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DeepSpeech
提交 9069ba66 编写于 作者: H Hui Zhang
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add cmvn notebook

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.notebook/compute_cmvn_loader_test.ipynb
浏览文件 @ 9069ba66
......@@ -10,13 +10,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
"/workspace/DeepSpeech-2.x\n"
"/home/ssd5/zhanghui/DeepSpeech2.x\n"
]
},
{
"data": {
"text/plain": [
"'/workspace/DeepSpeech-2.x'"
"'/home/ssd5/zhanghui/DeepSpeech2.x'"
]
},
"execution_count": 1,
......@@ -39,68 +39,15 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 6,
"id": "patient-convention",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" def convert_to_list(value, n, name, dtype=np.int):\n",
"register user softmax to paddle, remove this when fixed!\n",
"register user log_softmax to paddle, remove this when fixed!\n",
"register user sigmoid to paddle, remove this when fixed!\n",
"register user log_sigmoid to paddle, remove this when fixed!\n",
"register user relu to paddle, remove this when fixed!\n",
"override cat of paddle if exists or register, remove this when fixed!\n",
"override item of paddle.Tensor if exists or register, remove this when fixed!\n",
"override long of paddle.Tensor if exists or register, remove this when fixed!\n",
"override new_full of paddle.Tensor if exists or register, remove this when fixed!\n",
"override eq of paddle.Tensor if exists or register, remove this when fixed!\n",
"override eq of paddle if exists or register, remove this when fixed!\n",
"override contiguous of paddle.Tensor if exists or register, remove this when fixed!\n",
"override size of paddle.Tensor (`to_static` do not process `size` property, maybe some `paddle` api dependent on it), remove this when fixed!\n",
"register user view to paddle.Tensor, remove this when fixed!\n",
"register user view_as to paddle.Tensor, remove this when fixed!\n",
"register user masked_fill to paddle.Tensor, remove this when fixed!\n",
"register user masked_fill_ to paddle.Tensor, remove this when fixed!\n",
"register user fill_ to paddle.Tensor, remove this when fixed!\n",
"register user repeat to paddle.Tensor, remove this when fixed!\n",
"register user softmax to paddle.Tensor, remove this when fixed!\n",
"register user sigmoid to paddle.Tensor, remove this when fixed!\n",
"register user relu to paddle.Tensor, remove this when fixed!\n",
"register user type_as to paddle.Tensor, remove this when fixed!\n",
"register user to to paddle.Tensor, remove this when fixed!\n",
"register user float to paddle.Tensor, remove this when fixed!\n",
"register user tolist to paddle.Tensor, remove this when fixed!\n",
"register user glu to paddle.nn.functional, remove this when fixed!\n",
"override ctc_loss of paddle.nn.functional if exists, remove this when fixed!\n",
"register user Module to paddle.nn, remove this when fixed!\n",
"register user ModuleList to paddle.nn, remove this when fixed!\n",
"register user GLU to paddle.nn, remove this when fixed!\n",
"register user ConstantPad2d to paddle.nn, remove this when fixed!\n",
"register user export to paddle.jit, remove this when fixed!\n",
"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/scipy/fftpack/__init__.py:103: DeprecationWarning: The module numpy.dual is deprecated. Instead of using dual, use the functions directly from numpy or scipy.\n",
" from numpy.dual import register_func\n",
"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/scipy/special/orthogonal.py:81: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" from numpy import (exp, inf, pi, sqrt, floor, sin, cos, around, int,\n",
"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/numba/core/types/__init__.py:108: DeprecationWarning: `np.long` is a deprecated alias for `np.compat.long`. To silence this warning, use `np.compat.long` by itself. In the likely event your code does not need to work on Python 2 you can use the builtin `int` for which `np.compat.long` is itself an alias. Doing this will not modify any behaviour and is safe. When replacing `np.long`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" long_ = _make_signed(np.long)\n",
"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/numba/core/types/__init__.py:109: DeprecationWarning: `np.long` is a deprecated alias for `np.compat.long`. To silence this warning, use `np.compat.long` by itself. In the likely event your code does not need to work on Python 2 you can use the builtin `int` for which `np.compat.long` is itself an alias. Doing this will not modify any behaviour and is safe. When replacing `np.long`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" ulong = _make_unsigned(np.long)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Namespace(delta_delta=False, feat_dim=13, manifest_path='examples/aishell/s1/data/manifest.train.raw', num_samples=-1, num_workers=1, output_path='data/librispeech/mean_std.npz', sample_rate=16000, specgram_type='linear', stride_ms=10.0, window_ms=20.0)\n"
"Namespace(delta_delta=False, feat_dim=80, manifest_path='examples/aishell/s1/data/manifest.train.raw', num_samples=-1, num_workers=16, output_path='data/librispeech/mean_std.npz', sample_rate=16000, specgram_type='fbank', stride_ms=10.0, window_ms=25.0)\n"
]
}
],
......@@ -119,21 +66,21 @@
"# yapf: disable\n",
"add_arg('num_samples', int, -1, \"# of samples to for statistics.\")\n",
"add_arg('specgram_type', str,\n",
" 'linear',\n",
" 'fbank',\n",
" \"Audio feature type. Options: linear, mfcc, fbank.\",\n",
" choices=['linear', 'mfcc', 'fbank'])\n",
"add_arg('feat_dim', int, 13, \"Audio feature dim.\")\n",
"add_arg('feat_dim', int, 80, \"Audio feature dim.\")\n",
"add_arg('delta_delta', bool,\n",
" False,\n",
" \"Audio feature with delta delta.\")\n",
"add_arg('stride_ms', float, 10.0, \"stride length in ms.\")\n",
"add_arg('window_ms', float, 20.0, \"stride length in ms.\")\n",
"add_arg('window_ms', float, 25.0, \"stride length in ms.\")\n",
"add_arg('sample_rate', int, 16000, \"target sample rate.\")\n",
"add_arg('manifest_path', str,\n",
" 'examples/aishell/s1/data/manifest.train.raw',\n",
" \"Filepath of manifest to compute normalizer's mean and stddev.\")\n",
"add_arg('num_workers',\n",
" default=1,\n",
" default=16,\n",
" type=int,\n",
" help='num of subprocess workers for processing')\n",
"add_arg('output_path', str,\n",
......@@ -146,7 +93,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 7,
"id": "enormous-currency",
"metadata": {},
"outputs": [],
......@@ -213,67 +160,134 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 5,
"id": "armed-semester",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Process Process-2:\n",
"Traceback (most recent call last):\n",
" File \"/usr/local/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
" self.run()\n",
" File \"/usr/local/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
" self._target(*self._args, **self._kwargs)\n",
" File \"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py\", line 463, in _worker_loop\n",
" six.reraise(*sys.exc_info())\n",
" File \"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
" raise value\n",
" File \"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py\", line 446, in _worker_loop\n",
" for s in sample:\n",
"TypeError: 'int' object is not iterable\n",
"2021-04-20 07:43:09,866 - ERROR - DataLoader reader thread raised an exception!\n"
]
},
{
"ename": "SystemError",
"evalue": "(Fatal) Blocking queue is killed because the data reader raises an exception.\n [Hint: Expected killed_ != true, but received killed_:1 == true:1.] (at /paddle/paddle/fluid/operators/reader/blocking_queue.h:158)\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-b5adcffc5685>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mwav_number\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;31m# for i, batch in enumerate(data_loader()):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_stat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 778\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0min_dygraph_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_next_var_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 780\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_return_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mSystemError\u001b[0m: (Fatal) Blocking queue is killed because the data reader raises an exception.\n [Hint: Expected killed_ != true, but received killed_:1 == true:1.] (at /paddle/paddle/fluid/operators/reader/blocking_queue.h:158)\n"
]
},
{
"name": "stderr",
"name": "stdout",
"output_type": "stream",
"text": [
"Exception in thread Thread-5:\n",
"Traceback (most recent call last):\n",
" File \"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py\", line 684, in _get_data\n",
" data = self._data_queue.get(timeout=self._timeout)\n",
" File \"/usr/local/lib/python3.7/multiprocessing/queues.py\", line 105, in get\n",
" raise Empty\n",
"_queue.Empty\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/usr/local/lib/python3.7/threading.py\", line 917, in _bootstrap_inner\n",
" self.run()\n",
" File \"/usr/local/lib/python3.7/threading.py\", line 865, in run\n",
" self._target(*self._args, **self._kwargs)\n",
" File \"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py\", line 616, in _thread_loop\n",
" batch = self._get_data()\n",
" File \"/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py\", line 700, in _get_data\n",
" \"pids: {}\".format(len(failed_workers), pids))\n",
"RuntimeError: DataLoader 1 workers exit unexpectedly, pids: 40561\n",
"\n"
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]
}
],
......@@ -317,8 +331,8 @@
" all_var_stat = None\n",
" all_number = 0\n",
" wav_number = 0\n",
" # for i, batch in enumerate(data_loader()):\n",
" for batch in data_loader():\n",
" for i, batch in enumerate(data_loader()):\n",
" #for batch in data_loader():\n",
" number, mean_stat, var_stat = batch\n",
" if i == 0:\n",
" all_mean_stat = mean_stat\n",
......@@ -342,11 +356,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"id": "danish-executive",
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"print(cmvn_info)"
]
},
{
"cell_type": "code",
......@@ -358,9 +382,389 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"id": "dominant-abuse",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"process 1000 wavs,450240 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 2000 wavs,886411 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 3000 wavs,1352580 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 4000 wavs,1814397 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 5000 wavs,2356587 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 6000 wavs,2825310 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 7000 wavs,3272506 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 8000 wavs,3688045 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 9000 wavs,4134669 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 10000 wavs,4586357 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 11000 wavs,5014429 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 12000 wavs,5453334 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 13000 wavs,5892888 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 14000 wavs,6316059 frames\n",
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"process 15000 wavs,6728870 frames\n",
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"process 18000 wavs,8083729 frames\n",
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"process 21000 wavs,9341778 frames\n",
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"process 26000 wavs,11544482 frames\n",
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"process 27000 wavs,11996273 frames\n",
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"process 29000 wavs,12900895 frames\n",
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"process 36000 wavs,16004323 frames\n",
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"process 52000 wavs,23074160 frames\n",
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"process 59000 wavs,26185964 frames\n",
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"process 60000 wavs,26663953 frames\n",
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"process 61000 wavs,27117720 frames\n",
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"process 62000 wavs,27585349 frames\n",
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"process 63000 wavs,28032693 frames\n",
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"process 64000 wavs,28487074 frames\n",
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"process 65000 wavs,28956462 frames\n",
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"process 78000 wavs,34721536 frames\n",
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"process 81000 wavs,36080909 frames\n",
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"process 83000 wavs,37042976 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 84000 wavs,37474403 frames\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"process 85000 wavs,37943596 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 86000 wavs,38371620 frames\n",
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"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 105000 wavs,47153089 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 106000 wavs,47628699 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 107000 wavs,48067945 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 108000 wavs,48539256 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 109000 wavs,49030485 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 110000 wavs,49469189 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 111000 wavs,49928968 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 112000 wavs,50370921 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 113000 wavs,50840090 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 114000 wavs,51286249 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 115000 wavs,51715786 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 116000 wavs,52184017 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 117000 wavs,52666156 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 118000 wavs,53109645 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 119000 wavs,53553253 frames\n",
"<class 'int'> <class 'paddle.VarBase'> <class 'paddle.VarBase'>\n",
"process 120000 wavs,54009877 frames\n",
"{'mean_stat': [700612678.1184504, 704246512.9321843, 720430663.1822729, 754033269.0474415, 798737761.616614, 829467218.4204571, 851246702.9426627, 862261185.2661449, 859339943.6923889, 846303730.8696194, 832995109.605447, 823196536.6029147, 832626008.2569772, 845571326.1936859, 848801373.0562981, 846503549.328017, 836774344.5500796, 823481091.0445303, 820728368.2518216, 804571348.4957463, 795306095.0083207, 811729024.2415155, 805734803.5703195, 813076782.1959459, 806620199.406499, 809655573.8886961, 804371708.9347517, 809272248.6085774, 810322689.7490631, 814294131.1973915, 816262716.0476038, 816213124.2411841, 817158473.4380915, 821414211.5629157, 827408091.5728914, 834353896.0519086, 840094990.3467333, 842613218.6554606, 842070761.1727513, 834970952.5260613, 837020570.8200948, 829592602.7833654, 830116543.8893851, 829482316.3881509, 833397219.4597517, 839251633.3120549, 845475010.4718693, 852378426.7183967, 859563981.8633184, 866063840.5523493, 867790921.9978689, 868215100.5962687, 869683066.032885, 872467375.6674014, 873097681.1780069, 873025823.0543871, 869897292.7201596, 866386426.3869117, 863166726.7256871, 854653071.2244718, 842402803.9000899, 830838253.4144138, 830143002.3536818, 831492285.0310817, 833304371.8781006, 838896092.8621838, 843866088.9578133, 847316792.1429776, 851038022.3643295, 855931698.0149751, 859320543.9795249, 863031001.3470656, 868325062.1832993, 873626971.0115026, 878726636.924209, 884861725.972504, 886920281.5192285, 883056006.5094173, 863719240.7255149, 773378975.9476194], 'var_stat': [9237018652.657722, 9417257721.82426, 10105084297.159702, 11071318522.587782, 12422783727.426847, 13400306419.784964, 14148498843.406874, 14576436982.89939, 14529009036.494726, 14105645932.596651, 13682988821.478252, 13413013425.088106, 13764134927.293928, 14233704806.737064, 14361631309.367067, 14281358385.45644, 13939662689.213865, 13496884231.929493, 13382566162.783987, 12871350930.6626, 12576198160.876635, 13051463889.56708, 12859205935.513906, 13053861416.098743, 12830323588.550724, 12886405923.897238, 12708529922.84171, 12847306110.231739, 12880398489.53404, 13002566299.565536, 13066708060.463543, 13064231286.858614, 13088983337.353497, 13221393824.891022, 13412425607.755072, 13631485149.777075, 13807797519.156103, 13877277485.033077, 13848613909.96762, 13609176326.2529, 13649815250.130072, 13397698404.696907, 13388964704.359968, 13354326914.968012, 13469861474.898457, 13652539440.283333, 13846837321.329163, 14062143714.601675, 14292571198.61228, 14504626563.299246, 14563864749.132776, 14579720287.991764, 14626700787.353922, 14716185568.128899, 14728532777.28015, 14719101187.113443, 14607945896.239174, 14478517828.531614, 14355110561.681187, 14057430280.249746, 13634284490.879377, 13248236002.494394, 13217602306.335958, 13257856701.946049, 13323688441.072674, 13515395318.023148, 13685827169.67645, 13811622609.426846, 13947347160.615082, 14115883822.884943, 14231204526.433033, 14356066668.651815, 14533604268.238445, 14708971788.69237, 14875667326.732443, 15079098318.79331, 15144888989.667963, 15002658970.504765, 14349232841.34513, 11544480117.013124], 'frame_num': 54068199}\n"
]
}
],
"source": [
"import random\n",
"\n",
"import numpy as np\n",
"import paddle\n",
"from paddle.io import DataLoader\n",
"from paddle.io import Dataset\n",
"\n",
"from deepspeech.frontend.audio import AudioSegment\n",
"from deepspeech.frontend.utility import load_cmvn\n",
"from deepspeech.frontend.utility import read_manifest\n",
"\n",
"# https://github.com/PaddlePaddle/Paddle/pull/31481\n",
"class CollateFunc(object):\n",
" ''' Collate function for AudioDataset\n",
" '''\n",
" def __init__(self, feature_func):\n",
" self.feature_func = feature_func\n",
" \n",
" def __call__(self, batch):\n",
" mean_stat = None\n",
" var_stat = None\n",
" number = 0\n",
" for item in batch:\n",
" audioseg = AudioSegment.from_file(item['feat'])\n",
" feat = self.feature_func(audioseg) #(D, T)\n",
"\n",
" sums = np.sum(feat, axis=1)\n",
" if mean_stat is None:\n",
" mean_stat = sums\n",
" else:\n",
" mean_stat += sums\n",
"\n",
" square_sums = np.sum(np.square(feat), axis=1)\n",
" if var_stat is None:\n",
" var_stat = square_sums\n",
" else:\n",
" var_stat += square_sums\n",
"\n",
" number += feat.shape[1]\n",
" return number, mean_stat, var_stat\n",
"\n",
"\n",
"class AudioDataset(Dataset):\n",
" def __init__(self, manifest_path, num_samples=-1, rng=None, random_seed=0):\n",
" self._rng = rng if rng else np.random.RandomState(random_seed)\n",
" manifest = read_manifest(manifest_path)\n",
" if num_samples == -1:\n",
" sampled_manifest = manifest\n",
" else:\n",
" sampled_manifest = self._rng.choice(manifest, num_samples, replace=False)\n",
" self.items = sampled_manifest\n",
"\n",
" def __len__(self):\n",
" return len(self.items)\n",
"\n",
" def __getitem__(self, idx):\n",
" return self.items[idx]\n",
" \n",
" \n",
"augmentation_pipeline = AugmentationPipeline('{}')\n",
"audio_featurizer = AudioFeaturizer(\n",
" specgram_type=args.specgram_type,\n",
" feat_dim=args.feat_dim,\n",
" delta_delta=args.delta_delta,\n",
" stride_ms=args.stride_ms,\n",
" window_ms=args.window_ms,\n",
" n_fft=None,\n",
" max_freq=None,\n",
" target_sample_rate=args.sample_rate,\n",
" use_dB_normalization=True,\n",
" target_dB=-20)\n",
"\n",
"def augment_and_featurize(audio_segment):\n",
" augmentation_pipeline.transform_audio(audio_segment)\n",
" return audio_featurizer.featurize(audio_segment)\n",
"\n",
"\n",
"collate_func = CollateFunc(augment_and_featurize)\n",
"\n",
"dataset = AudioDataset(\n",
" args.manifest_path,\n",
" args.num_samples)\n",
"\n",
"batch_size = 20\n",
"data_loader = DataLoader(\n",
" dataset,\n",
" batch_size=batch_size,\n",
" shuffle=False,\n",
" num_workers=args.num_workers,\n",
" collate_fn=collate_func)\n",
"\n",
"with paddle.no_grad():\n",
" all_mean_stat = None\n",
" all_var_stat = None\n",
" all_number = 0\n",
" wav_number = 0\n",
" for i, batch in enumerate(data_loader):\n",
" number, mean_stat, var_stat = batch\n",
" if i == 0:\n",
" all_mean_stat = mean_stat\n",
" all_var_stat = var_stat\n",
" else:\n",
" all_mean_stat += mean_stat\n",
" all_var_stat += var_stat\n",
" all_number += number\n",
" wav_number += batch_size\n",
"\n",
" if wav_number % 1000 == 0:\n",
" print('process {} wavs,{} frames'.format(wav_number,\n",
" all_number))\n",
"\n",
"cmvn_info = {\n",
" 'mean_stat': list(all_mean_stat.tolist()),\n",
" 'var_stat': list(all_var_stat.tolist()),\n",
" 'frame_num': all_number\n",
"}\n",
"print(cmvn_info)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "unlike-search",
"metadata": {},
"outputs": [],
"source": []
}
......
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