msb-public
/
PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
1
0
Code Issues Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '1690bd5911'
${ noResults }
PaddleSpeech/.notebook/jit_infer.ipynb

672 lines
27 KiB
Raw Blame History Escape

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ssd5/zhanghui/DeepSpeech2.x\n"
]
},
{
"data": {
"text/plain": [
"'/home/ssd5/zhanghui/DeepSpeech2.x'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%cd ..\n",
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-03-26 02:55:23,873 - WARNING - register user softmax to paddle, remove this when fixed!\n",
"2021-03-26 02:55:23,875 - WARNING - register user sigmoid to paddle, remove this when fixed!\n",
"2021-03-26 02:55:23,875 - WARNING - register user relu to paddle, remove this when fixed!\n",
"2021-03-26 02:55:23,876 - WARNING - override cat of paddle if exists or register, remove this when fixed!\n",
"2021-03-26 02:55:23,876 - WARNING - override eq of paddle.Tensor if exists or register, remove this when fixed!\n",
"2021-03-26 02:55:23,877 - WARNING - override contiguous of paddle.Tensor if exists or register, remove this when fixed!\n",
"2021-03-26 02:55:23,877 - WARNING - override size of paddle.Tensor (`to_static` do not process `size` property, maybe some `paddle` api dependent on it), remove this when fixed!\n",
"2021-03-26 02:55:23,878 - WARNING - register user view to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,878 - WARNING - register user view_as to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,879 - WARNING - register user masked_fill to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,880 - WARNING - register user masked_fill_ to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,880 - WARNING - register user fill_ to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,881 - WARNING - register user repeat to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,881 - WARNING - register user softmax to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,882 - WARNING - register user sigmoid to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,882 - WARNING - register user relu to paddle.Tensor, remove this when fixed!\n",
"2021-03-26 02:55:23,883 - WARNING - register user glu to paddle.nn.functional, remove this when fixed!\n",
"2021-03-26 02:55:23,883 - WARNING - override ctc_loss of paddle.nn.functional if exists, remove this when fixed!\n",
"2021-03-26 02:55:23,884 - WARNING - register user GLU to paddle.nn, remove this when fixed!\n",
"2021-03-26 02:55:23,884 - WARNING - register user ConstantPad2d to paddle.nn, remove this when fixed!\n",
"/home/ssd5/zhanghui/DeepSpeech2.x/tools/venv-dev/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",
"/home/ssd5/zhanghui/DeepSpeech2.x/tools/venv-dev/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"
]
}
],
"source": [
"import os\n",
"import time\n",
"import argparse\n",
"import functools\n",
"import paddle\n",
"import numpy as np\n",
"\n",
"from deepspeech.utils.socket_server import warm_up_test\n",
"from deepspeech.utils.socket_server import AsrTCPServer\n",
"from deepspeech.utils.socket_server import AsrRequestHandler\n",
"\n",
"from deepspeech.training.cli import default_argument_parser\n",
"from deepspeech.exps.deepspeech2.config import get_cfg_defaults\n",
"\n",
"from deepspeech.frontend.utility import read_manifest\n",
"from deepspeech.utils.utility import add_arguments, print_arguments\n",
"\n",
"from deepspeech.models.deepspeech2 import DeepSpeech2Model\n",
"from deepspeech.models.deepspeech2 import DeepSpeech2InferModel\n",
"from deepspeech.io.dataset import ManifestDataset\n",
"\n",
"\n",
"\n",
"from deepspeech.frontend.utility import read_manifest"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0.0\n",
"e7f28d6c0db54eb9c9a810612300b526687e56a6\n",
"OFF\n",
"OFF\n",
"commit: e7f28d6c0db54eb9c9a810612300b526687e56a6\n",
"None\n",
"0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ssd5/zhanghui/DeepSpeech2.x/tools/venv-dev/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
]
},
{
"data": {
"text/plain": [
"['__builtins__',\n",
" '__cached__',\n",
" '__doc__',\n",
" '__file__',\n",
" '__loader__',\n",
" '__name__',\n",
" '__package__',\n",
" '__spec__',\n",
" 'commit',\n",
" 'full_version',\n",
" 'istaged',\n",
" 'major',\n",
" 'minor',\n",
" 'mkl',\n",
" 'patch',\n",
" 'rc',\n",
" 'show',\n",
" 'with_mkl']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(paddle.__version__)\n",
"print(paddle.version.commit)\n",
"print(paddle.version.with_mkl)\n",
"print(paddle.version.mkl())\n",
"print(paddle.version.show())\n",
"print(paddle.version.patch)\n",
"dir(paddle.version)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data:\n",
" augmentation_config: conf/augmentation.config\n",
" batch_size: 64\n",
" dev_manifest: data/manifest.dev\n",
" keep_transcription_text: False\n",
" max_duration: 27.0\n",
" max_freq: None\n",
" mean_std_filepath: examples/aishell/data/mean_std.npz\n",
" min_duration: 0.0\n",
" n_fft: None\n",
" num_workers: 0\n",
" random_seed: 0\n",
" shuffle_method: batch_shuffle\n",
" sortagrad: True\n",
" specgram_type: linear\n",
" stride_ms: 10.0\n",
" target_dB: -20\n",
" target_sample_rate: 16000\n",
" test_manifest: examples/aishell/data/manifest.test\n",
" train_manifest: data/manifest.train\n",
" use_dB_normalization: True\n",
" vocab_filepath: examples/aishell/data/vocab.txt\n",
" window_ms: 20.0\n",
"decoding:\n",
" alpha: 2.6\n",
" batch_size: 128\n",
" beam_size: 300\n",
" beta: 5.0\n",
" cutoff_prob: 0.99\n",
" cutoff_top_n: 40\n",
" decoding_method: ctc_beam_search\n",
" error_rate_type: cer\n",
" lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm\n",
" num_proc_bsearch: 10\n",
"model:\n",
" num_conv_layers: 2\n",
" num_rnn_layers: 3\n",
" rnn_layer_size: 1024\n",
" share_rnn_weights: False\n",
" use_gru: True\n",
"training:\n",
" global_grad_clip: 5.0\n",
" lr: 0.0005\n",
" lr_decay: 0.83\n",
" n_epoch: 30\n",
" weight_decay: 1e-06\n",
"----------- Configuration Arguments -----------\n",
"checkpoint_path: examples/aishell/ckpt-loss2e-3-0.83-5/checkpoints/step-11725\n",
"config: examples/aishell/conf/deepspeech2.yaml\n",
"device: gpu\n",
"dump_config: None\n",
"export_path: None\n",
"host_ip: localhost\n",
"host_port: 8086\n",
"model_dir: None\n",
"model_file: examples/aishell/jit.model.pdmodel\n",
"nprocs: 1\n",
"opts: ['data.test_manifest', 'examples/aishell/data/manifest.test', 'data.mean_std_filepath', 'examples/aishell/data/mean_std.npz', 'data.vocab_filepath', 'examples/aishell/data/vocab.txt']\n",
"output: None\n",
"params_file: examples/aishell/jit.model.pdiparams\n",
"speech_save_dir: demo_cache\n",
"use_gpu: False\n",
"warmup_manifest: examples/aishell/data/manifest.test\n",
"------------------------------------------------\n"
]
}
],
"source": [
"parser = default_argument_parser()\n",
"add_arg = functools.partial(add_arguments, argparser=parser)\n",
"add_arg('host_ip', str,\n",
" 'localhost',\n",
" \"Server's IP address.\")\n",
"add_arg('host_port', int, 8086, \"Server's IP port.\")\n",
"add_arg('speech_save_dir', str,\n",
" 'demo_cache',\n",
" \"Directory to save demo audios.\")\n",
"add_arg('warmup_manifest', \n",
" str, \n",
" \"examples/aishell/data/manifest.test\", \n",
" \"Filepath of manifest to warm up.\")\n",
"add_arg(\n",
" \"--model_file\",\n",
" type=str,\n",
" default=\"examples/aishell/jit.model.pdmodel\",\n",
" help=\"Model filename, Specify this when your model is a combined model.\"\n",
")\n",
"add_arg(\n",
" \"--params_file\",\n",
" type=str,\n",
" default=\"examples/aishell/jit.model.pdiparams\",\n",
" help=\n",
" \"Parameter filename, Specify this when your model is a combined model.\"\n",
")\n",
"add_arg(\n",
" \"--model_dir\",\n",
" type=str,\n",
" default=None,\n",
" help=\n",
" \"Model dir, If you load a non-combined model, specify the directory of the model.\"\n",
")\n",
"add_arg(\"--use_gpu\",type=bool,default=False, help=\"Whether use gpu.\")\n",
"\n",
"\n",
"args = parser.parse_args(\n",
" \"--checkpoint_path examples/aishell/ckpt-loss2e-3-0.83-5/checkpoints/step-11725 --config examples/aishell/conf/deepspeech2.yaml --opts data.test_manifest examples/aishell/data/manifest.test data.mean_std_filepath examples/aishell/data/mean_std.npz data.vocab_filepath examples/aishell/data/vocab.txt\".split()\n",
")\n",
"\n",
"\n",
"config = get_cfg_defaults()\n",
"if args.config:\n",
" config.merge_from_file(args.config)\n",
"if args.opts:\n",
" config.merge_from_list(args.opts)\n",
"config.freeze()\n",
"print(config)\n",
"\n",
"args.warmup_manifest = config.data.test_manifest\n",
"\n",
"print_arguments(args)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"dataset = ManifestDataset(\n",
" config.data.test_manifest,\n",
" config.data.unit_type,\n",
" config.data.vocab_filepath,\n",
" config.data.mean_std_filepath,\n",
" augmentation_config=\"{}\",\n",
" max_duration=config.data.max_duration,\n",
" min_duration=config.data.min_duration,\n",
" stride_ms=config.data.stride_ms,\n",
" window_ms=config.data.window_ms,\n",
" n_fft=config.data.n_fft,\n",
" max_freq=config.data.max_freq,\n",
" target_sample_rate=config.data.target_sample_rate,\n",
" specgram_type=config.data.specgram_type,\n",
" feat_dim=config.data.feat_dim,\n",
" delta_delta=config.data.delat_delta,\n",
" use_dB_normalization=config.data.use_dB_normalization,\n",
" target_dB=config.data.target_dB,\n",
" random_seed=config.data.random_seed,\n",
" keep_transcription_text=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-03-26 02:55:57,930 - INFO - [checkpoint] Rank 0: loaded model from examples/aishell/ckpt-loss2e-3-0.83-5/checkpoints/step-11725.pdparams\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"layer summary:\n",
"encoder.conv.conv_in.conv.weight|[32, 1, 41, 11]|14432\n",
"encoder.conv.conv_in.bn.weight|[32]|32\n",
"encoder.conv.conv_in.bn.bias|[32]|32\n",
"encoder.conv.conv_in.bn._mean|[32]|32\n",
"encoder.conv.conv_in.bn._variance|[32]|32\n",
"encoder.conv.conv_stack.0.conv.weight|[32, 32, 21, 11]|236544\n",
"encoder.conv.conv_stack.0.bn.weight|[32]|32\n",
"encoder.conv.conv_stack.0.bn.bias|[32]|32\n",
"encoder.conv.conv_stack.0.bn._mean|[32]|32\n",
"encoder.conv.conv_stack.0.bn._variance|[32]|32\n",
"encoder.rnn.rnn_stacks.0.fw_fc.weight|[1312, 3072]|4030464\n",
"encoder.rnn.rnn_stacks.0.fw_bn.weight|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.fw_bn.bias|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.fw_bn._mean|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.fw_bn._variance|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.bw_fc.weight|[1312, 3072]|4030464\n",
"encoder.rnn.rnn_stacks.0.bw_bn.weight|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.bw_bn.bias|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.bw_bn._mean|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.bw_bn._variance|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.fw_cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.0.fw_cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.bw_cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.0.bw_cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.fw_rnn.cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.0.fw_rnn.cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.0.bw_rnn.cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.0.bw_rnn.cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.fw_fc.weight|[2048, 3072]|6291456\n",
"encoder.rnn.rnn_stacks.1.fw_bn.weight|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.fw_bn.bias|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.fw_bn._mean|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.fw_bn._variance|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.bw_fc.weight|[2048, 3072]|6291456\n",
"encoder.rnn.rnn_stacks.1.bw_bn.weight|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.bw_bn.bias|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.bw_bn._mean|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.bw_bn._variance|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.fw_cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.1.fw_cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.bw_cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.1.bw_cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.fw_rnn.cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.1.fw_rnn.cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.1.bw_rnn.cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.1.bw_rnn.cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.fw_fc.weight|[2048, 3072]|6291456\n",
"encoder.rnn.rnn_stacks.2.fw_bn.weight|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.fw_bn.bias|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.fw_bn._mean|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.fw_bn._variance|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.bw_fc.weight|[2048, 3072]|6291456\n",
"encoder.rnn.rnn_stacks.2.bw_bn.weight|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.bw_bn.bias|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.bw_bn._mean|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.bw_bn._variance|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.fw_cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.2.fw_cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.bw_cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.2.bw_cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.fw_rnn.cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.2.fw_rnn.cell.bias_hh|[3072]|3072\n",
"encoder.rnn.rnn_stacks.2.bw_rnn.cell.weight_hh|[3072, 1024]|3145728\n",
"encoder.rnn.rnn_stacks.2.bw_rnn.cell.bias_hh|[3072]|3072\n",
"decoder.ctc_lo.weight|[2048, 4300]|8806400\n",
"decoder.ctc_lo.bias|[4300]|4300\n",
"layer has 66 parameters, 80148012 elements.\n"
]
}
],
"source": [
"model = DeepSpeech2InferModel.from_pretrained(dataset, config,\n",
" args.checkpoint_path)\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"examples/aishell/jit.model.pdmodel\n",
"examples/aishell/jit.model.pdiparams\n",
"0\n",
"False\n"
]
}
],
"source": [
"\n",
"from paddle.inference import Config\n",
"from paddle.inference import PrecisionType\n",
"from paddle.inference import create_predictor\n",
"\n",
"args.use_gpu=False\n",
"paddle.set_device('cpu')\n",
"\n",
"def init_predictor(args):\n",
" if args.model_dir is not None:\n",
" config = Config(args.model_dir)\n",
" else:\n",
" config = Config(args.model_file, args.params_file)\n",
"\n",
" if args.use_gpu:\n",
" config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0)\n",
"# config.enable_tensorrt_engine(precision_mode=PrecisionType.Float32,\n",
"# use_calib_mode=True) # 开启TensorRT预测，精度为fp32，开启int8离线量化\n",
" else:\n",
" # If not specific mkldnn, you can set the blas thread.\n",
" # The thread num should not be greater than the number of cores in the CPU.\n",
" config.set_cpu_math_library_num_threads(1)\n",
" config.enable_mkldnn()\n",
" \n",
" config.enable_memory_optim()\n",
" config.switch_ir_optim(True)\n",
" \n",
" print(config.model_dir())\n",
" print(config.prog_file())\n",
" print(config.params_file())\n",
" print(config.gpu_device_id())\n",
" print(args.use_gpu)\n",
" predictor = create_predictor(config)\n",
" return predictor\n",
"\n",
"def run(predictor, audio, audio_len):\n",
" # copy img data to input tensor\n",
" input_names = predictor.get_input_names()\n",
" for i, name in enumerate(input_names):\n",
" print(\"input:\", i, name)\n",
" \n",
" audio_tensor = predictor.get_input_handle('audio')\n",
" audio_tensor.reshape(audio.shape)\n",
" audio_tensor.copy_from_cpu(audio.copy())\n",
" \n",
" audiolen_tensor = predictor.get_input_handle('audio_len')\n",
" audiolen_tensor.reshape(audio_len.shape)\n",
" audiolen_tensor.copy_from_cpu(audio_len.copy())\n",
"\n",
" output_names = predictor.get_output_names()\n",
" for i, name in enumerate(output_names):\n",
" print(\"output:\", i, name)\n",
"\n",
" # do the inference\n",
" predictor.run()\n",
"\n",
" results = []\n",
" # get out data from output tensor\n",
" output_names = predictor.get_output_names()\n",
" for i, name in enumerate(output_names):\n",
" output_tensor = predictor.get_output_handle(name)\n",
" output_data = output_tensor.copy_to_cpu()\n",
" results.append(output_data)\n",
"\n",
" return results\n",
"\n",
"\n",
"predictor = init_predictor(args)\n",
"\n",
"def file_to_transcript(filename):\n",
" print(filename)\n",
" feature = dataset.process_utterance(filename, \"\")\n",
" audio = np.array([feature[0]]).astype('float32') #[1, D, T]\n",
" audio_len = feature[0].shape[1]\n",
" audio_len = np.array([audio_len]).astype('int64') # [1]\n",
" \n",
" \n",
" i_probs = run(predictor, audio, audio_len)\n",
" print('jit:', i_probs[0], type(i_probs[0]))\n",
" \n",
" audio = paddle.to_tensor(audio)\n",
" audio_len = paddle.to_tensor(audio_len)\n",
" print(audio.shape)\n",
" print(audio_len.shape)\n",
" \n",
" #eouts, eouts_len = model.encoder(audio, audio_len)\n",
" #probs = model.decoder.softmax(eouts)\n",
" probs = model.forward(audio, audio_len)\n",
" print('paddle:', probs.numpy())\n",
" \n",
" flag = np.allclose(i_probs[0], probs.numpy())\n",
" print(flag)\n",
" \n",
" return probs\n",
"\n",
"# result_transcript = model.decode(\n",
"# audio,\n",
"# audio_len,\n",
"# vocab_list=dataset.vocab_list,\n",
"# decoding_method=config.decoding.decoding_method,\n",
"# lang_model_path=config.decoding.lang_model_path,\n",
"# beam_alpha=config.decoding.alpha,\n",
"# beam_beta=config.decoding.beta,\n",
"# beam_size=config.decoding.beam_size,\n",
"# cutoff_prob=config.decoding.cutoff_prob,\n",
"# cutoff_top_n=config.decoding.cutoff_top_n,\n",
"# num_processes=config.decoding.num_proc_bsearch)\n",
"# return result_transcript[0]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warm-up Test Case %d: %s 0 /home/ssd5/zhanghui/DeepSpeech2.x/examples/aishell/../dataset/aishell/data_aishell/wav/test/S0764/BAC009S0764W0124.wav\n",
"/home/ssd5/zhanghui/DeepSpeech2.x/examples/aishell/../dataset/aishell/data_aishell/wav/test/S0764/BAC009S0764W0124.wav\n",
"input: 0 audio\n",
"input: 1 audio_len\n",
"output: 0 tmp_75\n",
"jit: [[[8.91786298e-12 4.45648032e-12 3.67572750e-09 ... 8.91767563e-12\n",
" 8.91573707e-12 4.64317296e-08]\n",
" [1.55950222e-15 2.62794089e-14 4.50423509e-12 ... 1.55944271e-15\n",
" 1.55891342e-15 9.99992609e-01]\n",
" [1.24638127e-17 7.61802427e-16 2.93265812e-14 ... 1.24633371e-17\n",
" 1.24587264e-17 1.00000000e+00]\n",
" ...\n",
" [4.37488240e-15 2.43676260e-12 1.98770514e-12 ... 4.37479896e-15\n",
" 4.37354747e-15 1.00000000e+00]\n",
" [3.89334696e-13 1.66754856e-11 1.42900388e-11 ... 3.89329492e-13\n",
" 3.89252270e-13 1.00000000e+00]\n",
" [1.00349985e-10 2.56293708e-10 2.91177582e-10 ... 1.00347876e-10\n",
" 1.00334095e-10 9.99998808e-01]]] <class 'numpy.ndarray'>\n",
"[1, 161, 522]\n",
"[1]\n",
"paddle: [[[8.91789680e-12 4.45649724e-12 3.67574149e-09 ... 8.91770945e-12\n",
" 8.91577090e-12 4.64319072e-08]\n",
" [1.55950222e-15 2.62794089e-14 4.50423509e-12 ... 1.55944271e-15\n",
" 1.55891342e-15 9.99992609e-01]\n",
" [1.24638599e-17 7.61805339e-16 2.93267472e-14 ... 1.24633842e-17\n",
" 1.24587735e-17 1.00000000e+00]\n",
" ...\n",
" [4.37488240e-15 2.43676737e-12 1.98770514e-12 ... 4.37479896e-15\n",
" 4.37354747e-15 1.00000000e+00]\n",
" [3.89336187e-13 1.66755481e-11 1.42900925e-11 ... 3.89330983e-13\n",
" 3.89253761e-13 1.00000000e+00]\n",
" [1.00349985e-10 2.56293708e-10 2.91177582e-10 ... 1.00347876e-10\n",
" 1.00334095e-10 9.99998808e-01]]]\n",
"False\n"
]
}
],
"source": [
"manifest = read_manifest(args.warmup_manifest)\n",
"\n",
"for idx, sample in enumerate(manifest[:1]):\n",
" print(\"Warm-up Test Case %d: %s\", idx, sample['audio_filepath'])\n",
" start_time = time.time()\n",
" transcript = file_to_transcript(sample['audio_filepath'])\n",
" finish_time = time.time()\n",
"# print(\"Response Time: %f, Transcript: %s\" %\n",
"# (finish_time - start_time, transcript))\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 161, 522) (1,)\n",
"input: 0 audio\n",
"input: 1 audio_len\n",
"output: 0 tmp_75\n",
"jit: [[[8.91789680e-12 4.45649724e-12 3.67574149e-09 ... 8.91770945e-12\n",
" 8.91577090e-12 4.64319072e-08]\n",
" [1.55950222e-15 2.62794089e-14 4.50423509e-12 ... 1.55944271e-15\n",
" 1.55891342e-15 9.99992609e-01]\n",
" [1.24638599e-17 7.61805339e-16 2.93267472e-14 ... 1.24633842e-17\n",
" 1.24587735e-17 1.00000000e+00]\n",
" ...\n",
" [4.37488240e-15 2.43676737e-12 1.98770514e-12 ... 4.37479896e-15\n",
" 4.37354747e-15 1.00000000e+00]\n",
" [3.89336187e-13 1.66755481e-11 1.42900925e-11 ... 3.89330983e-13\n",
" 3.89253761e-13 1.00000000e+00]\n",
" [1.00349985e-10 2.56293708e-10 2.91177582e-10 ... 1.00347876e-10\n",
" 1.00334095e-10 9.99998808e-01]]]\n"
]
}
],
"source": [
"def test(filename):\n",
" feature = dataset.process_utterance(filename, \"\")\n",
" audio = np.array([feature[0]]).astype('float32') #[1, D, T]\n",
" audio_len = feature[0].shape[1]\n",
" audio_len = np.array([audio_len]).astype('int64') # [1]\n",
" \n",
" print(audio.shape, audio_len.shape)\n",
"\n",
" i_probs = run(predictor, audio, audio_len)\n",
" print('jit:', i_probs[0])\n",
" return i_probs\n",
" \n",
"probs = test(sample['audio_filepath'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in new issue View Git Blame Copy Permalink