{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "espnet_onnx.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "source": [ "# espnet_onnx demonstration\n", "\n", "This notebook provides a demonstration of how to export your trained model into onnx format.\n", "Currently only ASR is supported.\n", "\n", "see also:\n", "- ESPnet: https://github.com/espnet/espnet\n", "- espnet_onnx: https://github.com/Masao-Someki/espnet_onnx\n", "\n", "Author: [Masao Someki](https://github.com/Masao-Someki)\n", "\n", "\n", "## Table of Contents\n", "\n", "- Install Dependency\n", "- Export your model\n", "- Inference with onnx\n", "- Using streaming model" ], "metadata": { "id": "CBgUqWroOulc" } }, { "cell_type": "markdown", "source": [ "# Install Dependency" ], "metadata": { "id": "lWtGQwmqTzn0" } }, { "cell_type": "markdown", "source": [ "To run this demo, you need to install the following packages.\n", "- espnet_onnx\n", "- torch >= 1.11.0 (already installed in Colab)\n", "- espnet\n", "- espnet_model_zoo\n", "- onnx\n", "\n", "`torch`, `espnet`, `espnet_model_zoo`, `onnx` is required to run the exportation demo." ], "metadata": { "id": "D7CYtP6-T2TD" } }, { "cell_type": "code", "source": [ "!pip install -U espnet_onnx espnet espnet_model_zoo onnx\n", "\n", "# in this demo, we need to update scipy to avoid an error\n", "!pip install -U scipy" ], "metadata": { "id": "4FEU9BUoUAqi" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Export your model" ], "metadata": { "id": "XPDeBM1YPVm5" } }, { "cell_type": "markdown", "source": [ "\n", "## Export model from espnet_model_zoo\n", "\n", "The easiest way to export a model is to use `espnet_model_zoo`. You can download, unpack, and export the pretrained models with `export_from_pretrained` method.\n", "`espnet_onnx` will save the onnx models into cache directory, which is `${HOME}/.cache/espnet_onnx` in default." ], "metadata": { "id": "fLB4R8xCR2NB" } }, { "cell_type": "code", "source": [ "# export the model.\n", "from espnet_onnx.export import ModelExport\n", "\n", "tag_name = 'kamo-naoyuki/timit_asr_train_asr_raw_word_valid.acc.ave'\n", "\n", "m = ModelExport()\n", "m.export_from_pretrained(tag_name)" ], "metadata": { "id": "RZMMolDMSBO-" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Export from custom model\n", "\n", "`espnet_onnx` can also export your own trained model with `export` method.\n", "\n", "The following script shows how to export from `espnet2.bin.asr_inference.Speech2Text` instance.\n", "You can also export from a zipped file, by using the `export_from_zip` function. \n", "For this demonstration, I'm using the `from_pretrained` method to load parameters, but you can load your own model." ], "metadata": { "id": "VUicHf3nZQp-" } }, { "cell_type": "code", "source": [ "# prepare the espnet2.bin.asr_inference.Speech2Text instance.\n", "from espnet2.bin.asr_inference import Speech2Text\n", "\n", "tag_name = 'kamo-naoyuki/timit_asr_train_asr_raw_word_valid.acc.ave'\n", "speech2text = Speech2Text.from_pretrained(tag_name)\n", "\n", "\n", "# export model\n", "from espnet_onnx.export import ModelExport\n", "\n", "sample_model_tag = 'demo/sample_model_1'\n", "m = ModelExport()\n", "m.export(\n", " speech2text,\n", " sample_model_tag,\n", " quantize=False\n", ")" ], "metadata": { "id": "t4FsmkzzZtNk" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Inference with onnx" ], "metadata": { "id": "lA8LIpG4WYVk" } }, { "cell_type": "markdown", "source": [ "Now, let's use the exported models for inference." ], "metadata": { "id": "wQFWtLVbWc1A" } }, { "cell_type": "code", "source": [ "# please provide the tag_name to specify exported model.\n", "tag_name = 'kamo-naoyuki/timit_asr_train_asr_raw_word_valid.acc.ave'\n", "\n", "\n", "# upload wav file and let's inference!\n", "import librosa\n", "from google.colab import files\n", "\n", "wav_file = files.upload()\n", "y, sr = librosa.load(list(wav_file.keys())[0], sr=16000)\n", "\n", "\n", "# Use the exported onnx file to inference.\n", "from espnet_onnx import Speech2Text\n", "\n", "speech2text = Speech2Text(tag_name)\n", "nbest = speech2text(y)\n", "print(nbest[0][0])" ], "metadata": { "id": "mEXNvl5RZDDs", "colab": { "resources": { "http://localhost:8080/nbextensions/google.colab/files.js": { "data": 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"ok": true, "headers": [ [ "content-type", "application/javascript" ] ], "status": 200, "status_text": "OK" } }, "base_uri": "https://localhost:8080/", "height": 126 }, "outputId": "e2166417-b149-40e0-a7ec-77fe8ee553f2" }, "execution_count": 1, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving LJ050-0030.wav to LJ050-0030 (1).wav\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/espnet_onnx/asr/scorer/interface.py:96: UserWarning: RNNDecoder batch score is implemented through for loop not parallelized\n", " self.__class__.__name__\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "ih n uw n ih sh ih z ah v aa l z ow r eh sil t er m ey z z\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Using streaming model" ], "metadata": { "id": "cbpsXSRhffmz" } }, { "cell_type": "markdown", "source": [ "Model exportation is exactly the same as non-streaming model. You can follow the `#Export your model` chapter.\n", "\n", "As for streaming, you can specify the following configuration additionaly. Usually, these values should be the same as the training configuration.\n", "- block_size\n", "- hop_size\n", "- look_ahead\n", "\n", "The length of the speech should be the same as `streaming_model.hop_size`.\n", "This value is calculated as follows\n", "\n", "$$\n", "\\begin{align}\n", "h &= \\text{hop_size} * \\text{encoder.subsample} * \\text{stft.hop_length}\\\\\n", "\\text{padding} &= (\\text{stft.n_fft} // \\text{stft.hop_length}) * \\text{stft.hop_length} \\\\\n", "\\text{len(wav)} &= h + \\text{padding}\n", "\\end{align}\n", "$$\n", "\n", "For example, the length of the speech is 8704 with the following configuration.\n", "- block_size = 40\n", "- hop_size = 16\n", "- look_ahead = 16\n", "- encoder.subsample = 4\n", "- stft.n_fft = 512\n", "- stft.hop_length = 128\n", "\n", "Now, let's demonstrate the streaming inference." ], "metadata": { "id": "PE-pniN0fwf7" } }, { "cell_type": "code", "source": [ "# Export the streaming model.\n", "# Note that the following model is very large\n", "from espnet_onnx.export import ModelExport\n", "\n", "tag_name = 'D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave'\n", "\n", "m = ModelExport()\n", "m.export_from_pretrained(tag_name)" ], "metadata": { "id": "fYqZB-DifYdj" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# In this tutorial, we will use the recorded wav file to simulate streaming.\n", "import librosa\n", "from espnet_onnx import StreamingSpeech2Text\n", "\n", "tag_name = 'D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave'\n", "streaming_model = StreamingSpeech2Text(tag_name)\n", "\n", "# upload wav file\n", "from google.colab import files\n", "wav_file = files.upload()\n", "y, sr = librosa.load(list(wav_file.keys())[0], sr=16000)\n", "\n", "num_process = len(y) // streaming_model.hop_size + 1\n", "print(f\"I will split your audio file into {num_process} blocks.\")\n", "\n", "# simulate streaming.\n", "streaming_model.start()\n", "for i in range(num_process):\n", " # prepare wav file\n", " start = i * streaming_model.hop_size\n", " end = (i + 1) * streaming_model.hop_size\n", " wav_streaming = y[start : end]\n", "\n", " # apply padding if len(wav_streaming) < streaming_model.hop_size\n", " wav_streaming = streaming_model.pad(wav_streaming)\n", " \n", " # compute asr\n", " nbest = streaming_model(wav_streaming)\n", " print(f'Result at position {i} : {nbest[0][0]}')\n", "\n", "final_nbest = streaming_model.end()\n", "print(f'Final result : {final_nbest[0][0]}')" ], "metadata": { "id": "-guloDfSkYb-", "colab": { "resources": { "http://localhost:8080/nbextensions/google.colab/files.js": { "data": 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"ok": true, "headers": [ [ "content-type", "application/javascript" ] ], "status": 200, "status_text": "OK" } }, "base_uri": "https://localhost:8080/", "height": 178 }, "outputId": "ef485301-8710-45b5-d703-0a2a9bf163c9" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving LJ050-0030.wav to LJ050-0030 (2).wav\n", "I will split your audio file into 4 blocks.\n", "Result at position 0 : \n", "Result at position 1 : the commis\n", "Result at position 2 : and the commiss\n", "Result at position 3 : the commission also recommen\n", "Final result : the commission also recommends\n" ] } ] } ] }