Containerize transcription demo

Dockerfile

@@ -0,0 +1,31 @@
+FROM python:3.9-slim AS builder
+
+WORKDIR /build
+
+# Install system build dependencies
+RUN apt-get update && apt-get install -y build-essential
+
+# Install Python build dependencies
+RUN pip install pybind11[global]
+
+# Copy application code
+COPY . .
+
+# Build project's wheels
+RUN pip wheel --no-cache-dir --wheel-dir=/wheels -e .
+
+
+FROM python:3.9-slim
+
+WORKDIR /app
+
+# Install required libraries
+RUN apt-get update && apt-get install -y libgomp1 && \
+    apt-get clean && rm -rf /var/lib/apt/lists/*
+
+# Install the application
+RUN --mount=type=bind,from=builder,source=/wheels,target=/wheels \
+    pip install --no-cache-dir --no-index --find-links=/wheels useful_transformers
+
+# Set the application as the entrypoint
+ENTRYPOINT ["taskset", "-c", "4-7", "python", "-m", "useful_transformers.transcribe_wav"]

README.md

@@ -17,6 +17,22 @@ If you don't have a wav file handy, running the above command will transcribe an
     $ taskset -c 4-7 python -m useful_transformers.transcribe_wav
     Ever tried, ever failed. No matter, try again. Fail again. Fail better.
 
+### Try in Docker
+
+You can also run the transcription in Docker with:
+```bash
+$ docker build -t useful-transformers .
+$ docker run --rm -t \
+    --device /dev/dri:/dev/dri \
+    -v "$(PWD)":/work useful-transformers \
+    /work/<wav_file in CWD>
+```
+
+> **Note:** You can use `ffmpeg` to convert your audio file to a mono WAV file with a sample rate of 16 kHz:
+> ```bash
+> ffmpeg -i <input.*> -ac 1 -ar 16000 -f wav <output.wav>
+> ```
+
 ## Performance
 
 ![Performance comparison](https://github.com/usefulsensors/useful-transformers/blob/main/examples/whisper/assets/perf-comparison.png)

lib/profiler.cc

@@ -1,5 +1,6 @@
 #include "profiler.h"
 
+#include <algorithm>
 #include <cassert>
 #include <cmath>
 #include <iomanip>