{
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"source": [
"# Speech to Text with OpenVINO™\n",
"\n",
"This tutorial demonstrates speech-to-text recognition with OpenVINO.\n",
"\n",
"This tutorial uses the [QuartzNet 15x5](https://docs.openvino.ai/2024/omz_models_model_quartznet_15x5_en.html) model. QuartzNet performs automatic speech recognition. Its design is based on the Jasper architecture, which is a convolutional model trained with Connectionist Temporal Classification (CTC) loss. The model is available from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/).\n",
"\n",
"\n",
"#### Table of contents:\n",
"\n",
"- [Imports](#Imports)\n",
"- [Settings](#Settings)\n",
"- [Download and Convert Public Model](#Download-and-Convert-Public-Model)\n",
" - [Download Model](#Download-Model)\n",
"- [Audio Processing](#Audio-Processing)\n",
" - [Define constants](#Define-constants)\n",
" - [Available Audio Formats](#Available-Audio-Formats)\n",
" - [Load Audio File](#Load-Audio-File)\n",
" - [Visualize Audio File](#Visualize-Audio-File)\n",
" - [Change Type of Data](#Change-Type-of-Data)\n",
" - [Convert Audio to Mel Spectrum](#Convert-Audio-to-Mel-Spectrum)\n",
" - [Run Conversion from Audio to Mel Format](#Run-Conversion-from-Audio-to-Mel-Format)\n",
" - [Visualize Mel Spectrogram](#Visualize-Mel-Spectrogram)\n",
" - [Adjust Mel scale to Input](#Adjust-Mel-scale-to-Input)\n",
"- [Load the Model](#Load-the-Model)\n",
" - [Do Inference](#Do-Inference)\n",
" - [Read Output](#Read-Output)\n",
" - [Implementation of Decoding](#Implementation-of-Decoding)\n",
" - [Run Decoding and Print Output](#Run-Decoding-and-Print-Output)\n",
"\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4aa5b3ca",
"metadata": {},
"source": [
"## Imports\n",
"[back to top ⬆️](#Table-of-contents:)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "84332af9",
"metadata": {},
"outputs": [],
"source": [
"%pip install -q \"librosa>=0.8.1\" \"scipy\" \"matplotlib<3.8\" \"openvino-dev>=2024.0.0\" \"numpy<1.24\" \"ruamel.yaml\" \"torch\" \"tqdm\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0d5b38d8",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"tags": []
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import sys\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import IPython.display as ipd\n",
"import matplotlib.pyplot as plt\n",
"import librosa\n",
"import librosa.display\n",
"import numpy as np\n",
"import scipy\n",
"import openvino as ov\n",
"\n",
"# Fetch `notebook_utils` module\n",
"import requests\n",
"\n",
"r = requests.get(\n",
" url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py\",\n",
")\n",
"\n",
"open(\"notebook_utils.py\", \"w\").write(r.text)\n",
"from notebook_utils import download_file"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "dfed8a85",
"metadata": {},
"source": [
"## Settings\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"In this part, all variables used in the notebook are set."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1e9a7f30",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model_folder = \"model\"\n",
"download_folder = \"output\"\n",
"data_folder = \"data\"\n",
"\n",
"precision = \"FP16\"\n",
"model_name = \"quartznet-15x5-en\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cfb3b8ca",
"metadata": {},
"source": [
"## Download and Convert Public Model\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"If it is your first run, models will be downloaded and converted here. It my take a few minutes. Use `omz_downloader` and `omz_converter`, which are command-line tools from the `openvino-dev` package. \n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4d946d37",
"metadata": {},
"source": [
"### Download Model\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"The `omz_downloader` tool automatically creates a directory structure and downloads the selected model. This step is skipped if the model is already downloaded. The selected model comes from the public directory, which means it must be converted into OpenVINO Intermediate Representation (OpenVINO IR)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4938f7e8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Check if a model is already downloaded (to the download directory).\n",
"path_to_model_weights = Path(f\"{download_folder}/public/{model_name}/models\")\n",
"downloaded_model_file = list(path_to_model_weights.glob(\"*.pth\"))\n",
"\n",
"if not path_to_model_weights.is_dir() or len(downloaded_model_file) == 0:\n",
" download_command = f\"omz_downloader --name {model_name} --output_dir {download_folder} --precision {precision}\"\n",
" ! $download_command\n",
"\n",
"sys.path.insert(0, str(path_to_model_weights))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7194170b-1ba6-460e-a514-24d115b47302",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def convert_model(model_path: Path, converted_model_path: Path):\n",
" \"\"\"\n",
" helper function for converting QuartzNet model to IR\n",
" The function accepts path to directory with dowloaded packages, weights and configs using OMZ downloader,\n",
" initialize model and convert to OpenVINO model and serialize it to IR.\n",
" Params:\n",
" model_path: path to model modules, weights and configs downloaded via omz_downloader\n",
" converted_model_path: path for saving converted model\n",
" Returns:\n",
" None\n",
" \"\"\"\n",
" # add model path to PYTHONPATH for access to downloaded modules\n",
" sys.path.append(str(model_path))\n",
"\n",
" # import necessary classes\n",
" from ruamel.yaml import YAML\n",
"\n",
" from nemo.collections.asr import JasperEncoder, JasperDecoderForCTC\n",
" from nemo.core import NeuralModuleFactory, DeviceType\n",
"\n",
" YAML = YAML(typ=\"safe\")\n",
"\n",
" # utility fornction fr replacing 1d convolutions to 2d for better efficiency\n",
" def convert_to_2d(model):\n",
" for name, l in model.named_children():\n",
" layer_type = l.__class__.__name__\n",
" if layer_type == \"Conv1d\":\n",
" new_layer = nn.Conv2d(\n",
" l.in_channels,\n",
" l.out_channels,\n",
" (1, l.kernel_size[0]),\n",
" (1, l.stride[0]),\n",
" (0, l.padding[0]),\n",
" (1, l.dilation[0]),\n",
" l.groups,\n",
" False if l.bias is None else True,\n",
" l.padding_mode,\n",
" )\n",
" params = l.state_dict()\n",
" params[\"weight\"] = params[\"weight\"].unsqueeze(2)\n",
" new_layer.load_state_dict(params)\n",
" setattr(model, name, new_layer)\n",
" elif layer_type == \"BatchNorm1d\":\n",
" new_layer = nn.BatchNorm2d(l.num_features, l.eps)\n",
" new_layer.load_state_dict(l.state_dict())\n",
" new_layer.eval()\n",
" setattr(model, name, new_layer)\n",
" else:\n",
" convert_to_2d(l)\n",
"\n",
" # model class\n",
" class QuartzNet(torch.nn.Module):\n",
" def __init__(self, model_config, encoder_weights, decoder_weights):\n",
" super().__init__()\n",
" with open(model_config, \"r\") as config:\n",
" model_args = YAML.load(config)\n",
" _ = NeuralModuleFactory(placement=DeviceType.CPU)\n",
"\n",
" encoder_params = model_args[\"init_params\"][\"encoder_params\"][\"init_params\"]\n",
" self.encoder = JasperEncoder(**encoder_params)\n",
" self.encoder.load_state_dict(torch.load(encoder_weights, map_location=\"cpu\"))\n",
"\n",
" decoder_params = model_args[\"init_params\"][\"decoder_params\"][\"init_params\"]\n",
" self.decoder = JasperDecoderForCTC(**decoder_params)\n",
" self.decoder.load_state_dict(torch.load(decoder_weights, map_location=\"cpu\"))\n",
"\n",
" self.encoder._prepare_for_deployment()\n",
" self.decoder._prepare_for_deployment()\n",
" convert_to_2d(self.encoder)\n",
" convert_to_2d(self.decoder)\n",
"\n",
" def forward(self, input_signal):\n",
" input_signal = input_signal.unsqueeze(axis=2)\n",
" i_encoded = self.encoder(input_signal)\n",
" i_log_probs = self.decoder(i_encoded)\n",
"\n",
" shape = i_log_probs.shape\n",
" return i_log_probs.reshape(shape[0], shape[1], shape[3])\n",
"\n",
" # path to configs and weights for creating model instane\n",
" model_config = model_path / \".nemo_tmp/module.yaml\"\n",
" encoder_weights = model_path / \".nemo_tmp/JasperEncoder.pt\"\n",
" decoder_weights = model_path / \".nemo_tmp/JasperDecoderForCTC.pt\"\n",
" # create model instance\n",
" model = QuartzNet(model_config, encoder_weights, decoder_weights)\n",
" # turn model to inference mode\n",
" model.eval()\n",
" # convert model to OpenVINO Model using model conversion API\n",
" ov_model = ov.convert_model(model, example_input=torch.zeros([1, 64, 128]))\n",
" # save model in IR format for next usage\n",
" ov.save_model(ov_model, converted_model_path)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8cfaf7ee",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[NeMo W 2023-09-11 15:01:17 jasper:148] Turned off 170 masked convolutions\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[NeMo W 2023-09-11 15:01:18 deprecated:66] Function ``local_parameters`` is deprecated. It is going to be removed in the 0.11 version.\n"
]
}
],
"source": [
"# Check if a model is already converted (in the model directory).\n",
"path_to_converted_weights = Path(f\"{model_folder}/public/{model_name}/{precision}/{model_name}.bin\")\n",
"path_to_converted_model = Path(f\"{model_folder}/public/{model_name}/{precision}/{model_name}.xml\")\n",
"\n",
"if not path_to_converted_weights.is_file():\n",
" downloaded_model_path = Path(\"output/public/quartznet-15x5-en/models\")\n",
" convert_model(downloaded_model_path, path_to_converted_model)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "778958d4",
"metadata": {},
"source": [
"## Audio Processing\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Now that the model is converted, load an audio file. "
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5c4ad393",
"metadata": {},
"source": [
"### Define constants\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"First, locate an audio file and define the alphabet used by the model. This tutorial uses the Latin alphabet beginning with a space symbol and ending with a blank symbol. In this case it will be `~`, but that could be any other character."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4657cc4e",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"tags": []
},
"outputs": [],
"source": [
"audio_file_name = \"edge_to_cloud.ogg\"\n",
"alphabet = \" abcdefghijklmnopqrstuvwxyz'~\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "47592c21",
"metadata": {},
"source": [
"### Available Audio Formats\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"There are multiple supported audio formats that can be used with the model: \n",
"\n",
"`AIFF`, `AU`, `AVR`, `CAF`, `FLAC`, `HTK`, `SVX`, `MAT4`, `MAT5`, `MPC2K`, `OGG`, `PAF`, `PVF`, `RAW`, `RF64`, `SD2`, `SDS`, `IRCAM`, `VOC`, `W64`, `WAV`, `NIST`, `WAVEX`, `WVE`, `XI`"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5aa408c2",
"metadata": {},
"source": [
"### Load Audio File\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Load the file after checking a file extension. Pass `sr` (stands for a `sampling rate`) as an additional parameter. The model supports files with a `sampling rate` of 16 kHz."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "dbb5f77c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Download the audio from the openvino_notebooks storage\n",
"file_name = download_file(\n",
" \"https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/audio/\" + audio_file_name,\n",
" directory=data_folder,\n",
")\n",
"\n",
"audio, sampling_rate = librosa.load(path=str(file_name), sr=16000)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5271feee",
"metadata": {},
"source": [
"Now, you can play your audio file."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3a8db1c8",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
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""
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ipd.Audio(audio, rate=sampling_rate)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e4fd06e0",
"metadata": {},
"source": [
"### Visualize Audio File\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"You can visualize how your audio file presents on a wave plot and spectrogram."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ecbd9d4b",
"metadata": {
"tags": []
},
"outputs": [
{
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1025, 51)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"librosa.display.waveshow(y=audio, sr=sampling_rate, max_points=50000, x_axis=\"time\", offset=0.0)\n",
"plt.show()\n",
"specto_audio = librosa.stft(audio)\n",
"specto_audio = librosa.amplitude_to_db(np.abs(specto_audio), ref=np.max)\n",
"print(specto_audio.shape)\n",
"librosa.display.specshow(specto_audio, sr=sampling_rate, x_axis=\"time\", y_axis=\"hz\");"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0564a2d8",
"metadata": {},
"source": [
"### Change Type of Data\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"The file loaded in the previous step may contain data in `float` type with a range of values between -1 and 1. To generate a viable input, multiply each value by the max value of `int16` and convert it to `int16` type. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2eb5cbe0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"if max(np.abs(audio)) <= 1:\n",
" audio = audio * (2**15 - 1)\n",
"audio = audio.astype(np.int16)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "666a47cd",
"metadata": {},
"source": [
"### Convert Audio to Mel Spectrum\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Next, convert the pre-pre-processed audio to [Mel Spectrum](https://medium.com/analytics-vidhya/understanding-the-mel-spectrogram-fca2afa2ce53). For more information on why it needs to be done, refer to [this article](https://towardsdatascience.com/audio-deep-learning-made-simple-part-2-why-mel-spectrograms-perform-better-aad889a93505)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a7ecd4ea",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def audio_to_mel(audio, sampling_rate):\n",
" assert sampling_rate == 16000, \"Only 16 KHz audio supported\"\n",
" preemph = 0.97\n",
" preemphased = np.concatenate([audio[:1], audio[1:] - preemph * audio[:-1].astype(np.float32)])\n",
"\n",
" # Calculate the window length.\n",
" win_length = round(sampling_rate * 0.02)\n",
"\n",
" # Based on the previously calculated window length, run short-time Fourier transform.\n",
" spec = np.abs(\n",
" librosa.core.spectrum.stft(\n",
" preemphased,\n",
" n_fft=512,\n",
" hop_length=round(sampling_rate * 0.01),\n",
" win_length=win_length,\n",
" center=True,\n",
" window=scipy.signal.windows.hann(win_length),\n",
" pad_mode=\"reflect\",\n",
" )\n",
" )\n",
"\n",
" # Create mel filter-bank, produce transformation matrix to project current values onto Mel-frequency bins.\n",
" mel_basis = librosa.filters.mel(sr=sampling_rate, n_fft=512, n_mels=64, fmin=0.0, fmax=8000.0, htk=False)\n",
" return mel_basis, spec\n",
"\n",
"\n",
"def mel_to_input(mel_basis, spec, padding=16):\n",
" # Convert to a logarithmic scale.\n",
" log_melspectrum = np.log(np.dot(mel_basis, np.power(spec, 2)) + 2**-24)\n",
"\n",
" # Normalize the output.\n",
" normalized = (log_melspectrum - log_melspectrum.mean(1)[:, None]) / (log_melspectrum.std(1)[:, None] + 1e-5)\n",
"\n",
" # Calculate padding.\n",
" remainder = normalized.shape[1] % padding\n",
" if remainder != 0:\n",
" return np.pad(normalized, ((0, 0), (0, padding - remainder)))[None]\n",
" return normalized[None]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "7d86046e",
"metadata": {},
"source": [
"### Run Conversion from Audio to Mel Format\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"In this step, convert a current audio file into [Mel scale](https://en.wikipedia.org/wiki/Mel_scale)."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a767331a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"mel_basis, spec = audio_to_mel(audio=audio.flatten(), sampling_rate=sampling_rate)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4e3276fa",
"metadata": {},
"source": [
"### Visualize Mel Spectrogram\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"For more information about Mel spectrogram, refer to this [article](https://towardsdatascience.com/getting-to-know-the-mel-spectrogram-31bca3e2d9d0). The first image visualizes Mel frequency spectrogram, the second one presents filter bank for converting Hz to Mels."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "bef57088",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"librosa.display.specshow(data=spec, sr=sampling_rate, x_axis=\"time\", y_axis=\"log\")\n",
"plt.show()\n",
"librosa.display.specshow(data=mel_basis, sr=sampling_rate, x_axis=\"linear\")\n",
"plt.ylabel(\"Mel filter\");"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "503f1778",
"metadata": {},
"source": [
"### Adjust Mel scale to Input\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Before reading the network, make sure that the input is ready."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8a63159b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"audio = mel_to_input(mel_basis=mel_basis, spec=spec)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e4aec612",
"metadata": {},
"source": [
"## Load the Model\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Now, you can read and load the network. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "7d5dc5c8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"core = ov.Core()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ce3fc33e",
"metadata": {},
"source": [
"You may run the model on multiple devices. By default, it will load the model on CPU (you can choose manually CPU, GPU etc.) or let the engine choose the best available device (AUTO).\n",
"\n",
"To list all available devices that can be used, run `print(core.available_devices)` command."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "6a69236c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['CPU', 'GPU']\n"
]
}
],
"source": [
"print(core.available_devices)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a57d2f27",
"metadata": {},
"source": [
"Select device from dropdown list"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "dc438409-2b7f-41bf-ae21-346d5f34f543",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a7396ec808d148a5b5c6a7a8c194e192",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import ipywidgets as widgets\n",
"\n",
"device = widgets.Dropdown(\n",
" options=core.available_devices + [\"AUTO\"],\n",
" value=\"AUTO\",\n",
" description=\"Device:\",\n",
" disabled=False,\n",
")\n",
"\n",
"device"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "3319e2d2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model = core.read_model(model=f\"{model_folder}/public/{model_name}/{precision}/{model_name}.xml\")\n",
"model_input_layer = model.input(0)\n",
"shape = model_input_layer.partial_shape\n",
"shape[2] = -1\n",
"model.reshape({model_input_layer: shape})\n",
"compiled_model = core.compile_model(model=model, device_name=device.value)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a566de49",
"metadata": {},
"source": [
"### Do Inference\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Everything is set up. Now, the only thing that remains is passing input to the previously loaded network and running inference."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "44d64136",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"character_probabilities = compiled_model([ov.Tensor(audio)])[0]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cc761843",
"metadata": {},
"source": [
"### Read Output\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"After inference, you need to reach out the output. The default output format for `QuartzNet 15x5` are per-frame probabilities (after LogSoftmax) for every symbol in the alphabet, name - output, shape - 1x64x29, output data format is `BxNxC`, where:\n",
"\n",
"* B - batch size\n",
"* N - number of audio frames\n",
"* C - alphabet size, including the Connectionist Temporal Classification (CTC) blank symbol\n",
"\n",
"You need to make it in a more human-readable format. To do this you, use a symbol with the highest probability. When you hold a list of indexes that are predicted to have the highest probability, due to limitations given by [Connectionist Temporal Classification Decoding](https://towardsdatascience.com/beam-search-decoding-in-ctc-trained-neural-networks-5a889a3d85a7) you will remove concurrent symbols and then remove all the blanks.\n",
"\n",
"The last step is getting symbols from corresponding indexes in charlist."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "ce9e3604",
"metadata": {},
"outputs": [],
"source": [
"# Remove unnececery dimension\n",
"character_probabilities = np.squeeze(character_probabilities)\n",
"\n",
"# Run argmax to pick most possible symbols\n",
"character_probabilities = np.argmax(character_probabilities, axis=1)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "477d8b66",
"metadata": {},
"source": [
"### Implementation of Decoding\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"To decode previously explained output, you need the [Connectionist Temporal Classification (CTC) decode](https://towardsdatascience.com/beam-search-decoding-in-ctc-trained-neural-networks-5a889a3d85a7) function. This solution will remove consecutive letters from the output."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "6cfd8a5d",
"metadata": {},
"outputs": [],
"source": [
"def ctc_greedy_decode(predictions):\n",
" previous_letter_id = blank_id = len(alphabet) - 1\n",
" transcription = list()\n",
" for letter_index in predictions:\n",
" if previous_letter_id != letter_index != blank_id:\n",
" transcription.append(alphabet[letter_index])\n",
" previous_letter_id = letter_index\n",
" return \"\".join(transcription)"
]
},
{
"attachments": {},
"cell_type": "markdown",
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"source": [
"### Run Decoding and Print Output\n",
"[back to top ⬆️](#Table-of-contents:)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "19626945",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"from the edge to the cloud\n"
]
}
],
"source": [
"transcription = ctc_greedy_decode(character_probabilities)\n",
"print(transcription)"
]
}
],
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"Audio-to-Text",
"Speech Recognition"
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