diff --git a/examples/pyaudio-streaming/README.md b/examples/pyaudio-streaming/README.md new file mode 100644 index 0000000..75309a6 --- /dev/null +++ b/examples/pyaudio-streaming/README.md @@ -0,0 +1,11 @@ +# Pyaudio Streaming Example + +This example shows how micophone audio fetched by pyaudio can be processed with Silero-VAD. + +It has been designed as a low-level example for binary real-time streaming using only the prediction of the model, processing the binary data and plotting the speech probabilities at the end to visualize it. + + + + + + diff --git a/examples/pyaudio-streaming/pyaudio-streaming-examples.ipynb b/examples/pyaudio-streaming/pyaudio-streaming-examples.ipynb index b5840aa..1fb4add 100644 --- a/examples/pyaudio-streaming/pyaudio-streaming-examples.ipynb +++ b/examples/pyaudio-streaming/pyaudio-streaming-examples.ipynb @@ -9,28 +9,65 @@ "\n", "A simple notebook that uses pyaudio to get the microphone audio and feeds this audio then to Silero VAD.\n", "\n", - "I created it as an example on how binary data from a stream could be feed into Silero VAD." + "I created it as an example on how binary data from a stream could be feed into Silero VAD.\n", + "\n", + "\n", + "Has been tested on Ubuntu 21.04 (x86). After you installed the dependencies below, no additional setup is required." + ] + }, + { + "cell_type": "markdown", + "id": "64cbe1eb", + "metadata": {}, + "source": [ + "## Dependencies\n", + "The cell below lists all used dependencies and the used versions. Uncomment to install them from within the notebook." ] }, { "cell_type": "code", "execution_count": 1, + "id": "57bc2aac", + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install numpy==1.20.2\n", + "#!pip install torch==1.8.1\n", + "#!pip install matplotlib==3.4.2\n", + "#!pip install torchaudio==0.8.1\n", + "#!pip install soundfile==0.10.3.post1\n", + "#!pip install pyaudio==0.2.11" + ] + }, + { + "cell_type": "markdown", + "id": "110de761", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "5a647d8d", "metadata": {}, "outputs": [], "source": [ + "import io\n", "import numpy as np\n", "import torch\n", "torch.set_num_threads(1)\n", "import torchaudio\n", "import matplotlib\n", "import matplotlib.pylab as plt\n", - "torchaudio.set_audio_backend(\"soundfile\")" + "torchaudio.set_audio_backend(\"soundfile\")\n", + "import pyaudio" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "725d7066", "metadata": {}, "outputs": [ @@ -48,6 +85,22 @@ " force_reload=True)" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1c0b2ea7", + "metadata": {}, + "outputs": [], + "source": [ + "(get_speech_ts,\n", + " get_speech_ts_adaptive,\n", + " save_audio,\n", + " read_audio,\n", + " state_generator,\n", + " single_audio_stream,\n", + " collect_chunks) = utils" + ] + }, { "cell_type": "markdown", "id": "f9112603", @@ -58,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "5abc6330", "metadata": {}, "outputs": [], @@ -85,19 +138,16 @@ "id": "5124095e", "metadata": {}, "source": [ - "## Pyaudio" + "## Pyaudio Set-up" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "a845356e", "metadata": {}, "outputs": [], "source": [ - "import pyaudio\n", - "import io\n", - "\n", "FORMAT = pyaudio.paInt16\n", "CHANNELS = 1\n", "SAMPLE_RATE = 16000\n", @@ -117,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "9d3d2c10", "metadata": {}, "outputs": [], @@ -129,13 +179,21 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "3cb44a4a", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started Recording\n", + "Stopped the recording\n" + ] + }, { "data": { - "image/png": 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" ] @@ -155,7 +213,7 @@ "data = []\n", "voiced_confidences = []\n", "\n", - "\n", + "print(\"Started Recording\")\n", "for i in range(0, frames_to_record):\n", " \n", " audio_chunk = stream.read(int(SAMPLE_RATE * frame_duration_ms / 1000.0))\n", @@ -171,6 +229,8 @@ " vad_outs = validate(model, torch.from_numpy(audio_float32))\n", " # only keep the confidence for the speech\n", " voiced_confidences.append(vad_outs[:,1])\n", + " \n", + "print(\"Stopped the recording\")\n", "\n", "# plot the confidences for the speech\n", "plt.figure(figsize=(20,6))\n", @@ -181,7 +241,7 @@ { "cell_type": "code", "execution_count": null, - "id": "56b225f5", + "id": "430a343e", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/pyaudio-streaming/requirements.txt b/examples/pyaudio-streaming/requirements.txt new file mode 100644 index 0000000..e905375 --- /dev/null +++ b/examples/pyaudio-streaming/requirements.txt @@ -0,0 +1,65 @@ +argon2-cffi==20.1.0 +async-generator==1.10 +attrs==21.2.0 +backcall==0.2.0 +bleach==3.3.0 +cffi==1.14.5 +cycler==0.10.0 +decorator==5.0.7 +defusedxml==0.7.1 +entrypoints==0.3 +ipykernel==5.5.4 +ipython==7.23.1 +ipython-genutils==0.2.0 +ipywidgets==7.6.3 +jedi==0.18.0 +Jinja2==2.11.3 +jsonschema==3.2.0 +jupyter==1.0.0 +jupyter-client==6.1.12 +jupyter-console==6.4.0 +jupyter-core==4.7.1 +jupyterlab-pygments==0.1.2 +jupyterlab-widgets==1.0.0 +kiwisolver==1.3.1 +MarkupSafe==1.1.1 +matplotlib==3.4.2 +matplotlib-inline==0.1.2 +mistune==0.8.4 +nbclient==0.5.3 +nbconvert==6.0.7 +nbformat==5.1.3 +nest-asyncio==1.5.1 +notebook==6.3.0 +numpy==1.20.2 +packaging==20.9 +pandocfilters==1.4.3 +parso==0.8.2 +pexpect==4.8.0 +pickleshare==0.7.5 +Pillow==8.2.0 +prometheus-client==0.10.1 +prompt-toolkit==3.0.18 +ptyprocess==0.7.0 +PyAudio==0.2.11 +pycparser==2.20 +Pygments==2.9.0 +pyparsing==2.4.7 +pyrsistent==0.17.3 +python-dateutil==2.8.1 +pyzmq==22.0.3 +qtconsole==5.1.0 +QtPy==1.9.0 +Send2Trash==1.5.0 +six==1.16.0 +SoundFile==0.10.3.post1 +terminado==0.9.4 +testpath==0.4.4 +torch==1.8.1 +torchaudio==0.8.1 +tornado==6.1 +traitlets==5.0.5 +typing-extensions==3.10.0.0 +wcwidth==0.2.5 +webencodings==0.5.1 +widgetsnbextension==3.5.1