{
 "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": [
      "2021-03-17 11:09:34,972 - WARNING - override cat of paddle.Tensor if exists or register, remove this when fixed!\n",
      "2021-03-17 11:09:34,973 - WARNING - override size of paddle.Tensor if exists or register (`to_static` do not process `size` property, maybe some `paddle` api dependent on it), remove this when fixed!\n",
      "2021-03-17 11:09:34,974 - WARNING - register user masked_fill to paddle.Tensor, remove this when fixed!\n",
      "2021-03-17 11:09:34,975 - WARNING - register user masked_fill_ to paddle.Tensor, remove this when fixed!\n",
      "2021-03-17 11:09:34,975 - WARNING - register user repeat to paddle.Tensor, remove this when fixed!\n",
      "2021-03-17 11:09:34,976 - WARNING - register user glu to paddle.nn.functional, remove this when fixed!\n",
      "2021-03-17 11:09:34,976 - WARNING - register user GLU to paddle.nn, remove this when fixed!\n",
      "2021-03-17 11:09:34,977 - WARNING - register user ConstantPad2d to paddle.nn, remove this when fixed!\n",
      "2021-03-17 11:09:34,977 - WARNING - override ctc_loss of paddle.nn.functional if exists, 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",
    "from paddle.inference import Config\n",
    "from paddle.inference import create_predictor\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",
      "<module 'paddle.version' from '/home/ssd5/zhanghui/DeepSpeech2.x/tools/venv-dev/lib/python3.7/site-packages/paddlepaddle_gpu-0.0.0-py3.7-linux-x86_64.egg/paddle/version.py'>\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"
     ]
    }
   ],
   "source": [
    "print(paddle.__version__)\n",
    "print(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/checkpoints/step-1876\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: True\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',  str, \"examples/aishell/data/manifest.test\", \"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=True, help=\"Whether use gpu.\")\n",
    "args = parser.parse_args(\"--checkpoint_path examples/aishell/ckpt/checkpoints/step-1876 --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",
    "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",
    "print_arguments(args)\n",
    "\n",
    "if args.dump_config:\n",
    "    with open(args.dump_config, 'w') as f:\n",
    "        print(config, file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = ManifestDataset(\n",
    "        config.data.test_manifest,\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",
    "        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": [
      "/home/ssd5/zhanghui/DeepSpeech2.x/tools/venv-dev/lib/python3.7/site-packages/paddlepaddle_gpu-0.0.0-py3.7-linux-x86_64.egg/paddle/fluid/dygraph/layers.py:1303: UserWarning: Skip loading for decoder.ctc_lo.weight. decoder.ctc_lo.weight is not found in the provided dict.\n",
      "  warnings.warn((\"Skip loading for {}. \".format(key) + str(err)))\n",
      "/home/ssd5/zhanghui/DeepSpeech2.x/tools/venv-dev/lib/python3.7/site-packages/paddlepaddle_gpu-0.0.0-py3.7-linux-x86_64.egg/paddle/fluid/dygraph/layers.py:1303: UserWarning: Skip loading for decoder.ctc_lo.bias. decoder.ctc_lo.bias is not found in the provided dict.\n",
      "  warnings.warn((\"Skip loading for {}. \".format(key) + str(err)))\n",
      "2021-03-17 11:10:00,017 - INFO - [checkpoint] Rank 0: loaded model from examples/aishell/ckpt/checkpoints/step-1876.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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "    config.enable_memory_optim()\n",
    "    if args.use_gpu:\n",
    "        config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0)\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(4)\n",
    "        #config.enable_mkldnn()\n",
    "        \n",
    "    config.switch_ir_optim(False)\n",
    "\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.probs(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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm-up Test Case %d: %s 0 /home/ssd5/zhanghui/DeepSpeech2.x/examples/aishell/../..//examples/dataset/aishell/data_aishell/wav/test/S0764/BAC009S0764W0121.wav\n",
      "/home/ssd5/zhanghui/DeepSpeech2.x/examples/aishell/../..//examples/dataset/aishell/data_aishell/wav/test/S0764/BAC009S0764W0121.wav\n",
      "input: 0 audio\n",
      "input: 1 audio_len\n",
      "output: 0 tmp_75\n",
      "jit: [[[1.40282078e-04 3.31296207e-04 5.57157793e-04 ... 1.07916087e-04\n",
      "   8.73636964e-05 1.96113906e-04]\n",
      "  [1.38032061e-04 2.70526099e-04 4.53807996e-04 ... 1.02293277e-04\n",
      "   8.40202629e-05 1.90612729e-04]\n",
      "  [1.38912103e-04 2.45687814e-04 3.99624696e-04 ... 9.70420660e-05\n",
      "   7.88255784e-05 1.80084753e-04]\n",
      "  ...\n",
      "  [3.28999187e-04 2.59723864e-04 3.03535169e-04 ... 2.82066030e-04\n",
      "   1.11002744e-04 1.27009131e-04]\n",
      "  [2.91427423e-04 2.20203598e-04 2.85082555e-04 ... 3.27318383e-04\n",
      "   1.09202861e-04 1.17112293e-04]\n",
      "  [3.63971514e-04 1.47859042e-04 2.24457763e-04 ... 3.63016297e-04\n",
      "   1.34765272e-04 1.61947115e-04]]] <class 'numpy.ndarray'>\n",
      "[1, 161, 419]\n",
      "[1]\n",
      "paddle: [[[3.4913886e-04 2.5836096e-04 4.2449642e-04 ... 7.2210147e-05\n",
      "   7.1211573e-05 2.0057644e-04]\n",
      "  [3.8406707e-04 2.4088801e-04 5.0910388e-04 ... 6.1701416e-05\n",
      "   6.7852285e-05 2.3967208e-04]\n",
      "  [4.1069370e-04 2.5478008e-04 6.7985675e-04 ... 5.8369777e-05\n",
      "   6.2065104e-05 2.5938542e-04]\n",
      "  ...\n",
      "  [6.6656910e-04 3.1835871e-04 7.5929717e-04 ... 1.1990797e-04\n",
      "   3.7087579e-05 3.4520373e-04]\n",
      "  [4.7881933e-04 2.7979453e-04 6.7949941e-04 ... 1.2511105e-04\n",
      "   4.5631223e-05 3.7984925e-04]\n",
      "  [2.8661705e-04 2.9201157e-04 4.5970027e-04 ... 1.4581002e-04\n",
      "   7.8281126e-05 3.8263199e-04]]]\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 161, 419) (1,)\n",
      "input: 0 audio\n",
      "input: 1 audio_len\n",
      "output: 0 tmp_75\n",
      "jit: [[[1.40282078e-04 3.31296207e-04 5.57157793e-04 ... 1.07916087e-04\n",
      "   8.73636964e-05 1.96113906e-04]\n",
      "  [1.38032061e-04 2.70526099e-04 4.53807996e-04 ... 1.02293277e-04\n",
      "   8.40202629e-05 1.90612729e-04]\n",
      "  [1.38912103e-04 2.45687814e-04 3.99624696e-04 ... 9.70420660e-05\n",
      "   7.88255784e-05 1.80084753e-04]\n",
      "  ...\n",
      "  [3.28999187e-04 2.59723864e-04 3.03535169e-04 ... 2.82066030e-04\n",
      "   1.11002744e-04 1.27009131e-04]\n",
      "  [2.91427423e-04 2.20203598e-04 2.85082555e-04 ... 3.27318383e-04\n",
      "   1.09202861e-04 1.17112293e-04]\n",
      "  [3.63971514e-04 1.47859042e-04 2.24457763e-04 ... 3.63016297e-04\n",
      "   1.34765272e-04 1.61947115e-04]]]\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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ssd5/zhanghui/DeepSpeech2.x/tools/venv-dev/lib/python3.7/site-packages/paddlepaddle_gpu-0.0.0-py3.7-linux-x86_64.egg/paddle/tensor/creation.py:143: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if data.dtype == np.object:\n"
     ]
    }
   ],
   "source": [
    "a = paddle.to_tensor([1,3,4])\n",
    "a.numpy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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