as_numpy_iterator():
+  print(ex['filename'])
+  print(ex['embedding'].shape)
+  break
+
diff --git a/src/retired/embed_audio_old.py b/src/retired/embed_audio_old.py
new file mode 100644
index 0000000..684a4b4
--- /dev/null
+++ b/src/retired/embed_audio_old.py
@@ -0,0 +1,175 @@
+## duplication of the embed_audio.ipynb notebook, so it's easier to run with breakpoints in vscode
+
+#@title Imports. { vertical-output: true }
+
+# Global imports
+from etils import epath
+import numpy as np
+import tensorflow as tf
+import tqdm
+from chirp.inference import colab_utils
+colab_utils.initialize(use_tf_gpu=True, disable_warnings=True)
+import argparse
+
+from chirp import audio_utils
+from chirp import config_utils
+from chirp.configs import config_globals
+from chirp.inference import embed_lib
+from chirp.inference import tf_examples
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("input")
+parser.add_argument("output")
+
+args = parser.parse_args()
+
+
+# Name of base configuration file in `chirp/inference/configs`
+config_key = 'raw_soundscapes'  #@param
+config = embed_lib.get_config(config_key)
+config = config_utils.parse_config(config, config_globals.get_globals())
+
+# Here we adjust the input and output targets.
+# config.output_dir = 'output/all_02'  #@param
+# config.source_file_patterns = ['/napco_survey_project_audio/PW/inference_datasets/20230413/*/*/*.wav']  #@param
+
+config.output_dir = args.output  #@param
+config.source_file_patterns = [args.input]  #@param
+
+
+
+# Define the model
+model_choice = 'perch'
+# For Perch, the directory containing the model.
+# For BirdNET, point to the specific tflite file.
+model_path = "/phil/bird-vocalization-classifier_3/"  #@param
+config.embed_fn_config.model_config.model_path = model_path
+if model_choice == 'perch':
+  config.embed_fn_config.model_config.window_size_s = 5.0
+  config.embed_fn_config.model_config.hop_size_s = 5.0
+  config.embed_fn_config.model_config.sample_rate = 32000
+elif model_choice == 'birdnet':
+  config.embed_fn_config.model_config.window_size_s = 3.0
+  config.embed_fn_config.model_config.hop_size_s = 3.0
+  config.embed_fn_config.model_config.sample_rate = 16000
+
+# Only write embeddings to reduce size.
+config.embed_fn_config.write_embeddings = True
+config.embed_fn_config.write_logits = False
+config.embed_fn_config.write_separated_audio = False
+config.embed_fn_config.write_raw_audio = False
+
+
+# Embedding windows are broken up into groups, typically one minute in length.
+# This lets us limit input size to the model, track progres and
+# recover from failures more easily.
+config.shard_len_s = 60  #@param
+config.num_shards_per_file = 1  #@param
+
+# Number of parent directories to include in the filename.
+config.embed_fn_config.file_id_depth = 2
+
+# Number of TF Record files to create.
+config.tf_record_shards = 10  #@param
+
+# Speech filter threshold for YamNet.
+# Set to a value between 0 and 1, or -1 to disable.
+config.embed_fn_config.speech_filter_threshold = -1.0
+
+#@title Set up. { vertical-output: true }
+
+# Create output directory and write the configuration.
+output_dir = epath.Path(config.output_dir)
+output_dir.mkdir(exist_ok=True, parents=True)
+embed_lib.maybe_write_config(config, output_dir)
+
+# Create SourceInfos.
+source_infos = embed_lib.create_source_infos(
+    config.source_file_patterns,
+    config.num_shards_per_file,
+    config.shard_len_s)
+print(f'Found {len(source_infos)} source infos.')
+
+# Set up the embedding function, including loading models.
+embed_fn = embed_lib.EmbedFn(**config.embed_fn_config)
+print('\n\nLoading model(s)...')
+embed_fn.setup()
+
+print('\n\nTest-run of model...')
+# We run the test twice - the first run optimizes the execution, and
+# subsequent runs will be full-speed.
+window_size_s = config.embed_fn_config.model_config.window_size_s
+sr = config.embed_fn_config.model_config.sample_rate
+z = np.zeros([int(sr * window_size_s)])
+embed_fn.embedding_model.embed(z)
+print('Setup complete!')
+
+#@title Run embedding. (safe) { vertical-output: true }
+
+# Loads audio files one-by-one using methods which will tend not to fail
+# if the target files have minor problems (eg, wrong length metadata).
+
+embed_fn.min_audio_s = 1.0
+record_file = (output_dir / 'embeddings.tfrecord').as_posix()
+succ, fail = 0, 0
+with tf_examples.EmbeddingsTFRecordMultiWriter(
+    output_dir=output_dir, num_files=config.tf_record_shards) as file_writer:
+  for source_info in tqdm.tqdm(source_infos):
+    examples = embed_fn.process(source_info=source_info)
+    if examples is None:
+      fail += 1
+      continue
+    for example in examples:
+      file_writer.write(example.SerializeToString())
+    succ += 1
+  file_writer.flush()
+print(f'\n\nSuccessfully processed {succ} source_infos, failed {fail} times.')
+
+fns = [fn for fn in output_dir.glob('embeddings-*')]
+ds = tf.data.TFRecordDataset(fns)
+parser = tf_examples.get_example_parser()
+ds = ds.map(parser)
+for ex in ds.as_numpy_iterator():
+  print(ex['filename'])
+  print(ex['embedding'].shape)
+  break
+
+#@title Run embedding. (fast) { vertical-output: true }
+
+# Uses multiple threads to load audio before embedding.
+# This tends to be faster, but can fail if any audio files are corrupt.
+
+embed_fn.min_audio_s = 1.0
+record_file = (output_dir / 'embeddings.tfrecord').as_posix()
+succ, fail = 0, 0
+
+audio_iterator = audio_utils.multi_load_audio_window(
+    filepaths=[s.filepath for s in source_infos],
+    offsets=[s.shard_num * s.shard_len_s for s in source_infos],
+    sample_rate=config.embed_fn_config.model_config.sample_rate,
+    window_size_s=config.shard_len_s,
+)
+with tf_examples.EmbeddingsTFRecordMultiWriter(
+    output_dir=output_dir, num_files=config.tf_record_shards) as file_writer:
+  for source_info, audio in tqdm.tqdm(
+      zip(source_infos, audio_iterator), total=len(source_infos)):
+    file_id = source_info.file_id(config.embed_fn_config.file_id_depth)
+    offset_s = source_info.shard_num * source_info.shard_len_s
+    example = embed_fn.audio_to_example(file_id, offset_s, audio)
+    if example is None:
+      fail += 1
+      continue
+    file_writer.write(example.SerializeToString())
+    succ += 1
+  file_writer.flush()
+print(f'\n\nSuccessfully processed {succ} source_infos, failed {fail} times.')
+
+fns = [fn for fn in output_dir.glob('embeddings-*')]
+ds = tf.data.TFRecordDataset(fns)
+parser = tf_examples.get_example_parser()
+ds = ds.map(parser)
+for ex in ds.as_numpy_iterator():
+  print(ex['filename'])
+  print(ex['embedding'].shape)
+  break
\ No newline at end of file
diff --git a/src/retired/inference.py b/src/retired/inference.py
new file mode 100644
index 0000000..e6572ca
--- /dev/null
+++ b/src/retired/inference.py
@@ -0,0 +1,180 @@
+# from the last part of https://github.com/google-research/perch/blob/main/active_learning.ipynb
+
+
+#@title Imports. { vertical-output: true }
+import collections
+import json
+from ml_collections import config_dict
+import numpy as np
+from etils import epath
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import tqdm
+import keras
+
+from chirp.inference import colab_utils
+colab_utils.initialize(use_tf_gpu=True, disable_warnings=True)
+#
+from chirp.inference import tf_examples
+from chirp.models import metrics
+from chirp.projects.bootstrap import bootstrap
+from chirp.projects.bootstrap import search
+from chirp.projects.bootstrap import display
+from chirp.projects.multicluster import classify
+from chirp.projects.multicluster import data_lib
+
+#@title Configure data and model locations. { vertical-output: true }
+
+# Path containing TFRecords of unlabeled embeddings.
+# We will load the model which was used to compute the embeddings automatically.
+embeddings_path = '/phil/output/pw_embeddings_all/' #@param
+
+
+
+model_path = "/output/trained_model.keras"
+
+
+model = keras.models.load_model(model_path)
+
+
+
+# #@title Load the model. { vertical-output: true }
+
+# Get relevant info from the embedding configuration.
+embeddings_path = epath.Path(embeddings_path)
+with (embeddings_path / 'config.json').open() as f:
+  embedding_config = config_dict.ConfigDict(json.loads(f.read()))
+embeddings_glob = embeddings_path / 'embeddings-*'
+
+config = bootstrap.BootstrapConfig.load_from_embedding_config(
+    embeddings_path=embeddings_path,
+    # we don't need annotated data for this but the bootstrap config requires it
+    annotated_path='')
+embedding_hop_size_s = config.embedding_hop_size_s
+project_state = bootstrap.BootstrapState(config)
+# do we need this? we shouldn't
+embedding_model = project_state.embedding_model
+
+print(embedding_model)
+
+
+
+
+# #@title Run model on target unlabeled data. { vertical-output: true }
+
+# Choose the target class to work with.
+# TODO: we shouldn't need this, I think it's just for the active learning part
+target_class = 'pos'  #@param
+# Choose a target logit; will display results close to the target.
+# Set to None to get the highest-logit examples.
+target_logit = None  #@param
+# Number of results to display.
+# we actually want to save ALL results, in fact we want to save All logits, right?
+num_results = 25  #@param
+
+# # Create the embeddings dataset.
+embeddings_ds = tf_examples.create_embeddings_dataset(
+    embeddings_path, file_glob='embeddings-*')
+
+# in the active learning, this is taken from the merged dataset from folder_of_folders
+# we need to be able to find this out
+# target_class_idx = merged.labels.index(target_class)
+target_class_idx = 0
+
+results, all_logits = search.classifer_search_embeddings_parallel(
+    embeddings_classifier=model,
+    target_index=target_class_idx,
+    embeddings_dataset=embeddings_ds,
+    hop_size_s=embedding_hop_size_s,
+    target_score=target_logit,
+    top_k=num_results
+)
+
+# results is a results object from the chirp bootstrap
+print(results)
+
+results.write_labeled_data(config.annotated_path, embedding_model.sample_rat)
+
+# this is just a 1d np array. it's not very useful alone because we can't match the
+# index of this array to a particular file-offset without more info
+print(all_logits)
+
+
+# def plot_logits(target_class, all_logits, bins=128):
+  
+#     # Plot the histogram of logits.
+#     _, ys, _ = plt.hist(all_logits, bins=bins, density=True)
+#     plt.xlabel(f'{target_class} logit')
+#     plt.ylabel('density')
+#     # plt.yscale('log')
+#     plt.plot([target_logit, target_logit], [0.0, np.max(ys)], 'r:')
+#     plt.show()
+
+
+
+# #@title Display results for the target label. { vertical-output: true }
+
+# display_labels = merged.labels
+
+# extra_labels = []  #@param
+# for label in extra_labels:
+#   if label not in merged.labels:
+#     display_labels += (label,)
+# if 'unknown' not in merged.labels:
+#   display_labels += ('unknown',)
+
+# display.display_search_results(
+#     results, embedding_model.sample_rate,
+#     project_state.source_map,
+#     checkbox_labels=display_labels,
+#     max_workers=5)
+
+
+# #@title Add selected results to the labeled data. { vertical-output: true }
+
+# results.write_labeled_data(
+#     config.annotated_path, embedding_model.sample_rate)
+
+
+# #@title Write classifier inference CSV. { vertical-output: true }
+
+# threshold = 1.0  #@param
+# output_filepath = '/tmp/inference.csv'  #@param
+
+# # Create the embeddings dataset.
+# embeddings_ds = tf_examples.create_embeddings_dataset(
+#     embeddings_path, file_glob='embeddings-*')
+
+# def classify_batch(batch):
+#   """Classify a batch of embeddings."""
+#   emb = batch[tf_examples.EMBEDDING]
+#   emb_shape = tf.shape(emb)
+#   flat_emb = tf.reshape(emb, [-1, emb_shape[-1]])
+#   logits = model(flat_emb)
+#   logits = tf.reshape(
+#       logits, [emb_shape[0], emb_shape[1], tf.shape(logits)[-1]])
+#   # Take the maximum logit over channels.
+#   logits = tf.reduce_max(logits, axis=-2)
+#   batch['logits'] = logits
+#   return batch
+
+# inference_ds = tf_examples.create_embeddings_dataset(
+#     embeddings_path, file_glob='embeddings-*')
+# inference_ds = inference_ds.map(
+#     classify_batch, num_parallel_calls=tf.data.AUTOTUNE
+# )
+
+# with open(output_filepath, 'w') as f:
+#   # Write column headers.
+#   headers = ['filename', 'timestamp_s', 'label', 'logit']
+#   f.write(', '.join(headers) + '\n')
+#   for ex in tqdm.tqdm(inference_ds.as_numpy_iterator()):
+#     for t in range(ex['logits'].shape[0]):
+#       for i, label in enumerate(merged.class_names):
+#         if ex['logits'][t, i] > threshold:
+#           offset = ex['timestamp_s'] + t * config.embedding_hop_size_s
+#           logit = '{:.2f}'.format(ex['logits'][t, i])
+#           row = [ex['filename'].decode('utf-8'),
+#                  '{:.2f}'.format(offset),
+#                  label, logit]
+#           f.write(', '.join(row) + '\n')
\ No newline at end of file
diff --git a/src/retired/inference_slim.py b/src/retired/inference_slim.py
new file mode 100644
index 0000000..4ce5f57
--- /dev/null
+++ b/src/retired/inference_slim.py
@@ -0,0 +1,95 @@
+# in this file, we attempt to run inference with as few deps as possible
+# we we will drill into the classes and functions invoked by the active_learning notebook.
+# it may therefore have more code, as more code will need to be copy-pasted
+
+
+import keras
+import json
+from pathlib import Path
+
+# progress bar
+import tqdm
+
+import tensorflow as tf
+
+# utilities for mapping tf_examples. 
+# this is probably needed because it is used during the embedding stage
+# and for things like referring to keys in the embedding metadata we 
+# use constants defined here
+from chirp.inference import tf_examples
+
+model_path = "/output/trained_model.keras"
+embeddings_classifier = keras.models.load_model(model_path)
+
+
+# the embeddings path contains the embeddings files themselves plus a 
+# json file with some necessary?? metadata
+embeddings_path = '/phil/output/pw_embeddings_all/'
+embeddings_path = Path(embeddings_path)
+with (embeddings_path / 'config.json').open() as embeddings_config_json:
+  embeddings_config = json.loads(embeddings_config_json.read())
+
+print(embeddings_config)
+
+
+embeddings_ds = tf_examples.create_embeddings_dataset(
+    embeddings_path, file_glob='embeddings-*')
+
+
+
+def classify_batch(batch):
+    """
+    2nd map function
+    """
+    emb = batch[tf_examples.EMBEDDING]
+    emb_shape = tf.shape(emb)
+    flat_emb = tf.reshape(emb, [-1, emb_shape[-1]])
+    # this is where we actually run the forward pass
+    logits = embeddings_classifier(flat_emb)
+    logits = tf.reshape(
+        logits, [emb_shape[0], emb_shape[1], tf.shape(logits)[-1]]
+    )
+    # Restrict to target class.
+    # logits = logits[..., target_index]
+    # Take the maximum logit over channels.
+    logits = tf.reduce_max(logits, axis=-1)
+    batch['scores'] = logits
+    return batch
+
+# I don't think this actually processes anything yet, just 
+# adds the map function to the pipeline. docs say something about 
+# eager vs lazy which might have something to do with it
+embeddings_ds = embeddings_ds.map(classify_batch)
+
+
+all_distances = []
+all_results = []
+
+
+try:
+    # iterate over the examples, which are the embedding files
+    for ex in tqdm.tqdm(embeddings_ds.as_numpy_iterator()):
+      all_distances.append(ex['scores'].reshape([-1]))
+      
+      # iterate over the segments within the embedding file
+      for t in range(ex[tf_examples.EMBEDDING].shape[0]):
+        offset_s = t * embeddings_config["embed_fn_config"]["model_config"]["hop_size_s"] + ex[tf_examples.TIMESTAMP_S]
+ 
+        result = {
+           "embedding": ex[tf_examples.EMBEDDING][t, :, :],
+           "score": ex['scores'][t],
+           "filename": ex['filename'].decode(),
+           "offset_seconds": offset_s
+        }
+        
+        print(f'{result["filename"]} {result["offset_seconds"]}')
+        # print('.', end='')
+
+        all_results.append(result)
+
+      # print('+', end='')
+   
+except KeyboardInterrupt:
+    pass
+
+
diff --git a/src/retired/train_linear_model.py b/src/retired/train_linear_model.py
new file mode 100644
index 0000000..38cade0
--- /dev/null
+++ b/src/retired/train_linear_model.py
@@ -0,0 +1,134 @@
+# based off https://github.com/google-research/perch/blob/main/active_learning.ipynb
+
+# Global imports
+import collections
+import json
+from ml_collections import config_dict
+import numpy as np
+import tensorflow as tf
+from etils import epath
+import matplotlib.pyplot as plt
+
+from chirp.inference import colab_utils
+colab_utils.initialize(use_tf_gpu=True, disable_warnings=True)
+
+# Chirp imports
+from chirp.models import metrics
+from chirp.inference import tf_examples
+from chirp.projects.bootstrap import bootstrap
+from chirp.projects.bootstrap import display
+from chirp.projects.bootstrap import search
+from chirp.projects.multicluster import classify
+from chirp.projects.multicluster import data_lib
+
+# embeddings of unlabelled audio. 
+# why is this needed?? basically just because there is a combined config object
+# that holds info for both the embeddings and the labelled data path
+embeddings_path = '/phil/output/pw_embeddings_all/'  #@param
+embeddings_path = '/phil/output/site_039/'  #@param
+
+#labeled_data_path = '/phil/plains_wanderer_labelled/sorted_no_raven/'  #@param
+labeled_data_path = '/phil/plains_wanderer_labelled/tiny_sample/'  #@param
+
+# Get relevant info from the embedding configuration.
+embeddings_path = epath.Path(embeddings_path)
+with (embeddings_path / 'config.json').open() as f:
+  embedding_config = config_dict.ConfigDict(json.loads(f.read()))
+embeddings_glob = embeddings_path / 'embeddings-*'
+embedding_hop_size_s = embedding_config.embed_fn_config.model_config.hop_size_s
+
+config = bootstrap.BootstrapConfig.load_from_embedding_config(
+    embeddings_path=embeddings_path,
+    annotated_path=labeled_data_path)
+
+# this object holds 
+# - the config 
+# - the loaded model
+# - the embeddings 'dataset'. I think this is a tf dataset of the unlabelled embeddings
+# - a 'source map' which is a dict that maps the path to the original audio stored in the 
+#   embedding metadata (the saved embedding file) to the full path to the audio.  
+project_state = bootstrap.BootstrapState(config)
+embedding_model = project_state.embedding_model
+
+
+def supervised_learning():
+    """
+    This is based on everything under the Supervised Learning heading in the notebook
+    """
+
+    # Time-pooling strategy for examples longer than the model's window size.
+    time_pooling = 'mean'  #@param
+
+    # this performs the embeddings on the labelled set, so it takes a while
+    # todo: save these to disk
+    merged = data_lib.MergedDataset.from_folder_of_folders(
+        base_dir=labeled_data_path,
+        embedding_model=embedding_model,
+        time_pooling=time_pooling,
+        load_audio=False,
+        target_sample_rate=-2,
+        audio_file_pattern='*'
+    )
+
+    # Label distribution
+    lbl_counts = np.sum(merged.data['label_hot'], axis=0)
+    print('num classes :', (lbl_counts > 0).sum())
+    print('mean ex / class :', lbl_counts.sum() / (lbl_counts > 0).sum())
+    print('min ex / class :', (lbl_counts + (lbl_counts == 0) * 1e6).min())
+
+
+    # Number of random training examples to choose form each class.
+    train_ratio = 0.75
+    train_examples_per_class = None  #@param
+
+    # Number of random re-trainings. Allows judging model stability.
+    num_seeds = 1  #@param
+
+    # Classifier training hyperparams.
+    # These should be good defaults.
+    batch_size = 4
+    #num_epochs = 128
+    num_epochs = 8
+    num_hiddens = -1
+    learning_rate = 1e-3
+    
+    # creates a dictionary of lists where we can append to list by dictionary key
+    # and if the key is not present yet it will be created with an empty list
+    metrics2 = collections.defaultdict(list)
+    for seed in range(num_seeds):
+        if num_hiddens > 0:
+            model = classify.get_two_layer_model(num_hiddens, merged.embedding_dim, merged.num_classes, True)
+        else:
+            model = classify.get_linear_model(merged.embedding_dim, merged.num_classes)
+
+        run_metrics = classify.train_embedding_model(model, merged, train_ratio, train_examples_per_class, num_epochs, seed, batch_size, learning_rate)
+        metrics2['acc'].append(run_metrics.top1_accuracy)
+        metrics2['auc_roc'].append(run_metrics.auc_roc)
+        metrics2['cmap'].append(run_metrics.cmap_value)
+        metrics2['maps'].append(run_metrics.class_maps)
+        metrics2['test_logits'].append(run_metrics.test_logits)
+    mean_acc = np.mean(metrics2['acc'])
+    mean_auc = np.mean(metrics2['auc_roc'])
+    mean_cmap = np.mean(metrics2['cmap'])
+    # Merge the test_logits into a single array.
+    test_logits = {
+        k: np.concatenate([logits[k] for logits in metrics2['test_logits']])
+        for k in metrics2['test_logits'][0].keys()
+    }
+
+    print(f'acc:{mean_acc:5.2f}, auc_roc:{mean_auc:5.2f}, cmap:{mean_cmap:5.2f}')
+    for lbl, auc in zip(merged.labels, run_metrics.class_maps):
+      if np.isnan(auc):
+        continue
+      print(f'\n{lbl:8s}, auc_roc:{auc:5.2f}')
+      colab_utils.prstats(f'test_logits({lbl})', test_logits[merged.labels.index(lbl)])
+
+    return model
+
+
+
+model = supervised_learning()
+save_path = "/output/trained_model.keras"
+# https://www.tensorflow.org/guide/keras/serialization_and_saving
+model.save(save_path)
+print(model)
diff --git a/src/train_linear_model_slim.py b/src/train_linear_model_slim.py
new file mode 100644
index 0000000..87d34e9
--- /dev/null
+++ b/src/train_linear_model_slim.py
@@ -0,0 +1,171 @@
+# based off https://github.com/google-research/perch/blob/main/active_learning.ipynb
+
+import argparse
+
+
+# Global imports
+import collections
+import json
+from ml_collections import config_dict
+import numpy as np
+import tensorflow as tf
+from etils import epath
+import matplotlib.pyplot as plt
+
+from chirp.inference import colab_utils
+colab_utils.initialize(use_tf_gpu=True, disable_warnings=True)
+
+# Chirp imports
+from chirp.models import metrics
+from chirp.inference import tf_examples
+from chirp.projects.bootstrap import bootstrap
+from chirp.projects.bootstrap import display
+from chirp.projects.bootstrap import search
+from chirp.projects.multicluster import classify
+from chirp.projects.multicluster import data_lib
+
+from chirp.inference.models import TaxonomyModelTF
+
+
+# embeddings of unlabelled audio. 
+# why is this needed?? basically just because there is a combined config object
+# that holds info for both the embeddings and the labelled data path
+# embeddings_path = '/phil/output/pw_embeddings_all/'  #@param
+# embeddings_path = '/phil/output/site_039/'  #@param
+
+
+# # Get relevant info from the embedding configuration.
+# embeddings_path = epath.Path(embeddings_path)
+# with (embeddings_path / 'config.json').open() as f:
+#   embedding_config = config_dict.ConfigDict(json.loads(f.read()))
+# embeddings_glob = embeddings_path / 'embeddings-*'
+# embedding_hop_size_s = embedding_config.embed_fn_config.model_config.hop_size_s
+
+
+# config = bootstrap.BootstrapConfig.load_from_embedding_config(
+#     embeddings_path=embeddings_path,
+#     annotated_path=labeled_data_path)
+
+# # this object holds 
+# # - the config 
+# # - the loaded model
+# # - the embeddings 'dataset'. I think this is a tf dataset of the unlabelled embeddings
+# # - a 'source map' which is a dict that maps the path to the original audio stored in the 
+# #   embedding metadata (the saved embedding file) to the full path to the audio.  
+# project_state = bootstrap.BootstrapState(config)
+# embedding_model = project_state.embedding_model
+
+def get_model(model_path):
+   """
+   gets an object of class 'chirp.inference.models.TaxonomyModelTF'
+   which is what the from_folder_of_folders requires.
+   A bit of a hack, because to do this we require a config object   
+   """
+   model_config = config_dict.ConfigDict({
+        'hop_size_s': 5.0,
+        'model_path': model_path,
+        'sample_rate': 32000,
+        'window_size_s': 5.0,
+   })
+
+   taxonomy_model_tf = TaxonomyModelTF.from_config(model_config)
+
+   return taxonomy_model_tf
+
+
+
+def supervised_learning(labeled_data_path, embedding_model_version, train_params):
+    """
+    This is based on everything under the Supervised Learning heading in the notebook
+    """
+    embedding_model_path = f"/models/{embedding_model_version}"
+    embedding_model = get_model(embedding_model_path)
+
+    # Time-pooling strategy for examples longer than the model's window size.
+    time_pooling = 'mean'  #@param
+
+    # this performs the embeddings on the labelled set, so it takes a while
+    # todo: save these to disk
+    merged = data_lib.MergedDataset.from_folder_of_folders(
+        base_dir=labeled_data_path,
+        embedding_model=embedding_model,
+        time_pooling=time_pooling,
+        load_audio=False,
+        target_sample_rate=-2,
+        audio_file_pattern='*'
+    )
+
+    # Label distribution
+    lbl_counts = np.sum(merged.data['label_hot'], axis=0)
+    print('num classes :', (lbl_counts > 0).sum())
+    print('mean ex / class :', lbl_counts.sum() / (lbl_counts > 0).sum())
+    print('min ex / class :', (lbl_counts + (lbl_counts == 0) * 1e6).min())
+
+    
+    # creates a dictionary of lists where we can append to list by dictionary key
+    # and if the key is not present yet it will be created with an empty list
+    metrics2 = collections.defaultdict(list)
+    for seed in range(train_params["num_seeds"]):
+        if train_params["num_hiddens"] > 0:
+            model = classify.get_two_layer_model(train_params["num_hiddens"], merged.embedding_dim, merged.num_classes, True)
+        else:
+            model = classify.get_linear_model(merged.embedding_dim, merged.num_classes)
+
+        run_metrics = classify.train_embedding_model(model, merged, train_params["train_ratio"], 
+                                                     train_params["train_examples_per_class"], train_params["num_epochs"], seed, 
+                                                     train_params["batch_size"], train_params["learning_rate"])
+        metrics2['acc'].append(run_metrics.top1_accuracy)
+        metrics2['auc_roc'].append(run_metrics.auc_roc)
+        metrics2['cmap'].append(run_metrics.cmap_value)
+        metrics2['maps'].append(run_metrics.class_maps)
+        metrics2['test_logits'].append(run_metrics.test_logits)
+    mean_acc = np.mean(metrics2['acc'])
+    mean_auc = np.mean(metrics2['auc_roc'])
+    mean_cmap = np.mean(metrics2['cmap'])
+    # Merge the test_logits into a single array.
+    test_logits = {
+        k: np.concatenate([logits[k] for logits in metrics2['test_logits']])
+        for k in metrics2['test_logits'][0].keys()
+    }
+
+    print(f'acc:{mean_acc:5.2f}, auc_roc:{mean_auc:5.2f}, cmap:{mean_cmap:5.2f}')
+    for lbl, auc in zip(merged.labels, run_metrics.class_maps):
+      if np.isnan(auc):
+        continue
+      print(f'\n{lbl:8s}, auc_roc:{auc:5.2f}')
+      colab_utils.prstats(f'test_logits({lbl})', test_logits[merged.labels.index(lbl)])
+
+    return model, merged.labels
+
+# Classifier training hyperparams.
+# These should be good defaults.
+default_train_params = {
+    "batch_size" : 4,
+    "num_epochs" : 64,
+    "num_hiddens" : -1,
+    "learning_rate" : 1e-3,
+    "num_seeds" : 1,
+    "train_ratio" : 0.75,
+    "train_examples_per_class" : None
+}
+
+
+def train_and_save(labeled_data_path, output_file, embedding_model_version):
+    model, labels = supervised_learning(labeled_data_path=labeled_data_path, embedding_model_version=embedding_model_version, train_params=default_train_params)
+    # https://www.tensorflow.org/guide/keras/serialization_and_saving
+    model.save(output_file)
+    with open(output_file + '.labels.json', 'w') as f:
+        json.dump(labels, f)
+    print(model)
+    return model, labels
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--source", help="path to folder of folders")
+    parser.add_argument("--output_file", default='/output/trained_model.keras', help="where to save the model")
+    parser.add_argument("--embedding_model_version", default=4, help="path to embedding model")
+    args = parser.parse_args()
+    #config = config_dict.create(**vars(args))
+
+    train_and_save(args.source, args.output_file, int(args.embedding_model_version))
\ No newline at end of file
diff --git a/tests/Readme.md b/tests/Readme.md
new file mode 100644
index 0000000..e1bbb82
--- /dev/null
+++ b/tests/Readme.md
@@ -0,0 +1,30 @@
+This repository contains several parts
+- The App itself
+- Dockerfile for building the environment to run the App
+- Scripts to run from the docker host to launch the docker container. 
+
+We have two sets of tests. 
+
+- App Tests: 
+  - For testing the app iself
+  - These ensure the core logic and functionality of the application work as intended within its environment, which in most cases will be the docker container.
+- Docker Tests: 
+  - For testing the scripts that run the docker container
+  - These Verify that from the host the app is launched, files/directories are mounted, and the overall deployment process works without unexpected issues.
+
+There are some fixtures that are shared by both sets of tests, and both sets of tests make use of the input and output folders and source files.  
+
+- Input
+  - This is where we tell the app to find files to work on.
+
+- files
+  - This contains any test files that the app will work on. They are copied to the input folder before each test
+
+- Output
+  - This is where we tell the app to write results files
+  - When running app-tests, this must be mounted to a specific location that the app expects
+
+The input and output folders
+- When running app-tests in the container, these must be mounted to a specific that the app expects input
+- When running docker-tests, this is passed to the scripts that launch the container, which mount them to the location that the app expects
+- These folders are both cleaned up after each test
\ No newline at end of file
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/app_tests/__init__.py b/tests/app_tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/app_tests/conftest.py b/tests/app_tests/conftest.py
new file mode 100644
index 0000000..4c250d8
--- /dev/null
+++ b/tests/app_tests/conftest.py
@@ -0,0 +1,7 @@
+# pytest_plugins = [
+#   "tests.app_test.fixtures.embeddings",
+#   "tests.app_test.fixtures.clear_output_files"
+# ]
+
+from .fixtures.embeddings import *  # noqa: F401
+# from .fixtures.clear_output_files import *  # noqa: F401
\ No newline at end of file
diff --git a/tests/app_tests/fixtures/__init__.py b/tests/app_tests/fixtures/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/app_tests/fixtures/embeddings.py b/tests/app_tests/fixtures/embeddings.py
new file mode 100644
index 0000000..cf68339
--- /dev/null
+++ b/tests/app_tests/fixtures/embeddings.py
@@ -0,0 +1,35 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+
+@pytest.fixture(scope="session")
+def random_embeddings_01() -> np.array:
+    # a random np array with 3 channels, 123 frames
+    num_frames = 12
+    embeddings = np.random.rand(num_frames, 3, 1280).astype(np.float32)
+    hop_size = 5
+    offsets = np.repeat(np.arange(0,embeddings.shape[0]*hop_size,hop_size, dtype=np.float32), (3)).reshape(embeddings.shape[0],3,1)
+    return np.concatenate((offsets, embeddings), axis=2)
+
+
+   
+
+# Fixture for generating a random DataFrame
+@pytest.fixture
+def random_dataframe():
+    # Generate a random DataFrame based on your specifications
+    # For this test, you need to ensure that this DataFrame
+    # is in the correct format and can be converted back into an embeddings array.
+    # Example:
+    n_segments = 100  # for example
+    n_channels = 2   # for example
+    n_features = 1281  # for example
+
+    # Generating random data
+    data = np.random.rand(n_segments * n_channels, n_features + 2)
+    df = pd.DataFrame(data)
+    df['channel'] = np.repeat(range(n_channels), n_segments)
+    df.columns = ['offset'] + ['channel'] + [f'f{i:04d}' for i in range(n_features)]
+
+    return df
\ No newline at end of file
diff --git a/tests/app_tests/test_app.py b/tests/app_tests/test_app.py
new file mode 100644
index 0000000..2de7198
--- /dev/null
+++ b/tests/app_tests/test_app.py
@@ -0,0 +1,41 @@
+from pathlib import Path
+from pytest_mock import mocker 
+
+from src.app import main
+from src import batch
+from ml_collections import ConfigDict
+
+import inspect
+
+def test_embed_command(mocker) -> None:
+
+    mocker.patch('sys.argv', ['some_file_name.py', 'generate', '--source_file', 'tests/files/100sec.wav', '--output_folder', 'tests/output/'])
+
+    mocked_embed_file_and_save = mocker.patch('src.app.embed_file_and_save')
+
+    mocked_embed_file_and_save.return_value = "hi there"
+    
+    main()
+    
+    mocked_embed_file_and_save.assert_called_once_with('tests/files/100sec.wav', 'tests/output/', ConfigDict(**{}))
+
+
+
+def test_batch_entrypoint_item_0_1():
+    """Test batch processing from row 0 to 1 of the batch list"""
+
+    batch.batch('generate', source_csv='tests/files/batch_lists/batch_embed.csv', start_row=0, end_row=1, config_file=None)
+
+    assert Path('tests/output/100sec.parquet').exists()
+    assert Path('tests/output/some_subfolder/200sec.parquet').exists()
+    assert not Path('tests/output/some_subfolder/100sec_again.parquet').exists()
+
+
+def test_batch_entrypoint_item_2():
+    """Test batch processing from row 2 to 2 of the batch list"""
+
+    batch.batch('generate', source_csv='tests/files/batch_lists/batch_embed.csv', start_row=2, end_row=2, config_file=None)
+
+    assert not Path('tests/output/100sec.parquet').exists()
+    assert not Path('tests/output/some_subfolder/200sec.parquet').exists()
+    assert Path('tests/output/some_subfolder/100sec_again.parquet').exists()
diff --git a/tests/app_tests/test_classify.py b/tests/app_tests/test_classify.py
new file mode 100644
index 0000000..6d59dd7
--- /dev/null
+++ b/tests/app_tests/test_classify.py
@@ -0,0 +1,138 @@
+
+# import os
+# import shutil
+from pathlib import Path
+import pytest
+
+import pandas as pd
+
+from src import inference_parquet
+from ml_collections import config_dict
+from src.config import load_config
+
+valid_model_paths = ('pw', 
+            'pw/trained_model_pw_02.keras', 
+            '/models/pw',
+            '/models/pw/trained_model_pw_02.keras',
+            'tests/files/models/r1', 
+            'tests/files/models/r1/r1.keras')
+
+invalid_model_paths = ('nothing', '/app/models/nothing', 'tests/files/models/r1/nothing.keras')
+
+
+def test_find_model_file():
+
+    for path in valid_model_paths:
+        model_file, labels_file = inference_parquet.find_model(path)
+        assert model_file.exists()
+        assert labels_file.exists()
+    
+def test_dont_find_model():
+
+    for path in invalid_model_paths:
+        model_file, labels_file = inference_parquet.find_model(path)
+        assert model_file is None
+        assert labels_file is None
+
+def test_load_model():
+
+    for path in valid_model_paths:
+        classifier = inference_parquet.load_classifier(path)
+        assert classifier.model is not None
+        assert classifier.labels is not None
+
+def test_load_missing_model():
+
+    for path in invalid_model_paths:
+
+        with pytest.raises(ValueError) as e_info:
+            classifier = inference_parquet.load_classifier(path)
+        
+        assert str(e_info.value) == f"no keras file found at {path}"
+
+
+def test_classify_folder():
+
+    source_folder = "tests/files/embeddings"
+    output_folder = "tests/output"
+    classifier = "pw"
+
+    inference_parquet.process_folder(source_folder, output_folder, 
+                                         config_dict.create(classifier=classifier, skip_if_file_exists=True))
+
+    expected_files = [Path(output_folder) / Path("100sec.csv"), 
+                      Path(output_folder) / Path("200sec.csv")]
+    
+    assert expected_files[0].exists()
+    assert expected_files[1].exists()
+
+
+def test_classify_one_file():
+
+    source_file = "tests/files/embeddings/100sec.parquet"
+    output_folder = "tests/output"
+    config_file = "pw.classify.yml"
+    
+    config = load_config(config_file)
+
+    inference_parquet.classify_file_and_save(source_file, output_folder, config=config)
+
+    expected_file = Path(output_folder) / Path("100sec.csv")
+    
+    assert expected_file.exists()
+
+
+
+def test_classify_one_embeddings_file():
+
+    classifier = "pw"
+    results = inference_parquet.classify_embeddings_file("tests/files/embeddings/100sec.parquet", classifier)
+    #TODO: check rows and num columns
+    assert isinstance(results, pd.DataFrame)
+    assert list(results.columns) == ['filename', 'offset_seconds', 'neg', 'pos']
+    assert results.shape[0] == 20 # 100 seconds at 5 second intervals
+
+
+
+# def test_classify_one_file_and_save():
+    
+#     source = "tests/files/100sec.wav.parquet"
+#     destination = "tests/output/100sec_embeddings.parquet"
+#     model = "pw"
+
+#     inference_parquet.classify_file_and_save(source, destination, model)
+
+#     assert os.path.exists(destination)
+#     #TODO: check rows and num columns
+
+
+# def test_classify_files():
+
+#     # set up input folder
+
+#     one = Path("tests/input/embeddings/one")
+#     two = Path("tests/input/embeddings/two")
+#     one.mkdir(parents=True, exist_ok=True)
+#     two.mkdir(parents=True, exist_ok=True)
+#     shutil.copy("tests/files/embeddings/100sec.wav.parquet", one)
+#     shutil.copy("tests/files/embeddings/100sec.wav.parquet", two)
+
+#     source_folder = "tests/input/embeddings"
+#     output_folder = "tests/output"
+
+#     inference_parquet.classify_files(source_folder, output_folder)
+
+#     expected_files = [Path(output_folder) / Path("one/100sec.csv"), 
+#                       Path(output_folder) / Path("two/100sec.csv")]
+
+#     assert os.path.exists(expected_files[0])
+#     assert os.path.exists(expected_files[1])
+    
+#     # now read the embeddings back and check the shape and source columns are as expected
+#     df = pd.read_csv(expected_files[0])
+#     # TODO: assert shape 
+#     # assert df.shape == (20, 1283)
+#     # assert df.source[0] == "one/100sec.wav"
+
+
+# # for now we don't need to batch up classifications, because that's pretty fast if we have embeddings
\ No newline at end of file
diff --git a/tests/app_tests/test_config.py b/tests/app_tests/test_config.py
new file mode 100644
index 0000000..bcf0fe0
--- /dev/null
+++ b/tests/app_tests/test_config.py
@@ -0,0 +1,63 @@
+import pytest
+from src.config import load_config , config_locations
+
+# Assume test configs are in a subdirectory of the current test directory named 'test_configs'
+
+TEST_CONFIGS_DIR = "tests/files/configs/"
+
+# Fixture to modify locations
+@pytest.fixture(autouse=True)
+def modify_locations():
+    """
+    Automatically use this fixture in every test to modify the global
+    locations variable to include only the temporary test directory.
+    """
+    # Store the original locations
+    original_locations = config_locations.copy()
+    
+    # Update locations to only include a temporary directory for testing
+    config_locations.clear()
+    config_locations.append(TEST_CONFIGS_DIR)
+    config_locations.append("")
+
+    # This part runs after the test has finished
+    yield
+
+    # Reset locations to its original state
+    config_locations.clear()
+    config_locations.extend(original_locations)
+
+
+def test_load_simple_config():
+    config = load_config(f"{TEST_CONFIGS_DIR}base.yml")
+    assert config.parameter1 == 'value1'
+    assert config.parameter2 == 'value2'
+    assert config.parameter3 == 'value3'
+
+def test_load_extended_config():
+    config = load_config(f"{TEST_CONFIGS_DIR}extend.yml")
+    assert config.parameter1 == 'value1'
+    assert config.parameter2 == 'overridden_value2'
+    assert config.parameter3 == 'overridden_value3'
+    assert config.parameter4 == 'value4'
+    assert config.parameter5 == 'value5'
+
+
+def test_load_extended_again_config():
+    config = load_config(f"extend_again.yml")
+    assert config.parameter1 == 'value1'
+    assert config.parameter2 == 'overridden_twice_value2'
+    assert config.parameter3 == 'overridden_value3'
+    assert config.parameter4 == 'overridden_value4'
+    assert config.parameter5 == 'value5'
+    assert 'inherit' not in config
+
+
+def test_circular_reference_detection():
+    with pytest.raises(ValueError, match=r"circular reference detected"):
+        load_config(f"{TEST_CONFIGS_DIR}circular_a.yml")
+
+
+def test_file_not_found():
+    with pytest.raises(FileNotFoundError):
+        load_config("non_existent_file.yml")
\ No newline at end of file
diff --git a/tests/app_tests/test_data_frames.py b/tests/app_tests/test_data_frames.py
new file mode 100644
index 0000000..485485f
--- /dev/null
+++ b/tests/app_tests/test_data_frames.py
@@ -0,0 +1,77 @@
+
+import os
+import numpy as np
+import pytest
+
+from src import data_frames
+
+
+
+def test_embeddings_to_df(random_embeddings_01) -> None:
+
+    result = data_frames.embeddings_to_df(random_embeddings_01)
+    expected_shape = (random_embeddings_01.shape[0] * random_embeddings_01.shape[1], random_embeddings_01.shape[2] + 1)
+    assert result.shape == expected_shape
+    assert result.iloc[0,2] == random_embeddings_01[0,0,1]
+
+
+
+def test_embeddings_conversion_consistency(random_embeddings_01):
+    # Convert embeddings to DataFrame
+    df = data_frames.embeddings_to_df(random_embeddings_01)
+
+    # Convert the DataFrame back to embeddings
+    converted_embeddings = data_frames.df_to_embeddings(df)
+
+    # Check if the converted embeddings match the original ones
+    np.testing.assert_array_almost_equal(random_embeddings_01, converted_embeddings)
+
+
+
+def test_serialize_embeddings(random_embeddings_01) -> None:
+
+    embeddings_df = data_frames.embeddings_to_df(random_embeddings_01)
+    serialized_embeddings_df = data_frames.serialize_embeddings_df(embeddings_df)
+    num_rows, num_columns = serialized_embeddings_df.shape
+    # channel, offset, features
+    expected_num_columns = 3
+    assert num_rows == embeddings_df.shape[0] and num_columns == 3
+
+
+def test_deserialize_embeddings(random_embeddings_01) -> None:
+
+    embeddings_df = data_frames.embeddings_to_df(random_embeddings_01)
+    serialized_embeddings_df = data_frames.serialize_embeddings_df(embeddings_df)
+    deserialized_embeddings_df = data_frames.deserialize_embeddings_df(serialized_embeddings_df)
+
+    # checks that all elements of the original match the corresponding element of the deserialized
+    assert (embeddings_df == deserialized_embeddings_df).all().all()
+
+
+def test_serialize_array_wrong_type_1() -> None:
+
+    raw = [1.234, 2.345, 3.456]
+    # we always assume 32 bit floats
+    with pytest.raises(TypeError, match="Value must be a <class 'numpy.float32'> array, but <class 'list'> was given"):
+        data_frames.serialize_array(raw)
+
+
+def test_serialize_array_wrong_type_2() -> None:
+
+    raw = np.array([1.234, 2.345, 3.456])
+    # we always assume 32 bit floats
+    with pytest.raises(TypeError, match="Value must be a <class 'numpy.float32'> array, but <class 'numpy.ndarray'> float64 was given"):
+        data_frames.serialize_array(raw)
+
+
+def test_serialize_array() -> None:
+
+    raw = np.array([1.234, 2.345, 3.456], dtype=np.float32)
+    #raw = np.array([1.234], dtype=np.float32)
+    dtype=np.float32
+    #raw = np.array([1], dtype=dtype)
+    base64 = 'tvOdP3sUFkAbL11A'
+    serialized = data_frames.serialize_array(raw)
+    assert serialized == base64
+    deserialized = data_frames.deserialize_array(serialized, dtype)
+    assert np.array_equal(deserialized, raw)
\ No newline at end of file
diff --git a/tests/app_tests/test_embed.py b/tests/app_tests/test_embed.py
new file mode 100644
index 0000000..1e123f6
--- /dev/null
+++ b/tests/app_tests/test_embed.py
@@ -0,0 +1,75 @@
+
+import os
+import shutil
+from pathlib import Path
+
+import pandas as pd
+
+from src import embed_audio_slim
+
+from ml_collections import config_dict
+
+
+def test_embed_one_file():
+    """
+    tests both the embedding and the saving of the embeddings to csv
+    """
+
+    embeddings = embed_audio_slim.embed_one_file("tests/files/audio/100sec.wav")
+
+    destination = "tests/output/100sec_embeddings.csv"
+    embed_audio_slim.save_embeddings(embeddings, destination)
+
+    assert os.path.exists(destination)
+    assert len(embeddings.shape) == 3
+
+
+def test_embed_one_file_and_save():
+    
+    source = "tests/files/audio/100sec.wav"
+    destination = "tests/output/100sec_embeddings.parquet"
+
+    embed_audio_slim.embed_file_and_save(source, destination)
+
+    assert os.path.exists(destination)
+
+
+def test_embed_one_file_and_save_to_folder():
+    
+    source = "tests/files/audio/100sec.wav"
+    destination = "tests/output/"
+
+    embed_audio_slim.embed_file_and_save(source, destination)
+
+    assert os.path.exists(destination + "100sec.parquet")
+
+
+
+def test_embed_folder():
+
+    # set up input folder
+
+    one = Path("tests/input/files/one")
+    two = Path("tests/input/files/two")
+    one.mkdir(parents=True, exist_ok=True)
+    two.mkdir(parents=True, exist_ok=True)
+    shutil.copy("tests/files/audio/100sec.wav", one)
+    shutil.copy("tests/files/audio/100sec.wav", two)
+
+    source_folder = "tests/input/files"
+    output_folder = "tests/output"
+
+    embed_audio_slim.embed_folder(source_folder, output_folder)
+
+    expected_files = [Path(output_folder) / Path("one/100sec.parquet"), 
+                      Path(output_folder) / Path("two/100sec.parquet")]
+
+    assert os.path.exists(expected_files[0])
+    assert os.path.exists(expected_files[0])
+    
+    # now read the embeddings back and check the shape and source columns are as expected
+    df = pd.read_parquet(expected_files[0])
+    assert df.shape == (20, 1283)
+    assert df.source[0] == "one/100sec.wav"
+
+
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..37052a5
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,10 @@
+# Note: I could only get the clear_output_files autouse fixture to work by defining in pytest_plugins, rather than by direct import. 
+# pytest_plugins can only be used in the top level conftest.py file, not in any other nested conftest.py file.
+
+pytest_plugins = [
+  "tests.shared_fixtures.clear_output_files",
+  "tests.shared_fixtures.helpers"
+]
+
+
+
diff --git a/tests/docker_tests/test_classify.py b/tests/docker_tests/test_classify.py
new file mode 100644
index 0000000..c407ff7
--- /dev/null
+++ b/tests/docker_tests/test_classify.py
@@ -0,0 +1,23 @@
+import subprocess
+from pathlib import Path
+import shutil
+
+def test_classify_script():
+
+    one = Path("tests/input/files/one")
+    two = Path("tests/input/files/two")
+    one.mkdir(parents=True, exist_ok=True)
+    two.mkdir(parents=True, exist_ok=True)
+    shutil.copy("tests/files/embeddings/100sec.parquet", one)
+    shutil.copy("tests/files/embeddings/100sec.parquet", two)
+
+    # Run the embed helper script
+    command = ["./scripts/classify.sh", "./tests/input/", "./tests/output/", "pw", "cr08"]
+    print(f'running command: {" ".join(command)}')
+    subprocess.run(command, check=True)
+
+    # Specify the expected output files
+    expected_output_files = ["./tests/output/files/one/100sec.csv", "./tests/output/files/two/100sec.csv"] 
+
+    for file in expected_output_files:
+        assert Path(file).exists()
diff --git a/tests/docker_tests/test_embed.py b/tests/docker_tests/test_embed.py
new file mode 100644
index 0000000..8fbf18a
--- /dev/null
+++ b/tests/docker_tests/test_embed.py
@@ -0,0 +1,16 @@
+import pytest
+import subprocess
+from pathlib import Path
+
+def test_embed_script():
+
+    # Run the embed helper script
+    command = ["./scripts/embed.sh", "./tests/files/audio/100sec.wav", "./tests/output/", "cr08"]
+    print(f'running command: {" ".join(command)}')
+    subprocess.run(command, check=True)
+
+    # Specify the expected output files
+    expected_output_files = ["./tests/output/100sec.parquet"] 
+
+    for file in expected_output_files:
+        assert Path(file).exists()
\ No newline at end of file
diff --git a/tests/files/audio/100sec.wav b/tests/files/audio/100sec.wav
new file mode 100644
index 0000000..fb7f118
Binary files /dev/null and b/tests/files/audio/100sec.wav differ
diff --git a/tests/files/audio/200sec.wav b/tests/files/audio/200sec.wav
new file mode 100644
index 0000000..5eb7ec0
Binary files /dev/null and b/tests/files/audio/200sec.wav differ
diff --git a/tests/files/batch_lists/batch_embed.csv b/tests/files/batch_lists/batch_embed.csv
new file mode 100644
index 0000000..f231727
--- /dev/null
+++ b/tests/files/batch_lists/batch_embed.csv
@@ -0,0 +1,4 @@
+source,output
+tests/files/audio/100sec.wav,tests/output/100sec.parquet
+tests/files/audio/200sec.wav,tests/output/some_subfolder/200sec.parquet
+tests/files/audio/100sec.wav,tests/output/some_subfolder/100sec_again.parquet
diff --git a/tests/files/configs/base.yml b/tests/files/configs/base.yml
new file mode 100644
index 0000000..d4a07a8
--- /dev/null
+++ b/tests/files/configs/base.yml
@@ -0,0 +1,3 @@
+parameter1: value1
+parameter2: value2
+parameter3: value3
\ No newline at end of file
diff --git a/tests/files/configs/circular_a.yml b/tests/files/configs/circular_a.yml
new file mode 100644
index 0000000..6dfb808
--- /dev/null
+++ b/tests/files/configs/circular_a.yml
@@ -0,0 +1,2 @@
+inherit: circular_b.yml
+parameter: valueA
\ No newline at end of file
diff --git a/tests/files/configs/circular_b.yml b/tests/files/configs/circular_b.yml
new file mode 100644
index 0000000..9dcf249
--- /dev/null
+++ b/tests/files/configs/circular_b.yml
@@ -0,0 +1,2 @@
+inherit: circular_a.yml
+parameter: valueB
\ No newline at end of file
diff --git a/tests/files/configs/embed_1.yml b/tests/files/configs/embed_1.yml
new file mode 100644
index 0000000..2bf51a1
--- /dev/null
+++ b/tests/files/configs/embed_1.yml
@@ -0,0 +1 @@
+hop_size_s: 5
\ No newline at end of file
diff --git a/tests/files/configs/extend.yml b/tests/files/configs/extend.yml
new file mode 100644
index 0000000..c7674d0
--- /dev/null
+++ b/tests/files/configs/extend.yml
@@ -0,0 +1,5 @@
+inherit: base.yml
+parameter2: overridden_value2
+parameter3: overridden_value3
+parameter4: value4
+parameter5: value5
diff --git a/tests/files/configs/extend_again.yml b/tests/files/configs/extend_again.yml
new file mode 100644
index 0000000..06c999a
--- /dev/null
+++ b/tests/files/configs/extend_again.yml
@@ -0,0 +1,4 @@
+inherit: extend.yml
+parameter2: overridden_twice_value2
+parameter4: overridden_value4
+parameter6: value6
\ No newline at end of file
diff --git a/tests/files/embeddings/100sec.parquet b/tests/files/embeddings/100sec.parquet
new file mode 100644
index 0000000..e16d3bd
Binary files /dev/null and b/tests/files/embeddings/100sec.parquet differ
diff --git a/tests/files/embeddings/200sec.parquet b/tests/files/embeddings/200sec.parquet
new file mode 100644
index 0000000..0f52dfd
Binary files /dev/null and b/tests/files/embeddings/200sec.parquet differ
diff --git a/tests/files/models/r1/r1.keras b/tests/files/models/r1/r1.keras
new file mode 100755
index 0000000..3cb4ada
Binary files /dev/null and b/tests/files/models/r1/r1.keras differ
diff --git a/tests/files/models/r1/r1.keras.labels.json b/tests/files/models/r1/r1.keras.labels.json
new file mode 100755
index 0000000..178d04f
--- /dev/null
+++ b/tests/files/models/r1/r1.keras.labels.json
@@ -0,0 +1 @@
+["neg", "pos"]
\ No newline at end of file
diff --git a/tests/input/.gitkeep b/tests/input/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/tests/shared_fixtures/__init__.py b/tests/shared_fixtures/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/shared_fixtures/clear_output_files.py b/tests/shared_fixtures/clear_output_files.py
new file mode 100644
index 0000000..70b3d6d
--- /dev/null
+++ b/tests/shared_fixtures/clear_output_files.py
@@ -0,0 +1,31 @@
+import os
+import shutil
+from pytest import fixture
+
+
+@fixture(autouse=True)
+def _run_around_tests() -> None:
+  """Automatically called fixture that clears output files generated by tests."""
+  print("\nAutouse fixture is invoked!")
+  clear_files("tests/output")
+  clear_files("tests/input")
+  yield
+  clear_files("tests/output")
+  clear_files("tests/input")
+
+
+def clear_files(path) -> None:
+  """Deletes files generated by tests."""
+
+  contents = [item for item in os.listdir(path) if item != ".gitkeep"]
+
+  for item in contents:
+    item_path = os.path.join(path, item)
+    try:
+      #try to remove a directory (with contents)
+      shutil.rmtree(item_path)
+      print(f"removed dir: {item_path}")
+    except OSError:
+      # or a file is not a directory
+      print(f"removing file: {item_path}")
+      os.remove(item_path)
\ No newline at end of file
diff --git a/tests/shared_fixtures/helpers.py b/tests/shared_fixtures/helpers.py
new file mode 100644
index 0000000..06e8946
--- /dev/null
+++ b/tests/shared_fixtures/helpers.py
@@ -0,0 +1,13 @@
+from pathlib import Path
+import shutil
+
+def copy_test_files(files):
+    """
+    Helper function to copy files from the 'files' to the 'input' testing directory
+    """
+    input_dir = Path("./tests/input")
+    source_dir = Path("./tests/files")
+    for file in files:
+        source_path = source_dir / file
+        dest_path = input_dir / file
+        shutil.copyfile(source_path, dest_path)
\ No newline at end of file