{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Speaker count Detection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This tutorial is available as an IPython notebook at [malaya-speech/example/speaker-count](https://github.com/huseinzol05/malaya-speech/tree/master/example/speaker-count).\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This module is language independent, so it save to use on different languages.\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This is an application of malaya-speech Pipeline, read more about malaya-speech Pipeline at [malaya-speech/example/pipeline](https://github.com/huseinzol05/malaya-speech/tree/master/example/pipeline).\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset\n", "\n", "Trained on Musan Speech, VCTK, LibriSpeech, Mandarin speakers and Malaya-Speech TTS dataset to detect number of speakers." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "os.environ['CUDA_VISIBLE_DEVICES'] = ''" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-09-01 12:10:43.857159: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2023-09-01 12:10:45.110317: I tensorflow/core/util/port.cc:104] 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", "2023-09-01 12:10:46.035942: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", "2023-09-01 12:10:46.035979: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", "2023-09-01 12:10:46.035982: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n", "/home/husein/.local/lib/python3.8/site-packages/tensorflow_addons/utils/ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.4.0 and strictly below 2.7.0 (nightly versions are not supported). \n", " The versions of TensorFlow you are currently using is 2.11.0 and is not supported. \n", "Some things might work, some things might not.\n", "If you were to encounter a bug, do not file an issue.\n", "If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n", "You can find the compatibility matrix in TensorFlow Addon's readme:\n", "https://github.com/tensorflow/addons\n", " warnings.warn(\n", "/home/husein/.local/lib/python3.8/site-packages/tensorflow_addons/utils/resource_loader.py:78: UserWarning: You are currently using TensorFlow 2.11.0 and trying to load a custom op (custom_ops/seq2seq/_beam_search_ops.so).\n", "TensorFlow Addons has compiled its custom ops against TensorFlow 2.6.0, and there are no compatibility guarantees between the two versions. \n", "This means that you might get segfaults when loading the custom op, or other kind of low-level errors.\n", " If you do, do not file an issue on Github. This is a known limitation.\n", "\n", "It might help you to fallback to pure Python ops by setting environment variable `TF_ADDONS_PY_OPS=1` or using `tfa.options.disable_custom_kernel()` in your code. To do that, see https://github.com/tensorflow/addons#gpucpu-custom-ops \n", "\n", "You can also change the TensorFlow version installed on your system. You would need a TensorFlow version equal to or above 2.6.0 and strictly below 2.7.0.\n", " Note that nightly versions of TensorFlow, as well as non-pip TensorFlow like `conda install tensorflow` or compiled from source are not supported.\n", "\n", "The last solution is to find the TensorFlow Addons version that has custom ops compatible with the TensorFlow installed on your system. To do that, refer to the readme: https://github.com/tensorflow/addons\n", " warnings.warn(\n", "Cannot import beam_search_ops from Tensorflow Addons, ['malaya.jawi_rumi.deep_model', 'malaya.phoneme.deep_model', 'malaya.rumi_jawi.deep_model', 'malaya.stem.deep_model'] will not available to use, make sure Tensorflow Addons version >= 0.12.0\n", "check compatible Tensorflow version with Tensorflow Addons at https://github.com/tensorflow/addons/releases\n", "/home/husein/.local/lib/python3.8/site-packages/whisper/timing.py:58: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n", " def backtrace(trace: np.ndarray):\n", "/home/husein/.local/lib/python3.8/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n", " warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "/home/husein/.local/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "`pyaudio` is not available, `malaya_speech.streaming.pyaudio` is not able to use.\n" ] } ], "source": [ "import malaya_speech\n", "import numpy as np\n", "from malaya_speech import Pipeline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.2068125" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y, sr = malaya_speech.load('speech/vctk/p300_298_mic1.flac')\n", "len(y) / sr" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "60.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from datasets import Audio\n", "\n", "y2, sr = malaya_speech.load('speech/podcast/toodia.mp3')\n", "\n", "len(y2) / 16000" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import IPython.display as ipd\n", "ipd.Audio(y, rate = sr)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ipd.Audio(y2, rate = sr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List available Nemo models" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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original fromSize (MB)
huseinzol05/nemo-is-clean-speakernethttps://catalog.ngc.nvidia.com/orgs/nvidia/tea...16.2
huseinzol05/nemo-is-clean-titanet_largehttps://catalog.ngc.nvidia.com/orgs/nvidia/tea...88.8
\n", "
" ], "text/plain": [ " original from \\\n", "huseinzol05/nemo-is-clean-speakernet https://catalog.ngc.nvidia.com/orgs/nvidia/tea... \n", "huseinzol05/nemo-is-clean-titanet_large https://catalog.ngc.nvidia.com/orgs/nvidia/tea... \n", "\n", " Size (MB) \n", "huseinzol05/nemo-is-clean-speakernet 16.2 \n", "huseinzol05/nemo-is-clean-titanet_large 88.8 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "malaya_speech.is_clean.available_nemo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load Nemo model\n", "\n", "```python\n", "def nemo(\n", " model: str = 'huseinzol05/nemo-speaker-count-speakernet',\n", " **kwargs,\n", "):\n", " \"\"\"\n", " Load Nvidia Nemo speaker count model.\n", " Trained on 300 ms frames.\n", "\n", " Parameters\n", " ----------\n", " model : str, optional (default='huseinzol05/nemo-speaker-count-speakernet')\n", " Check available models at `malaya_speech.speaker_count.available_nemo()`.\n", "\n", " Returns\n", " -------\n", " result : malaya_speech.torch_model.nemo.Classification class\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [], "source": [ "model = malaya_speech.speaker_count.nemo(model = 'huseinzol05/nemo-speaker-count-titanet_large')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "_ = model.eval()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How to use Speaker Count detection\n", "\n", "We finetuned nemo models on 300 ms frame." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "frames = list(malaya_speech.utils.generator.frames(y, 300, sr, False))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.1 s, sys: 72.4 ms, total: 7.18 s\n", "Wall time: 992 ms\n" ] } ], "source": [ "%%time\n", "\n", "probs = [(frame, model.predict([frame])[0]) for frame in frames]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-09-01 12:14:17.946627: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2023-09-01 12:14:17.957400: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n", "2023-09-01 12:14:17.957442: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: husein-MS-7D31\n", "2023-09-01 12:14:17.957445: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: husein-MS-7D31\n", "2023-09-01 12:14:17.957725: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 470.199.2\n", "2023-09-01 12:14:17.957751: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 470.199.2\n", "2023-09-01 12:14:17.957754: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 470.199.2\n", "/home/husein/dev/malaya-speech/malaya_speech/utils/featurization.py:38: FutureWarning: Pass sr=16000, n_fft=512 as keyword args. From version 0.10 passing these as positional arguments will result in an error\n", " self.mel_basis = librosa.filters.mel(\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vad = malaya_speech.vad.deep_model(model = 'vggvox-v2')\n", "frames = list(malaya_speech.utils.generator.frames(y, 30, sr))\n", "p = Pipeline()\n", "pipeline = (\n", " p.batching(5)\n", " .foreach_map(vad.predict)\n", " .flatten()\n", ")\n", "p.visualize()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/husein/.local/lib/python3.8/site-packages/librosa/util/decorators.py:88: UserWarning: n_fft=512 is too small for input signal of length=480\n", " return f(*args, **kwargs)\n", "2023-09-01 12:14:19.769920: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:357] MLIR V1 optimization pass is not enabled\n", "/home/husein/.local/lib/python3.8/site-packages/librosa/util/decorators.py:88: UserWarning: n_fft=512 is too small for input signal of length=269\n", " return f(*args, **kwargs)\n" ] }, { "data": { "text/plain": [ "dict_keys(['batching', 'predict', 'flatten'])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result = p.emit(frames)\n", "result.keys()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "frames_vad = [(frame, result['flatten'][no]) for no, frame in enumerate(frames)]\n", "grouped_vad = malaya_speech.utils.group.group_frames(frames_vad)\n", "grouped_vad = malaya_speech.utils.group.group_frames_threshold(grouped_vad, threshold_to_stop = 0.3)\n", "grouped_vad = malaya_speech.utils.group.group_frames(grouped_vad)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nrows = 2\n", "fig, ax = plt.subplots(nrows = nrows, ncols = 1)\n", "fig.set_figwidth(20)\n", "fig.set_figheight(nrows * 3)\n", "malaya_speech.extra.visualization.visualize_vad(y, grouped_vad, sr, ax = ax[0])\n", "malaya_speech.extra.visualization.plot_classification(probs, 'speaker count', \n", " yaxis = True, ax = ax[1])\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "frames = list(malaya_speech.utils.generator.frames(y2, 300, sr, False))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 14s, sys: 994 ms, total: 1min 15s\n", "Wall time: 7.35 s\n" ] } ], "source": [ "%%time\n", "\n", "probs = [(frame, model.predict([frame])[0]) for frame in frames]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/husein/dev/malaya-speech/malaya_speech/utils/featurization.py:38: FutureWarning: Pass sr=16000, n_fft=512 as keyword args. From version 0.10 passing these as positional arguments will result in an error\n", " self.mel_basis = librosa.filters.mel(\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vad = malaya_speech.vad.deep_model(model = 'vggvox-v2')\n", "frames = list(malaya_speech.utils.generator.frames(y2, 30, sr))\n", "p = Pipeline()\n", "pipeline = (\n", " p.batching(5)\n", " .foreach_map(vad.predict)\n", " .flatten()\n", ")\n", "p.visualize()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/husein/.local/lib/python3.8/site-packages/librosa/util/decorators.py:88: UserWarning: n_fft=512 is too small for input signal of length=480\n", " return f(*args, **kwargs)\n" ] }, { "data": { "text/plain": [ "dict_keys(['batching', 'predict', 'flatten'])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result = p.emit(frames)\n", "result.keys()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "frames_vad = [(frame, result['flatten'][no]) for no, frame in enumerate(frames)]\n", "grouped_vad = malaya_speech.utils.group.group_frames(frames_vad)\n", "grouped_vad = malaya_speech.utils.group.group_frames_threshold(grouped_vad, threshold_to_stop = 0.3)\n", "grouped_vad = malaya_speech.utils.group.group_frames(grouped_vad)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nrows = 2\n", "fig, ax = plt.subplots(nrows = nrows, ncols = 1)\n", "fig.set_figwidth(20)\n", "fig.set_figheight(nrows * 3)\n", "malaya_speech.extra.visualization.visualize_vad(y2, grouped_vad, sr, ax = ax[0])\n", "malaya_speech.extra.visualization.plot_classification(probs, 'speaker count', \n", " yaxis = True, ax = ax[1])\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ipd.Audio(y2[20 * sr: 30 * sr], rate = sr)" ] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }