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diff --git a/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/README.md b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/README.md new file mode 100644 index 0000000000..4156112df2 --- /dev/null +++ b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/README.md @@ -0,0 +1,125 @@ +# APM Quality Assessment tool + +Python wrapper of APM simulators (e.g., `audioproc_f`) with which quality +assessment can be automatized. The tool allows to simulate different noise +conditions, input signals, APM configurations and it computes different scores. +Once the scores are computed, the results can be easily exported to an HTML page +which allows to listen to the APM input and output signals and also the +reference one used for evaluation. + +## Dependencies + - OS: Linux + - Python 2.7 + - Python libraries: enum34, numpy, scipy, pydub (0.17.0+), pandas (0.20.1+), + pyquery (1.2+), jsmin (2.2+), csscompressor (0.9.4) + - It is recommended that a dedicated Python environment is used + - install `virtualenv` + - `$ sudo apt-get install python-virtualenv` + - setup a new Python environment (e.g., `my_env`) + - `$ cd ~ && virtualenv my_env` + - activate the new Python environment + - `$ source ~/my_env/bin/activate` + - add dependcies via `pip` + - `(my_env)$ pip install enum34 numpy pydub scipy pandas pyquery jsmin \` + `csscompressor` + - PolqaOem64 (see http://www.polqa.info/) + - Tested with POLQA Library v1.180 / P863 v2.400 + - Aachen Impulse Response (AIR) Database + - Download https://www2.iks.rwth-aachen.de/air/air_database_release_1_4.zip + - Input probing signals and noise tracks (you can make your own dataset - *1) + +## Build + - Compile WebRTC + - Go to `out/Default/py_quality_assessment` and check that + `apm_quality_assessment.py` exists + +## Unit tests + - Compile WebRTC + - Go to `out/Default/py_quality_assessment` + - Run `python -m unittest discover -p "*_unittest.py"` + +## First time setup + - Deploy PolqaOem64 and set the `POLQA_PATH` environment variable + - e.g., `$ export POLQA_PATH=/var/opt/PolqaOem64` + - Deploy the AIR Database and set the `AECHEN_IR_DATABASE_PATH` environment + variable + - e.g., `$ export AECHEN_IR_DATABASE_PATH=/var/opt/AIR_1_4` + - Deploy probing signal tracks into + - `out/Default/py_quality_assessment/probing_signals` (*1) + - Deploy noise tracks into + - `out/Default/py_quality_assessment/noise_tracks` (*1, *2) + +(*1) You can use custom files as long as they are mono tracks sampled at 48kHz +encoded in the 16 bit signed format (it is recommended that the tracks are +converted and exported with Audacity). + +## Usage (scores computation) + - Go to `out/Default/py_quality_assessment` + - Check the `apm_quality_assessment.sh` as an example script to parallelize the + experiments + - Adjust the script according to your preferences (e.g., output path) + - Run `apm_quality_assessment.sh` + - The script will end by opening the browser and showing ALL the computed + scores + +## Usage (export reports) +Showing all the results at once can be confusing. You therefore may want to +export separate reports. In this case, you can use the +`apm_quality_assessment_export.py` script as follows: + + - Set `--output_dir, -o` to the same value used in `apm_quality_assessment.sh` + - Use regular expressions to select/filter out scores by + - APM configurations: `--config_names, -c` + - capture signals: `--capture_names, -i` + - render signals: `--render_names, -r` + - echo simulator: `--echo_simulator_names, -e` + - test data generators: `--test_data_generators, -t` + - scores: `--eval_scores, -s` + - Assign a suffix to the report name using `-f <suffix>` + +For instance: + +``` +$ ./apm_quality_assessment_export.py \ + -o output/ \ + -c "(^default$)|(.*AE.*)" \ + -t \(white_noise\) \ + -s \(polqa\) \ + -f echo +``` + +## Usage (boxplot) +After generating stats, it can help to visualize how a score depends on a +certain APM simulator parameter. The `apm_quality_assessment_boxplot.py` script +helps with that, producing plots similar to [this +one](https://matplotlib.org/mpl_examples/pylab_examples/boxplot_demo_06.png). + +Suppose some scores come from running the APM simulator `audioproc_f` with +or without the level controller: `--lc=1` or `--lc=0`. Then two boxplots +side by side can be generated with + +``` +$ ./apm_quality_assessment_boxplot.py \ + -o /path/to/output + -v <score_name> + -n /path/to/dir/with/apm_configs + -z lc +``` + +## Troubleshooting +The input wav file must be: + - sampled at a sample rate that is a multiple of 100 (required by POLQA) + - in the 16 bit format (required by `audioproc_f`) + - encoded in the Microsoft WAV signed 16 bit PCM format (Audacity default + when exporting) + +Depending on the license, the POLQA tool may take “breaks” as a way to limit the +throughput. When this happens, the APM Quality Assessment tool is slowed down. +For more details about this limitation, check Section 10.9.1 in the POLQA manual +v.1.18. + +In case of issues with the POLQA score computation, check +`py_quality_assessment/eval_scores.py` and adapt +`PolqaScore._parse_output_file()`. +The code can be also fixed directly into the build directory (namely, +`out/Default/py_quality_assessment/eval_scores.py`). |